moving

benchmarks/archive/README.md

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+# benchmarks/archive/
+
+Long-term archival folder for launch-claim-supporting benchmark runs.
+
+## Retention contract (per H-AUDIT-1 ratification §Q5)
+
+- **Tier 2 only.** This folder holds gzipped JSONL from runs that support a
+  published LoCoMo or SWE-ContextBench claim (e.g. H-42a/b Stage 2 full-run
+  when it lands). Sprint-internal probes (Stage 1 mikro-eval, Stage 2 4-cell
+  mini, reruns) stay in `benchmarks/results/` and are pruned at sprint close.
+- **12 months minimum** from commit date. Longer at PM discretion for
+  EU AI Act / legal / compliance alignment.
+- **Gzipped JSONL only.** Expected ≤300MB per full run (~20-30% of raw).
+- **Includes `reasoning_content`** when Stage 2 thinking=on runs land here —
+  external reviewers asking "why did the model answer this way on item N"
+  need the provider's reasoning chain. Exclusion rules (§2.4 of design doc)
+  apply to the read path, not to the archive write path.
+
+## Naming
+
+```
+h-42a-stage-2-full-YYYY-MM-DD.jsonl.gz   # Stage 2 main-run artifact
+h-42b-stage-2-full-YYYY-MM-DD.jsonl.gz   # Comparative run (if cut)
+```
+
+## NOT gitignored
+
+Both the folder and its contents ARE committed — see `.gitignore` exemption
+rule (Sprint 11 A2).
+
+## Sprint 11 state
+
+Empty. Sprint 11 only provisions the folder. Real archival files land in the
+sprint that executes H-42a/b.

benchmarks/calibration/v6-kappa-recal/_summary-v6-kappa.json

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+{
+  "verdict": "PASS",
+  "k_opus_gpt": 0.847958297132928,
+  "k_opus_minimax": 0.8548922056384745,
+  "k_gpt_minimax": 0.7877758913412564,
+  "k_conservative_trio": 0.7877758913412564,
+  "minimax_parse_success": 100,
+  "minimax_n_total": 100,
+  "minimax_lat_p50_ms": 11927,
+  "minimax_lat_p95_ms": 31426,
+  "minimax_routing_errors": 0,
+  "minimax_retries_total": 0,
+  "minimax_prompt_tokens_total": 53855,
+  "minimax_completion_tokens_total": 48920,
+  "cost_actual_usd": 0.0749,
+  "per_cell": [
+    {
+      "cell": "no-context",
+      "n": 20,
+      "mm_parsed": 20,
+      "k_og": 1.0,
+      "k_om": 1.0,
+      "k_gm": 1.0
+    },
+    {
+      "cell": "oracle-context",
+      "n": 20,
+      "mm_parsed": 20,
+      "k_og": 0.7058823529411763,
+      "k_om": 0.7916666666666667,
+      "k_gm": 0.7058823529411763
+    },
+    {
+      "cell": "full-context",
+      "n": 20,
+      "mm_parsed": 20,
+      "k_og": 0.8,
+      "k_om": 0.7,
+      "k_gm": 0.7058823529411763
+    },
+    {
+      "cell": "retrieval",
+      "n": 20,
+      "mm_parsed": 20,
+      "k_og": 1.0,
+      "k_om": 0.8936170212765956,
+      "k_gm": 0.8936170212765956
+    },
+    {
+      "cell": "agentic",
+      "n": 20,
+      "mm_parsed": 20,
+      "k_og": 0.782608695652174,
+      "k_om": 0.8979591836734693,
+      "k_gm": 0.6874999999999999
+    }
+  ],
+  "confusion_opus_gpt": {
+    "correct_correct": 32,
+    "correct_incorrect": 7,
+    "incorrect_correct": 0,
+    "incorrect_incorrect": 61
+  },
+  "confusion_opus_minimax": {
+    "correct_correct": 37,
+    "correct_incorrect": 2,
+    "incorrect_correct": 5,
+    "incorrect_incorrect": 56
+  },
+  "confusion_gpt_minimax": {
+    "correct_correct": 32,
+    "correct_incorrect": 0,
+    "incorrect_correct": 10,
+    "incorrect_incorrect": 58
+  }
+}

benchmarks/calibration/v6-kappa-recal/cold-probes-phase2.py

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+"""
+Manifest v6 Phase 2 pre-flight — cold alias probes
+====================================================
+
+Validates both v6 judge aliases are production-ready with a minimal
+probe sample before any N=400 commit. 6 calls total (3 MiniMax + 3
+Kimi) on the first 3 split instances from §1.3h sample.
+
+Probes use direct HTTP to upstream endpoints (same methodology as
+§1.3g / §1.3h / §1.3h-C / v6 κ re-cal). LiteLLM proxy is NOT in the
+loop — isolates upstream routing/parse behavior from middleware.
+
+Kimi backup has never been exercised in production under v6 authority;
+this is the first production-class test.
+
+Scope: §11-compliant (read-only access to frozen files; new artefact
+under benchmarks/calibration/v6-kappa-recal/).
+"""
+from __future__ import annotations
+
+import json
+import re
+import sys
+import time
+import urllib.error
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+
+try:
+    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+    sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+except Exception:
+    pass
+
+OUT_PATH = Path("D:/Projects/waggle-os/benchmarks/calibration/v6-kappa-recal/phase2-cold-probes.jsonl")
+# Reuse first 3 split instances from §1.3h — they exercised both providers before
+SAMPLE_PATH = Path("D:/Projects/waggle-os/benchmarks/probes/judge-swap-validation/split-cases-sample.jsonl")
+
+JUDGE_PROMPT_TEMPLATE = "\n".join([
+    "You are evaluating whether an LLM's answer is correct against ground truth.",
+    "",
+    "## Question",
+    "{question}",
+    "",
+    "## Ground-truth answer",
+    "{ground_truth}",
+    "",
+    "## Ground-truth supporting context (excerpt shown to the model)",
+    "{context}",
+    "",
+    "## Model's answer",
+    "{model_answer}",
+    "",
+    "## Your task",
+    "",
+    "Step 1: Determine if the model's answer is correct.",
+    "- \"correct\" means the model's answer contains all required facts from ground truth, with no additional incorrect claims.",
+    "- Minor phrasing differences, synonyms, or alternative but equivalent formulations are acceptable.",
+    "- Extra detail is acceptable ONLY if it is factually correct.",
+    "",
+    "Step 2: If incorrect, assign exactly one failure mode using this decision tree:",
+    "",
+    "1. Does the model explicitly refuse or say it does not know? -> F1 (ABSTAIN)",
+    "2. Does the model answer a DIFFERENT question than was asked (coherent but off-topic)? -> F5 (OFF-TOPIC)",
+    "3. Does the model rely on entities, names, dates, or claims that do NOT appear in the ground-truth context (fabrication)? -> F4 (HALLUCINATED)",
+    "4. Does the model correctly state SOME required facts but miss others, without stating any incorrect facts? -> F2 (PARTIAL)",
+    "5. Otherwise (model states facts derived from the context but gets them wrong): -> F3 (INCORRECT)",
+    "",
+    "Step 3: Return JSON only, no prose, in this exact schema:",
+    "",
+    "{{",
+    "  \"verdict\": \"correct\" | \"incorrect\",",
+    "  \"failure_mode\": null | \"F1\" | \"F2\" | \"F3\" | \"F4\" | \"F5\",",
+    "  \"rationale\": \"one sentence explaining the verdict\"",
+    "}}",
+    "",
+    "If verdict is \"correct\", failure_mode MUST be null.",
+    "If verdict is \"incorrect\", failure_mode MUST be one of F1-F5.",
+])
+
+
+def ts() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def logmsg(msg: str) -> None:
+    print(f"{ts()} {msg}", flush=True)
+
+
+def load_env() -> dict[str, str]:
+    env_path = Path("D:/Projects/waggle-os/.env")
+    out: dict[str, str] = {}
+    for line in env_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        k, _, v = line.partition("=")
+        out[k.strip()] = v.strip().strip('"').strip("'")
+    return out
+
+
+def extract_json_body(raw: str) -> dict | None:
+    if not raw:
+        return None
+    trimmed = raw.strip()
+    if trimmed.startswith("```"):
+        m = re.match(r"^```(?:json)?\s*\n?(.*?)```\s*$", trimmed, re.DOTALL)
+        if m:
+            trimmed = m.group(1).strip()
+    try:
+        return json.loads(trimmed)
+    except Exception:
+        pass
+    first = trimmed.find("{")
+    last = trimmed.rfind("}")
+    if first != -1 and last != -1 and last > first:
+        try:
+            return json.loads(trimmed[first:last + 1])
+        except Exception:
+            return None
+    return None
+
+
+def parse_verdict(raw: str) -> tuple[str | None, str | None, str | None]:
+    body = extract_json_body(raw)
+    if not isinstance(body, dict):
+        return (None, None, None)
+    v = body.get("verdict")
+    fm = body.get("failure_mode")
+    rat = body.get("rationale")
+    if v not in ("correct", "incorrect"):
+        return (None, None, None)
+    if fm is not None and fm not in ("F1", "F2", "F3", "F4", "F5"):
+        fm = None
+    return (v, fm, rat if isinstance(rat, str) else None)
+
+
+def http_post_json(url: str, headers: dict, body: dict, timeout_s: int = 60) -> tuple[int, dict | str]:
+    req = urllib.request.Request(
+        url, data=json.dumps(body).encode("utf-8"), method="POST",
+        headers={"Content-Type": "application/json", **headers},
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=timeout_s) as resp:
+            raw = resp.read().decode("utf-8", errors="replace")
+            try:
+                return resp.status, json.loads(raw)
+            except Exception:
+                return resp.status, raw
+    except urllib.error.HTTPError as e:
+        try:
+            return e.code, e.read().decode("utf-8", errors="replace")[:2000]
+        except Exception:
+            return e.code, ""
+    except Exception as e:
+        return 0, f"{type(e).__name__}: {e}"
+
+
+def call_minimax(prompt: str, or_key: str) -> dict:
+    url = "https://openrouter.ai/api/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {or_key}"}
+    body = {
+        "model": "minimax/minimax-m2.7",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.0,
+        "max_tokens": 4096,
+    }
+    started = time.time()
+    status, resp = http_post_json(url, headers, body)
+    latency = int((time.time() - started) * 1000)
+    if status == 200 and isinstance(resp, dict):
+        choices = resp.get("choices") or []
+        if choices:
+            msg = choices[0].get("message") or {}
+            content = msg.get("content") or msg.get("reasoning_content") or ""
+            usage = resp.get("usage", {})
+            return {
+                "raw_text": content,
+                "status": 200,
+                "latency_ms": latency,
+                "prompt_tokens": usage.get("prompt_tokens"),
+                "completion_tokens": usage.get("completion_tokens"),
+                "provider": "minimax",
+                "alias": "minimax-m27-via-openrouter",
+                "routing": "openrouter_direct_http",
+                "error": None,
+            }
+    return {
+        "raw_text": "",
+        "status": status,
+        "latency_ms": latency,
+        "error": str(resp)[:300],
+        "provider": "minimax",
+        "alias": "minimax-m27-via-openrouter",
+        "routing": "openrouter_direct_http",
+        "prompt_tokens": None,
+        "completion_tokens": None,
+    }
+
+
+def call_kimi(prompt: str, moonshot_key: str) -> dict:
+    """Kimi K2.6 via Moonshot direct intl endpoint. First production-class
+    run of the v6 kimi-k26-direct alias equivalent (LiteLLM proxy not in
+    loop; routes directly to upstream)."""
+    url = "https://api.moonshot.ai/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {moonshot_key}"}
+    body = {
+        "model": "kimi-k2.6",
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 4096,
+    }
+    started = time.time()
+    status, resp = http_post_json(url, headers, body)
+    latency = int((time.time() - started) * 1000)
+    if status == 200 and isinstance(resp, dict):
+        choices = resp.get("choices") or []
+        if choices:
+            msg = choices[0].get("message") or {}
+            content = msg.get("content") or msg.get("reasoning_content") or ""
+            usage = resp.get("usage", {})
+            return {
+                "raw_text": content,
+                "status": 200,
+                "latency_ms": latency,
+                "prompt_tokens": usage.get("prompt_tokens"),
+                "completion_tokens": usage.get("completion_tokens"),
+                "provider": "kimi",
+                "alias": "kimi-k26-direct",
+                "routing": "moonshot_direct_http",
+                "error": None,
+            }
+    return {
+        "raw_text": "",
+        "status": status,
+        "latency_ms": latency,
+        "error": str(resp)[:300],
+        "provider": "kimi",
+        "alias": "kimi-k26-direct",
+        "routing": "moonshot_direct_http",
+        "prompt_tokens": None,
+        "completion_tokens": None,
+    }
+
+
+def main() -> int:
+    logmsg("[cold-probes] Phase 2 pre-flight START")
+    env = load_env()
+    or_key = env.get("OPENROUTER_API_KEY", "").strip()
+    moonshot_key = env.get("MOONSHOT_API_KEY", "").strip()
+    if not or_key or not moonshot_key:
+        logmsg("[cold-probes] FATAL missing keys (OR or MOONSHOT)")
+        return 2
+
+    sample = []
+    with SAMPLE_PATH.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                sample.append(json.loads(line))
+    sample = sample[:3]  # first 3 instances
+    logmsg(f"[cold-probes] loaded {len(sample)} probe instances from §1.3h split sample")
+
+    rows = []
+    for i, s in enumerate(sample):
+        prompt = JUDGE_PROMPT_TEMPLATE.format(
+            question=s["question"], ground_truth=s["ground_truth"],
+            context=s["context"], model_answer=s["model_answer"],
+        )
+        for fn, label in ((call_minimax, "minimax"), (call_kimi, "kimi")):
+            resp = fn(prompt, or_key if label == "minimax" else moonshot_key)
+            verdict, fm, rat = parse_verdict(resp["raw_text"])
+            rows.append({
+                "instance_id": s["instance_id"],
+                "cell": s["cell"],
+                "provider": resp["provider"],
+                "alias": resp["alias"],
+                "routing": resp["routing"],
+                "http_status": resp["status"],
+                "error": resp.get("error"),
+                "latency_ms": resp["latency_ms"],
+                "prompt_tokens": resp.get("prompt_tokens"),
+                "completion_tokens": resp.get("completion_tokens"),
+                "parsed_verdict": verdict,
+                "parsed_failure_mode": fm,
+                "parsed_rationale": rat,
+                "raw_text": resp["raw_text"],
+                "opus_verdict_ref": s.get("opus_verdict"),
+                "gpt_verdict_ref": s.get("gpt_verdict"),
+            })
+            logmsg(
+                f"[cold-probes] {label:7} {i+1}/3 {s['instance_id']} status={resp['status']} "
+                f"verdict={verdict} lat={resp['latency_ms']}ms"
+            )
+
+    OUT_PATH.parent.mkdir(parents=True, exist_ok=True)
+    with OUT_PATH.open("w", encoding="utf-8") as f:
+        for r in rows:
+            f.write(json.dumps(r, ensure_ascii=False) + "\n")
+
+    # Summary
+    mm_rows = [r for r in rows if r["provider"] == "minimax"]
+    km_rows = [r for r in rows if r["provider"] == "kimi"]
+    mm_parsed = sum(1 for r in mm_rows if r["parsed_verdict"] is not None)
+    km_parsed = sum(1 for r in km_rows if r["parsed_verdict"] is not None)
+    logmsg(f"[cold-probes] SUMMARY MiniMax: {mm_parsed}/3 parsed  Kimi: {km_parsed}/3 parsed")
+    logmsg("[cold-probes] END")
+    return 0 if (mm_parsed == 3 and km_parsed == 3) else 3
+
+
+if __name__ == "__main__":
+    sys.exit(main())

benchmarks/calibration/v6-kappa-recal/kappa-sample-instances.jsonl

@@ -0,0 +1,100 @@
+{"instance_id": "locomo_conv-50_q086", "cell": "no-context", "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", "ground_truth": "Skiing", "context": "Session 9 (3:15 pm on 21 June, 2023):\nCalvin: Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak. [image: a photo of a person on skis on a snowy mountain]", "model_answer": "Surfing", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-26_q059", "cell": "no-context", "question": "Would Caroline be considered religious?", "ground_truth": "Somewhat, but not extremely religious", "context": "Session 12 (1:50 pm on 17 August, 2023):\nCaroline: Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!\n\nSession 14 (1:33 pm on 25 August, 2023):\nCaroline: Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change. [image: a photo of a large stained glass window in a church]", "model_answer": "Cannot be determined without context.", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-44_q000", "cell": "no-context", "question": "Which year did Audrey adopt the first three of her dogs?", "ground_truth": "2020", "context": "Session 1 (1:10 pm on 27 March, 2023):\nAudrey: I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!", "model_answer": "1954", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-41_q080", "cell": "no-context", "question": "What did Maria make for her home to remind her of a trip to England?", "ground_truth": "painting of a castle on a hill", "context": "Session 8 (6:03 pm on 6 March, 2023):\nMaria: Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too! [image: a photography of a painting of a castle on a hill]", "model_answer": "A wind chime", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-42_q001", "cell": "no-context", "question": "What kind of interests do Joanna and Nate share?", "ground_truth": "Watching movies, making desserts", "context": "Session 1 (7:31 pm on 21 January, 2022):\nJoanna: Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?\nNate: Playing video games and watching movies are my main hobbies.\nJoanna: Cool, Nate! So we both have similar interests. What type of movies do you like best?\n\nSession 3 (9:27 am on 7 February, 2022):\nNate: Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself. [image: a photo of a bowl of ice cream with a spoon in it]\n\nSession 4 (1:07 pm on 25 February, 2022):\nNate: Thanks, Joanna! It means a lot that you enjoy the desserts I bake.\n\nSession 10 (11:54 am on 2 May, 2022):\nJoanna: Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently! [image: a photo of a cake with white frosting on a wooden table]\n\nSession 20 (6:03 pm on 5 September, 2022):\nJoanna: Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this! [image: a photo of a piece of cake with strawberries and chocolate]", "model_answer": "Reading", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-43_q155", "cell": "no-context", "question": "How did John feel after being able to jog without pain?", "ground_truth": "It was a huge success.", "context": "Session 24 (3:37 pm on 16 December, 2023):\nJohn: It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate.", "model_answer": "Relieved.", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-50_q015", "cell": "no-context", "question": "Why does Dave regularly visit parks?", "ground_truth": "because it relaxes and calms him", "context": "Session 1 (11:53 am on 23 March, 2023):\nDave: That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before? [image: a photography of a park with a lake and a few boats]\n\nSession 8 (2:31 pm on 9 June, 2023):\nDave: Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?", "model_answer": "Unknown.", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-48_q020", "cell": "no-context", "question": "Which games have Jolene and her partner played together?", "ground_truth": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity! [image: a photo of a person laying in bed with a dog watching tv]\nJolene: We are planning to play \"Walking Dead\" next Saturday.\n\nSession 15 (7:37 pm on 9 July, 2023):\nJolene: Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?\n\nSession 19 (12:52 am on 19 August, 2023):\nJolene: Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!\n\nSession 20 (9:11 am on 21 August, 2023):\nJolene:  Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?", "model_answer": "Animal Crossing and Minecraft", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-48_q055", "cell": "no-context", "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", "ground_truth": "Seraphim", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: I bought it a year ago in Paris.\nJolene: Even as a child I learned to play on my own.\n\nSession 16 (9:26 am on 1 August, 2023):\nJolene: I adopted her two years ago when I was feeling lonely and wanted some company.", "model_answer": "Seraphim", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-26_q128", "cell": "no-context", "question": "What did Caroline find in her neighborhood during her walk?", "ground_truth": "a rainbow sidewalk", "context": "Session 14 (1:33 pm on 25 August, 2023):\nCaroline: I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere—even where we least expect it.", "model_answer": "A butterfly.", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-50_q010", "cell": "no-context", "question": "When did Calvin's place get flooded in Tokyo?", "ground_truth": "On a week before 16 May, 2023", "context": "Session 6 (11:50 am on 16 May, 2023):\nCalvin: Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up.", "model_answer": "October 2019", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-50_q078", "cell": "no-context", "question": "What was the necklace Calvin received meant to remind him of?", "ground_truth": "why he keeps hustling as a musician", "context": "Session 4 (6:24 pm on 1 May, 2023):\nCalvin: Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!", "model_answer": "Home.", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-47_q092", "cell": "no-context", "question": "What did John create for the charitable foundation that helped generate reports for analysis?", "ground_truth": "computer application on smartphones", "context": "Session 11 (5:00 pm on 11 May, 2022):\nJohn: Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone.", "model_answer": "A database", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-30_q057", "cell": "no-context", "question": "What advice does Gina give to Jon about running a successful business?", "ground_truth": "build relationships with customers, create a strong brand image, stay positive", "context": "Session 7 (7:28 pm on 23 March, 2023):\nJon: Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers – let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!", "model_answer": "Focus on the customer.", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-30_q029", "cell": "no-context", "question": "Which cities has Jon visited?", "ground_truth": "Paris, Rome", "context": "Session 2 (2:32 pm on 29 January, 2023):\nJon: Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool. [image: a photo of a bathroom with a blue floor and a pink wall]\n\nSession 15 (10:04 am on 19 June, 2023):\nJon: Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little.", "model_answer": "Unknown.", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-44_q066", "cell": "no-context", "question": "What type of dog was Andrew looking to adopt based on his living space?", "ground_truth": "smaller dog", "context": "Session 5 (10:47 am on 6 May, 2023):\nAndrew: Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run.", "model_answer": "A small dog", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q002", "cell": "no-context", "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", "ground_truth": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", "context": "Session 13 (3:52 pm on 27 July, 2023):\nAndrew: Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!\n\nSession 19 (5:53 pm on 24 September, 2023):\nAndrew: Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers! [image: a photo of a balcony with a bunch of flowers on it]\n\nSession 23 (4:22 pm on 13 October, 2023):\nAndrew: Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately? [image: a photography of a dog sitting at a chess board with a chess set in front of him]\n\nSession 25 (10:14 am on 24 October, 2023):\nAndrew: Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!", "model_answer": "Board games", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-47_q046", "cell": "no-context", "question": "When did John and James meet at McGee's bar?", "ground_truth": "August 27, 2022", "context": "Session 21 (9:18 pm on 26 August, 2022):\nJohn: Great, then I agree! See you tomorrow at McGee's Pub!", "model_answer": "Never", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-43_q076", "cell": "no-context", "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", "ground_truth": "MinaLima's creation from the Harry Potter films", "context": "Session 2 (5:08 pm on 15 June, 2023):\nTim: Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!", "model_answer": "A selfie", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-41_q065", "cell": "no-context", "question": "What is John's main focus in local politics?", "ground_truth": "Improving education and infrastructure", "context": "Session 1 (11:01 am on 17 December, 2022):\nJohn: I'm passionate about improving education and infrastructure in our community. Those are my main focuses.", "model_answer": "Unknown which John.", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-50_q086", "cell": "oracle-context", "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", "ground_truth": "Skiing", "context": "Session 9 (3:15 pm on 21 June, 2023):\nCalvin: Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak. [image: a photo of a person on skis on a snowy mountain]", "model_answer": "Skiing", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-26_q059", "cell": "oracle-context", "question": "Would Caroline be considered religious?", "ground_truth": "Somewhat, but not extremely religious", "context": "Session 12 (1:50 pm on 17 August, 2023):\nCaroline: Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!\n\nSession 14 (1:33 pm on 25 August, 2023):\nCaroline: Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change. [image: a photo of a large stained glass window in a church]", "model_answer": "Yes", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-44_q000", "cell": "oracle-context", "question": "Which year did Audrey adopt the first three of her dogs?", "ground_truth": "2020", "context": "Session 1 (1:10 pm on 27 March, 2023):\nAudrey: I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!", "model_answer": "2020", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-41_q080", "cell": "oracle-context", "question": "What did Maria make for her home to remind her of a trip to England?", "ground_truth": "painting of a castle on a hill", "context": "Session 8 (6:03 pm on 6 March, 2023):\nMaria: Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too! [image: a photography of a painting of a castle on a hill]", "model_answer": "A painting", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-42_q001", "cell": "oracle-context", "question": "What kind of interests do Joanna and Nate share?", "ground_truth": "Watching movies, making desserts", "context": "Session 1 (7:31 pm on 21 January, 2022):\nJoanna: Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?\nNate: Playing video games and watching movies are my main hobbies.\nJoanna: Cool, Nate! So we both have similar interests. What type of movies do you like best?\n\nSession 3 (9:27 am on 7 February, 2022):\nNate: Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself. [image: a photo of a bowl of ice cream with a spoon in it]\n\nSession 4 (1:07 pm on 25 February, 2022):\nNate: Thanks, Joanna! It means a lot that you enjoy the desserts I bake.\n\nSession 10 (11:54 am on 2 May, 2022):\nJoanna: Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently! [image: a photo of a cake with white frosting on a wooden table]\n\nSession 20 (6:03 pm on 5 September, 2022):\nJoanna: Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this! [image: a photo of a piece of cake with strawberries and chocolate]", "model_answer": "Movies", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-43_q155", "cell": "oracle-context", "question": "How did John feel after being able to jog without pain?", "ground_truth": "It was a huge success.", "context": "Session 24 (3:37 pm on 16 December, 2023):\nJohn: It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate.", "model_answer": "Great", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q015", "cell": "oracle-context", "question": "Why does Dave regularly visit parks?", "ground_truth": "because it relaxes and calms him", "context": "Session 1 (11:53 am on 23 March, 2023):\nDave: That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before? [image: a photography of a park with a lake and a few boats]\n\nSession 8 (2:31 pm on 9 June, 2023):\nDave: Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?", "model_answer": "To relax", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-48_q020", "cell": "oracle-context", "question": "Which games have Jolene and her partner played together?", "ground_truth": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity! [image: a photo of a person laying in bed with a dog watching tv]\nJolene: We are planning to play \"Walking Dead\" next Saturday.\n\nSession 15 (7:37 pm on 9 July, 2023):\nJolene: Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?\n\nSession 19 (12:52 am on 19 August, 2023):\nJolene: Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!\n\nSession 20 (9:11 am on 21 August, 2023):\nJolene:  Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?", "model_answer": "Detroit, It Takes Two, Overcooked 2", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-48_q055", "cell": "oracle-context", "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", "ground_truth": "Seraphim", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: I bought it a year ago in Paris.\nJolene: Even as a child I learned to play on my own.\n\nSession 16 (9:26 am on 1 August, 2023):\nJolene: I adopted her two years ago when I was feeling lonely and wanted some company.", "model_answer": "Susie", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-26_q128", "cell": "oracle-context", "question": "What did Caroline find in her neighborhood during her walk?", "ground_truth": "a rainbow sidewalk", "context": "Session 14 (1:33 pm on 25 August, 2023):\nCaroline: I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere—even where we least expect it.", "model_answer": "A rainbow sidewalk", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q010", "cell": "oracle-context", "question": "When did Calvin's place get flooded in Tokyo?", "ground_truth": "On a week before 16 May, 2023", "context": "Session 6 (11:50 am on 16 May, 2023):\nCalvin: Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up.", "model_answer": "Last week", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-50_q078", "cell": "oracle-context", "question": "What was the necklace Calvin received meant to remind him of?", "ground_truth": "why he keeps hustling as a musician", "context": "Session 4 (6:24 pm on 1 May, 2023):\nCalvin: Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!", "model_answer": "Why he keeps hustling as a musician", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-47_q092", "cell": "oracle-context", "question": "What did John create for the charitable foundation that helped generate reports for analysis?", "ground_truth": "computer application on smartphones", "context": "Session 11 (5:00 pm on 11 May, 2022):\nJohn: Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone.", "model_answer": "An application", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-30_q057", "cell": "oracle-context", "question": "What advice does Gina give to Jon about running a successful business?", "ground_truth": "build relationships with customers, create a strong brand image, stay positive", "context": "Session 7 (7:28 pm on 23 March, 2023):\nJon: Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers – let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!", "model_answer": "Not mentioned.", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-30_q029", "cell": "oracle-context", "question": "Which cities has Jon visited?", "ground_truth": "Paris, Rome", "context": "Session 2 (2:32 pm on 29 January, 2023):\nJon: Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool. [image: a photo of a bathroom with a blue floor and a pink wall]\n\nSession 15 (10:04 am on 19 June, 2023):\nJon: Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little.", "model_answer": "Paris and Rome", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q066", "cell": "oracle-context", "question": "What type of dog was Andrew looking to adopt based on his living space?", "ground_truth": "smaller dog", "context": "Session 5 (10:47 am on 6 May, 2023):\nAndrew: Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run.", "model_answer": "A smaller dog", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q002", "cell": "oracle-context", "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", "ground_truth": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", "context": "Session 13 (3:52 pm on 27 July, 2023):\nAndrew: Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!\n\nSession 19 (5:53 pm on 24 September, 2023):\nAndrew: Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers! [image: a photo of a balcony with a bunch of flowers on it]\n\nSession 23 (4:22 pm on 13 October, 2023):\nAndrew: Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately? [image: a photography of a dog sitting at a chess board with a chess set in front of him]\n\nSession 25 (10:14 am on 24 October, 2023):\nAndrew: Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!", "model_answer": "Board games.", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-47_q046", "cell": "oracle-context", "question": "When did John and James meet at McGee's bar?", "ground_truth": "August 27, 2022", "context": "Session 21 (9:18 pm on 26 August, 2022):\nJohn: Great, then I agree! See you tomorrow at McGee's Pub!", "model_answer": "27 August, 2022", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-43_q076", "cell": "oracle-context", "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", "ground_truth": "MinaLima's creation from the Harry Potter films", "context": "Session 2 (5:08 pm on 15 June, 2023):\nTim: Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!", "model_answer": "MinaLima", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-41_q065", "cell": "oracle-context", "question": "What is John's main focus in local politics?", "ground_truth": "Improving education and infrastructure", "context": "Session 1 (11:01 am on 17 December, 2022):\nJohn: I'm passionate about improving education and infrastructure in our community. Those are my main focuses.", "model_answer": "Education and infrastructure", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q086", "cell": "full-context", "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", "ground_truth": "Skiing", "context": "Session 9 (3:15 pm on 21 June, 2023):\nCalvin: Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak. [image: a photo of a person on skis on a snowy mountain]", "model_answer": "skiing", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-26_q059", "cell": "full-context", "question": "Would Caroline be considered religious?", "ground_truth": "Somewhat, but not extremely religious", "context": "Session 12 (1:50 pm on 17 August, 2023):\nCaroline: Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!\n\nSession 14 (1:33 pm on 25 August, 2023):\nCaroline: Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change. [image: a photo of a large stained glass window in a church]", "model_answer": "unknown", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-44_q000", "cell": "full-context", "question": "Which year did Audrey adopt the first three of her dogs?", "ground_truth": "2020", "context": "Session 1 (1:10 pm on 27 March, 2023):\nAudrey: I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!", "model_answer": "2020", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-41_q080", "cell": "full-context", "question": "What did Maria make for her home to remind her of a trip to England?", "ground_truth": "painting of a castle on a hill", "context": "Session 8 (6:03 pm on 6 March, 2023):\nMaria: Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too! [image: a photography of a painting of a castle on a hill]", "model_answer": "a painting", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-42_q001", "cell": "full-context", "question": "What kind of interests do Joanna and Nate share?", "ground_truth": "Watching movies, making desserts", "context": "Session 1 (7:31 pm on 21 January, 2022):\nJoanna: Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?\nNate: Playing video games and watching movies are my main hobbies.\nJoanna: Cool, Nate! So we both have similar interests. What type of movies do you like best?\n\nSession 3 (9:27 am on 7 February, 2022):\nNate: Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself. [image: a photo of a bowl of ice cream with a spoon in it]\n\nSession 4 (1:07 pm on 25 February, 2022):\nNate: Thanks, Joanna! It means a lot that you enjoy the desserts I bake.\n\nSession 10 (11:54 am on 2 May, 2022):\nJoanna: Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently! [image: a photo of a cake with white frosting on a wooden table]\n\nSession 20 (6:03 pm on 5 September, 2022):\nJoanna: Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this! [image: a photo of a piece of cake with strawberries and chocolate]", "model_answer": "watching movies", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-43_q155", "cell": "full-context", "question": "How did John feel after being able to jog without pain?", "ground_truth": "It was a huge success.", "context": "Session 24 (3:37 pm on 16 December, 2023):\nJohn: It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate.", "model_answer": "great", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q015", "cell": "full-context", "question": "Why does Dave regularly visit parks?", "ground_truth": "because it relaxes and calms him", "context": "Session 1 (11:53 am on 23 March, 2023):\nDave: That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before? [image: a photography of a park with a lake and a few boats]\n\nSession 8 (2:31 pm on 9 June, 2023):\nDave: Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?", "model_answer": "to relax", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-48_q020", "cell": "full-context", "question": "Which games have Jolene and her partner played together?", "ground_truth": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity! [image: a photo of a person laying in bed with a dog watching tv]\nJolene: We are planning to play \"Walking Dead\" next Saturday.\n\nSession 15 (7:37 pm on 9 July, 2023):\nJolene: Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?\n\nSession 19 (12:52 am on 19 August, 2023):\nJolene: Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!\n\nSession 20 (9:11 am on 21 August, 2023):\nJolene:  Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?", "model_answer": "Detroit, It takes two, Overcooked 2", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-48_q055", "cell": "full-context", "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", "ground_truth": "Seraphim", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: I bought it a year ago in Paris.\nJolene: Even as a child I learned to play on my own.\n\nSession 16 (9:26 am on 1 August, 2023):\nJolene: I adopted her two years ago when I was feeling lonely and wanted some company.", "model_answer": "unknown", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-26_q128", "cell": "full-context", "question": "What did Caroline find in her neighborhood during her walk?", "ground_truth": "a rainbow sidewalk", "context": "Session 14 (1:33 pm on 25 August, 2023):\nCaroline: I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere—even where we least expect it.", "model_answer": "rainbow sidewalk", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q010", "cell": "full-context", "question": "When did Calvin's place get flooded in Tokyo?", "ground_truth": "On a week before 16 May, 2023", "context": "Session 6 (11:50 am on 16 May, 2023):\nCalvin: Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up.", "model_answer": "last week", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-50_q078", "cell": "full-context", "question": "What was the necklace Calvin received meant to remind him of?", "ground_truth": "why he keeps hustling as a musician", "context": "Session 4 (6:24 pm on 1 May, 2023):\nCalvin: Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!", "model_answer": "why I keep hustling as a musician", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-47_q092", "cell": "full-context", "question": "What did John create for the charitable foundation that helped generate reports for analysis?", "ground_truth": "computer application on smartphones", "context": "Session 11 (5:00 pm on 11 May, 2022):\nJohn: Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone.", "model_answer": "an application", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-30_q057", "cell": "full-context", "question": "What advice does Gina give to Jon about running a successful business?", "ground_truth": "build relationships with customers, create a strong brand image, stay positive", "context": "Session 7 (7:28 pm on 23 March, 2023):\nJon: Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers – let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!", "model_answer": "unknown", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-30_q029", "cell": "full-context", "question": "Which cities has Jon visited?", "ground_truth": "Paris, Rome", "context": "Session 2 (2:32 pm on 29 January, 2023):\nJon: Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool. [image: a photo of a bathroom with a blue floor and a pink wall]\n\nSession 15 (10:04 am on 19 June, 2023):\nJon: Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little.", "model_answer": "Paris Rome", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q066", "cell": "full-context", "question": "What type of dog was Andrew looking to adopt based on his living space?", "ground_truth": "smaller dog", "context": "Session 5 (10:47 am on 6 May, 2023):\nAndrew: Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run.", "model_answer": "smaller dog", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q002", "cell": "full-context", "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", "ground_truth": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", "context": "Session 13 (3:52 pm on 27 July, 2023):\nAndrew: Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!\n\nSession 19 (5:53 pm on 24 September, 2023):\nAndrew: Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers! [image: a photo of a balcony with a bunch of flowers on it]\n\nSession 23 (4:22 pm on 13 October, 2023):\nAndrew: Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately? [image: a photography of a dog sitting at a chess board with a chess set in front of him]\n\nSession 25 (10:14 am on 24 October, 2023):\nAndrew: Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!", "model_answer": "playing board games", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-47_q046", "cell": "full-context", "question": "When did John and James meet at McGee's bar?", "ground_truth": "August 27, 2022", "context": "Session 21 (9:18 pm on 26 August, 2022):\nJohn: Great, then I agree! See you tomorrow at McGee's Pub!", "model_answer": "tomorrow", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-43_q076", "cell": "full-context", "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", "ground_truth": "MinaLima's creation from the Harry Potter films", "context": "Session 2 (5:08 pm on 15 June, 2023):\nTim: Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!", "model_answer": "from MinaLima", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-41_q065", "cell": "full-context", "question": "What is John's main focus in local politics?", "ground_truth": "Improving education and infrastructure", "context": "Session 1 (11:01 am on 17 December, 2022):\nJohn: I'm passionate about improving education and infrastructure in our community. Those are my main focuses.", "model_answer": "improving education and infrastructure", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q086", "cell": "retrieval", "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", "ground_truth": "Skiing", "context": "Session 9 (3:15 pm on 21 June, 2023):\nCalvin: Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak. [image: a photo of a person on skis on a snowy mountain]", "model_answer": "Not mentioned", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-26_q059", "cell": "retrieval", "question": "Would Caroline be considered religious?", "ground_truth": "Somewhat, but not extremely religious", "context": "Session 12 (1:50 pm on 17 August, 2023):\nCaroline: Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!\n\nSession 14 (1:33 pm on 25 August, 2023):\nCaroline: Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change. [image: a photo of a large stained glass window in a church]", "model_answer": "No.", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-44_q000", "cell": "retrieval", "question": "Which year did Audrey adopt the first three of her dogs?", "ground_truth": "2020", "context": "Session 1 (1:10 pm on 27 March, 2023):\nAudrey: I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!", "model_answer": "Not mentioned.", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-41_q080", "cell": "retrieval", "question": "What did Maria make for her home to remind her of a trip to England?", "ground_truth": "painting of a castle on a hill", "context": "Session 8 (6:03 pm on 6 March, 2023):\nMaria: Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too! [image: a photography of a painting of a castle on a hill]", "model_answer": "A painting.", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-42_q001", "cell": "retrieval", "question": "What kind of interests do Joanna and Nate share?", "ground_truth": "Watching movies, making desserts", "context": "Session 1 (7:31 pm on 21 January, 2022):\nJoanna: Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?\nNate: Playing video games and watching movies are my main hobbies.\nJoanna: Cool, Nate! So we both have similar interests. What type of movies do you like best?\n\nSession 3 (9:27 am on 7 February, 2022):\nNate: Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself. [image: a photo of a bowl of ice cream with a spoon in it]\n\nSession 4 (1:07 pm on 25 February, 2022):\nNate: Thanks, Joanna! It means a lot that you enjoy the desserts I bake.\n\nSession 10 (11:54 am on 2 May, 2022):\nJoanna: Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently! [image: a photo of a cake with white frosting on a wooden table]\n\nSession 20 (6:03 pm on 5 September, 2022):\nJoanna: Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this! [image: a photo of a piece of cake with strawberries and chocolate]", "model_answer": "Movies and nature.", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-43_q155", "cell": "retrieval", "question": "How did John feel after being able to jog without pain?", "ground_truth": "It was a huge success.", "context": "Session 24 (3:37 pm on 16 December, 2023):\nJohn: It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate.", "model_answer": "Relieved", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-50_q015", "cell": "retrieval", "question": "Why does Dave regularly visit parks?", "ground_truth": "because it relaxes and calms him", "context": "Session 1 (11:53 am on 23 March, 2023):\nDave: That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before? [image: a photography of a park with a lake and a few boats]\n\nSession 8 (2:31 pm on 9 June, 2023):\nDave: Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?", "model_answer": "To relax, recharge, and hang out with friends.", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-48_q020", "cell": "retrieval", "question": "Which games have Jolene and her partner played together?", "ground_truth": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity! [image: a photo of a person laying in bed with a dog watching tv]\nJolene: We are planning to play \"Walking Dead\" next Saturday.\n\nSession 15 (7:37 pm on 9 July, 2023):\nJolene: Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?\n\nSession 19 (12:52 am on 19 August, 2023):\nJolene: Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!\n\nSession 20 (9:11 am on 21 August, 2023):\nJolene:  Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?", "model_answer": "Detroit, Overcooked 2, It Takes Two", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-48_q055", "cell": "retrieval", "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", "ground_truth": "Seraphim", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: I bought it a year ago in Paris.\nJolene: Even as a child I learned to play on my own.\n\nSession 16 (9:26 am on 1 August, 2023):\nJolene: I adopted her two years ago when I was feeling lonely and wanted some company.", "model_answer": "Seraphim", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-26_q128", "cell": "retrieval", "question": "What did Caroline find in her neighborhood during her walk?", "ground_truth": "a rainbow sidewalk", "context": "Session 14 (1:33 pm on 25 August, 2023):\nCaroline: I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere—even where we least expect it.", "model_answer": "A rainbow sidewalk.", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q010", "cell": "retrieval", "question": "When did Calvin's place get flooded in Tokyo?", "ground_truth": "On a week before 16 May, 2023", "context": "Session 6 (11:50 am on 16 May, 2023):\nCalvin: Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up.", "model_answer": "Last week.", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-50_q078", "cell": "retrieval", "question": "What was the necklace Calvin received meant to remind him of?", "ground_truth": "why he keeps hustling as a musician", "context": "Session 4 (6:24 pm on 1 May, 2023):\nCalvin: Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!", "model_answer": "Why he keeps hustling as a musician.", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-47_q092", "cell": "retrieval", "question": "What did John create for the charitable foundation that helped generate reports for analysis?", "ground_truth": "computer application on smartphones", "context": "Session 11 (5:00 pm on 11 May, 2022):\nJohn: Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone.", "model_answer": "A software tool", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-30_q057", "cell": "retrieval", "question": "What advice does Gina give to Jon about running a successful business?", "ground_truth": "build relationships with customers, create a strong brand image, stay positive", "context": "Session 7 (7:28 pm on 23 March, 2023):\nJon: Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers – let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!", "model_answer": "Stay passionate, focused, and resilient; believe in yourself, keep going, and stay open to learning.", "opus_verdict": "incorrect", "opus_failure_mode": "F4", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-30_q029", "cell": "retrieval", "question": "Which cities has Jon visited?", "ground_truth": "Paris, Rome", "context": "Session 2 (2:32 pm on 29 January, 2023):\nJon: Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool. [image: a photo of a bathroom with a blue floor and a pink wall]\n\nSession 15 (10:04 am on 19 June, 2023):\nJon: Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little.", "model_answer": "Paris", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-44_q066", "cell": "retrieval", "question": "What type of dog was Andrew looking to adopt based on his living space?", "ground_truth": "smaller dog", "context": "Session 5 (10:47 am on 6 May, 2023):\nAndrew: Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run.", "model_answer": "A smaller dog.", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q002", "cell": "retrieval", "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", "ground_truth": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", "context": "Session 13 (3:52 pm on 27 July, 2023):\nAndrew: Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!\n\nSession 19 (5:53 pm on 24 September, 2023):\nAndrew: Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers! [image: a photo of a balcony with a bunch of flowers on it]\n\nSession 23 (4:22 pm on 13 October, 2023):\nAndrew: Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately? [image: a photography of a dog sitting at a chess board with a chess set in front of him]\n\nSession 25 (10:14 am on 24 October, 2023):\nAndrew: Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!", "model_answer": "Wine tasting", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-47_q046", "cell": "retrieval", "question": "When did John and James meet at McGee's bar?", "ground_truth": "August 27, 2022", "context": "Session 21 (9:18 pm on 26 August, 2022):\nJohn: Great, then I agree! See you tomorrow at McGee's Pub!", "model_answer": "Tomorrow", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-43_q076", "cell": "retrieval", "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", "ground_truth": "MinaLima's creation from the Harry Potter films", "context": "Session 2 (5:08 pm on 15 June, 2023):\nTim: Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!", "model_answer": "MinaLima props", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-41_q065", "cell": "retrieval", "question": "What is John's main focus in local politics?", "ground_truth": "Improving education and infrastructure", "context": "Session 1 (11:01 am on 17 December, 2022):\nJohn: I'm passionate about improving education and infrastructure in our community. Those are my main focuses.", "model_answer": "Education and infrastructure", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q086", "cell": "agentic", "question": "What sports activity is Calvin planning to try after the tour with Frank Ocean?", "ground_truth": "Skiing", "context": "Session 9 (3:15 pm on 21 June, 2023):\nCalvin: Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak. [image: a photo of a person on skis on a snowy mountain]", "model_answer": "Max tool turns reached (3 turns, 3 tools used).", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-26_q059", "cell": "agentic", "question": "Would Caroline be considered religious?", "ground_truth": "Somewhat, but not extremely religious", "context": "Session 12 (1:50 pm on 17 August, 2023):\nCaroline: Hey Mel! How're ya doin'? Recently, I had a not-so-great experience on a hike. I ran into a group of religious conservatives who said something that really upset me. It made me think how much work we still have to do for LGBTQ rights. It's been so helpful to have people around me who accept and support me, so I know I'll be ok!\n\nSession 14 (1:33 pm on 25 August, 2023):\nCaroline: Thanks! It was made for a local church and shows time changing our lives. I made it to show my own journey as a transgender woman and how we should accept growth and change. [image: a photo of a large stained glass window in a church]", "model_answer": "Yes, Caroline has a connection to faith through her heritage (a necklace from Sweden representing \"love, faith and strength\") and by having made a painting for a local church.", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F4", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-44_q000", "cell": "agentic", "question": "Which year did Audrey adopt the first three of her dogs?", "ground_truth": "2020", "context": "Session 1 (1:10 pm on 27 March, 2023):\nAudrey: I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!", "model_answer": "Max tool turns reached (3 turns, 3 tools used).", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-41_q080", "cell": "agentic", "question": "What did Maria make for her home to remind her of a trip to England?", "ground_truth": "painting of a castle on a hill", "context": "Session 8 (6:03 pm on 6 March, 2023):\nMaria: Thanks, John! I got the idea from that trip to England a few years ago - I was mesmerized by the castles. I wanted to bring that same magic to my home, like a reminder of the world's beauty. I made a painting too! [image: a photography of a painting of a castle on a hill]", "model_answer": "a painting", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-42_q001", "cell": "agentic", "question": "What kind of interests do Joanna and Nate share?", "ground_truth": "Watching movies, making desserts", "context": "Session 1 (7:31 pm on 21 January, 2022):\nJoanna: Yeah! Besides writing, I also enjoy reading, watching movies, and exploring nature. Anything else you enjoy doing, Nate?\nNate: Playing video games and watching movies are my main hobbies.\nJoanna: Cool, Nate! So we both have similar interests. What type of movies do you like best?\n\nSession 3 (9:27 am on 7 February, 2022):\nNate: Thanks, Joanna. Not much has changed for me, but I just discovered that I can make coconut milk icecream and gave it a try. It was actually pretty good, so I'm proud of myself. [image: a photo of a bowl of ice cream with a spoon in it]\n\nSession 4 (1:07 pm on 25 February, 2022):\nNate: Thanks, Joanna! It means a lot that you enjoy the desserts I bake.\n\nSession 10 (11:54 am on 2 May, 2022):\nJoanna: Not much is new other than the screenplay. Been working on some projects and testing out dairy-free dessert recipes for friends and fam. Here's a pic of a cake I made recently! [image: a photo of a cake with white frosting on a wooden table]\n\nSession 20 (6:03 pm on 5 September, 2022):\nJoanna: Hey Nate! Cute turtles! Bummer about the setback. Any positive vibes comin' your way? I just revised on of my old recipes and made this! [image: a photo of a piece of cake with strawberries and chocolate]", "model_answer": "movies", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-43_q155", "cell": "agentic", "question": "How did John feel after being able to jog without pain?", "ground_truth": "It was a huge success.", "context": "Session 24 (3:37 pm on 16 December, 2023):\nJohn: It was great! After being out for so long, jogging without any pain was a huge success. My wife and I hosted a small get-together with friends and family to celebrate.", "model_answer": "great", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q015", "cell": "agentic", "question": "Why does Dave regularly visit parks?", "ground_truth": "because it relaxes and calms him", "context": "Session 1 (11:53 am on 23 March, 2023):\nDave: That's great, can't wait for you to return from your trip. I've been spending lots of time at this beautiful park lately - so calming. Have you been there before? [image: a photography of a park with a lake and a few boats]\n\nSession 8 (2:31 pm on 9 June, 2023):\nDave: Hey, nice photo of the studio! Working in a team yields incredible results, well done! Keep pushing it! I've been doing good - thanks for asking. I've been exploring some parks on the weekends to relax - it's so peaceful being surrounded by nature. Are there any chill spots you enjoy in Boston?", "model_answer": "to relax and recharge", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-48_q020", "cell": "agentic", "question": "Which games have Jolene and her partner played together?", "ground_truth": "Detroit, Walking Dead, Battlefield 1, It Takes Two, Overcooked 2", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: They are very unusual pets! Here's me and my partner gaming last week - it's so fun. We played the game \"Detroit\" on the console. We are both crazy about this activity! [image: a photo of a person laying in bed with a dog watching tv]\nJolene: We are planning to play \"Walking Dead\" next Saturday.\n\nSession 15 (7:37 pm on 9 July, 2023):\nJolene: Yeah, we love playing \"It takes two\" together! It's a fun team-strategy game and it's competitive. Plus, it's a great way for us to bond. Do you have any activities you like doing with people?\n\nSession 19 (12:52 am on 19 August, 2023):\nJolene: Oh, I forgot to mention Overcooked 2 - this is a good co-op game if you're into hilarious and chaotic cooking. My partner and I often play for bets! I once won three large pizzas!\n\nSession 20 (9:11 am on 21 August, 2023):\nJolene:  Long time no talk! We were given a new game for the console last week, it is Battlefield 1. What's been up with you?", "model_answer": "Detroit, Overcooked 2, It Takes Two", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-48_q055", "cell": "agentic", "question": "Which pet did Jolene adopt more recently - Susie or Seraphim?", "ground_truth": "Seraphim", "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: I bought it a year ago in Paris.\nJolene: Even as a child I learned to play on my own.\n\nSession 16 (9:26 am on 1 August, 2023):\nJolene: I adopted her two years ago when I was feeling lonely and wanted some company.", "model_answer": "Susie", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F3", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-26_q128", "cell": "agentic", "question": "What did Caroline find in her neighborhood during her walk?", "ground_truth": "a rainbow sidewalk", "context": "Session 14 (1:33 pm on 25 August, 2023):\nCaroline: I was out walking in my neighborhood when I came across this cool rainbow sidewalk for Pride Month. It was so vibrant and welcoming, I had to take a picture! It reminds us that love and acceptance are everywhere—even where we least expect it.", "model_answer": "a rainbow sidewalk", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-50_q010", "cell": "agentic", "question": "When did Calvin's place get flooded in Tokyo?", "ground_truth": "On a week before 16 May, 2023", "context": "Session 6 (11:50 am on 16 May, 2023):\nCalvin: Hey Dave, not everything has been going smoothly. I had an incident last week where my place got flooded, but thankfully, I managed to save my music gear and favorite microphone. It's been tough, but I'm staying positive and looking forward to getting everything fixed up.", "model_answer": "last week", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-50_q078", "cell": "agentic", "question": "What was the necklace Calvin received meant to remind him of?", "ground_truth": "why he keeps hustling as a musician", "context": "Session 4 (6:24 pm on 1 May, 2023):\nCalvin: Thanks, Dave! I got it from another artist as a gift - it's a great reminder of why I keep hustling as a musician!", "model_answer": "why he keeps hustling as a musician", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-47_q092", "cell": "agentic", "question": "What did John create for the charitable foundation that helped generate reports for analysis?", "ground_truth": "computer application on smartphones", "context": "Session 11 (5:00 pm on 11 May, 2022):\nJohn: Previously, this foundation used paper records and all inventory was recorded manually. I made an application that structured their work, and now everything they need for inventory is in one application on their smartphone.", "model_answer": "a software tool", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-30_q057", "cell": "agentic", "question": "What advice does Gina give to Jon about running a successful business?", "ground_truth": "build relationships with customers, create a strong brand image, stay positive", "context": "Session 7 (7:28 pm on 23 March, 2023):\nJon: Yeah, brand identity is key. Make sure yours stands out. Also be sure to build relationships with your customers – let them know you care. And don't forget to stay positive and motivate others. Your energy will be contagious!", "model_answer": "Focus on why you started, have faith in yourself, remind yourself of successes and progress, and have a good support system", "opus_verdict": "incorrect", "opus_failure_mode": "F3", "gpt_verdict": "incorrect", "gpt_failure_mode": "F5", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F4"}
+{"instance_id": "locomo_conv-30_q029", "cell": "agentic", "question": "Which cities has Jon visited?", "ground_truth": "Paris, Rome", "context": "Session 2 (2:32 pm on 29 January, 2023):\nJon: Hey Gina! Thanks for asking. I'm on the hunt for the ideal spot for my dance studio and it's been quite a journey! I've been looking at different places and picturing how the space would look. I even found a place with great natural light! Oh, I've been to Paris yesterday! It was sooo cool. [image: a photo of a bathroom with a blue floor and a pink wall]\n\nSession 15 (10:04 am on 19 June, 2023):\nJon: Hey Gina, hope you're doing great! Still working on my biz. Took a short trip last week to Rome to clear my mind a little.", "model_answer": "unknown", "opus_verdict": "incorrect", "opus_failure_mode": "F1", "gpt_verdict": "incorrect", "gpt_failure_mode": "F1", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F1"}
+{"instance_id": "locomo_conv-44_q066", "cell": "agentic", "question": "What type of dog was Andrew looking to adopt based on his living space?", "ground_truth": "smaller dog", "context": "Session 5 (10:47 am on 6 May, 2023):\nAndrew: Meeting all these adorable pups has been awesome! For those considering getting a pup, the size of living space and the exercise needs of the breed are important. For me, a person living in an apartment, a smaller dog would be best, but if one is active, consider getting one that loves to play and run.", "model_answer": "a smaller dog", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-44_q002", "cell": "agentic", "question": "What kind of indoor activities has Andrew pursued with his girlfriend?", "ground_truth": "boardgames, volunteering at pet shelter, wine tasting, growing flowers", "context": "Session 13 (3:52 pm on 27 July, 2023):\nAndrew: Hey Audrey! How are you? My GF and I just had a great experience volunteering at a pet shelter on Monday - it was so rewarding! We loved spending time with those cute animals and it gave us so much joy. It was so rewarding, it reminded me just how much I love them!\n\nSession 19 (5:53 pm on 24 September, 2023):\nAndrew: Yeah! They really do bring so much into our lives - it's amazing to watch them interact. Here's something I've been taking care of lately. Look at those flowers! [image: a photo of a balcony with a bunch of flowers on it]\n\nSession 23 (4:22 pm on 13 October, 2023):\nAndrew: Hey Audrey, it's been a busy week for me. Last Tuesday, my gf, Toby, and I had a really awesome night playing board games. It was really nice. What's been up with you lately? [image: a photography of a dog sitting at a chess board with a chess set in front of him]\n\nSession 25 (10:14 am on 24 October, 2023):\nAndrew: Hi Audrey! How have you been lately? My girlfriend and I went to this awesome wine tasting last weekend. It was great! We tried so many unique wines and learned a lot. I was surprised at how much I enjoyed it. A reminder to step out of the comfort zone!", "model_answer": "board games", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F2"}
+{"instance_id": "locomo_conv-47_q046", "cell": "agentic", "question": "When did John and James meet at McGee's bar?", "ground_truth": "August 27, 2022", "context": "Session 21 (9:18 pm on 26 August, 2022):\nJohn: Great, then I agree! See you tomorrow at McGee's Pub!", "model_answer": "Tomorrow", "opus_verdict": "incorrect", "opus_failure_mode": "F2", "gpt_verdict": "incorrect", "gpt_failure_mode": "F2", "gemini_verdict_v5": "incorrect", "gemini_failure_mode_v5": "F3"}
+{"instance_id": "locomo_conv-43_q076", "cell": "agentic", "question": "What kind of picture did Tim share as part of their Harry Potter book collection?", "ground_truth": "MinaLima's creation from the Harry Potter films", "context": "Session 2 (5:08 pm on 15 June, 2023):\nTim: Thanks! That picture is from MinaLima. They created all the props for the Harry Potter films, and I love their work. It's like having a piece of the wizarding world at home!", "model_answer": "A picture from MinaLima", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}
+{"instance_id": "locomo_conv-41_q065", "cell": "agentic", "question": "What is John's main focus in local politics?", "ground_truth": "Improving education and infrastructure", "context": "Session 1 (11:01 am on 17 December, 2022):\nJohn: I'm passionate about improving education and infrastructure in our community. Those are my main focuses.", "model_answer": "Education and infrastructure", "opus_verdict": "correct", "opus_failure_mode": null, "gpt_verdict": "correct", "gpt_failure_mode": null, "gemini_verdict_v5": "correct", "gemini_failure_mode_v5": null}

benchmarks/calibration/v6-kappa-recal/kappa-v6-analysis.md

@@ -0,0 +1,91 @@
+# Manifest v6 κ Re-Calibration Analysis
+
+**Date:** 2026-04-24  **Parent:** `38a830e` (v6 Phase 1 Commit 2)  **v6 anchor:** `60d061e`
+
+**Sample:** 100 instances from `benchmarks/results/locomo-mini-n20-retry-2026-04-24T00-02-12Z.jsonl` (authoritative v5 κ set; zero new Opus/GPT calls).
+
+**MiniMax verdicts:** 100 calls via OpenRouter `minimax/minimax-m2.7` (v6 alias: `minimax-m27-via-openrouter`); direct HTTP probe (LiteLLM proxy not in loop — isolates model behavior from middleware).
+
+**Prompt:** verbatim `failure-mode-judge.ts:245-258` (same as §1.3g / §1.3h / §1.3h-C).
+**Parameters:** `temperature=0.0`, `max_tokens=4096`.
+
+---
+
+## §1 Three pairwise Cohen's κ
+
+| Pair | n | Agree | Raw % | κ |
+|------|---|-------|-------|-----|
+| Opus vs GPT | 100 | 93 | 93.00% | **0.8480** |
+| Opus vs MiniMax | 100 | 93 | 93.00% | **0.8549** |
+| GPT vs MiniMax | 100 | 90 | 90.00% | **0.7878** |
+
+**Conservative trio κ = min = 0.7878**
+
+## §2 Verdict: **PASS**
+
+Per v6 §5.4 gate criteria:
+- `κ_trio ≥ 0.70` → PASS, halt with PM-RATIFY-V6-KAPPA
+- `0.60 ≤ κ_trio < 0.70` → BORDERLINE, halt with PM adjudication
+- `κ_trio < 0.60` → FAIL, halt with swap-path-re-evaluation
+
+---
+
+## §3 Confusion matrices
+
+### Opus vs GPT
+
+| | GPT=correct | GPT=incorrect |
+|---|---|---|
+| **Opus=correct** | 32 | 7 |
+| **Opus=incorrect** | 0 | 61 |
+
+### Opus vs MiniMax
+
+| | MiniMax=correct | MiniMax=incorrect |
+|---|---|---|
+| **Opus=correct** | 37 | 2 |
+| **Opus=incorrect** | 5 | 56 |
+
+### GPT vs MiniMax
+
+| | MiniMax=correct | MiniMax=incorrect |
+|---|---|---|
+| **GPT=correct** | 32 | 0 |
+| **GPT=incorrect** | 10 | 58 |
+
+---
+
+## §4 Per-cell κ breakdown (n=20 per cell)
+
+| Cell | n | MiniMax parsed | κ(Opus,GPT) | κ(Opus,MiniMax) | κ(GPT,MiniMax) |
+|------|---|-----------------|----------------|-------------------|------------------|
+| no-context | 20 | 20 | 1.0000 | 1.0000 | 1.0000 |
+| oracle-context | 20 | 20 | 0.7059 | 0.7917 | 0.7059 |
+| full-context | 20 | 20 | 0.8000 | 0.7000 | 0.7059 |
+| retrieval | 20 | 20 | 1.0000 | 0.8936 | 0.8936 |
+| agentic | 20 | 20 | 0.7826 | 0.8980 | 0.6875 |
+
+---
+
+## §5 MiniMax operational metrics
+
+- Calls: 100 total, parsed OK: **100/100 (100.0%)**
+- Routing errors (non-200 HTTP): **0/100** (0.0%)
+- Total retries: 0
+- Latency p50: **11.9 s**  |  p95: **31.4 s**
+- Token usage: prompt = 53,855, completion = 48,920
+- Cost actual (OR MiniMax pricing $0.30/$1.20 per 1M): **~$0.0749**
+
+Per brief §3.5 operational hedge thresholds:
+- parse ≥95/100 target: **MET** — actual 100/100
+- parse ≥90/100 halt: **MET** — actual 100/100
+- latency p50 ≤25s: **MET** — actual 11.9s
+- OR routing errors <5%: **MET** — actual 0.0%
+
+---
+
+## §6 Comparison to v5 historical baseline
+
+v5 κ baseline reference: Fleiss' κ=0.7458 on three-way Opus+GPT+Gemini ensemble.
+v6 κ(Opus, GPT) pairwise: **0.8480** — sanity check. If significantly different from v5 baseline range (~0.74-0.82 for a high-agreement pair), investigate.
+v6 conservative trio κ (Opus+GPT+MiniMax): **0.7878**.

benchmarks/calibration/v6-kappa-recal/kappa-v6-compute.py

@@ -0,0 +1,330 @@
+"""
+Manifest v6 Phase 1 — κ re-calibration computation
+===================================================
+
+Computes three pairwise Cohen's κ on 100-instance sample:
+  κ(Opus, GPT)       — should match v5 historical baseline ~0.74-0.82
+  κ(Opus, MiniMax)   — new measurement
+  κ(GPT, MiniMax)    — new measurement
+
+Conservative trio κ = min of the three.
+
+Also reports:
+  - Raw agreement % per pair
+  - Confusion matrix per pair
+  - Per-cell breakdown (no-context / oracle-context / full-context /
+    retrieval / agentic)
+  - MiniMax operational metrics: parse rate, latency p50/p95, routing
+    errors, token usage
+
+Writes:
+  kappa-v6-analysis.md — detailed matrix + per-cell breakdown
+  _summary-v6-kappa.json — machine-readable for halt ping
+"""
+from __future__ import annotations
+
+import json
+import statistics
+from collections import Counter
+from pathlib import Path
+
+OUT_DIR = Path("D:/Projects/waggle-os/benchmarks/calibration/v6-kappa-recal")
+SAMPLE_PATH = OUT_DIR / "kappa-sample-instances.jsonl"
+RESPONSES_PATH = OUT_DIR / "minimax-kappa-responses.jsonl"
+ANALYSIS_PATH = OUT_DIR / "kappa-v6-analysis.md"
+SUMMARY_JSON = OUT_DIR / "_summary-v6-kappa.json"
+
+
+def load_jsonl(path: Path) -> list[dict]:
+    out = []
+    with path.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                out.append(json.loads(line))
+    return out
+
+
+def cohen_kappa(pairs: list[tuple[str, str]]) -> tuple[float, dict]:
+    """Cohen's κ on 2-class (correct/incorrect) pairs. Returns (κ, detail)."""
+    if not pairs:
+        return (float("nan"), {"n": 0, "agree": 0, "po": 0.0, "pe": 0.0})
+    n = len(pairs)
+    agree = sum(1 for a, b in pairs if a == b)
+    po = agree / n
+    a_counts = {"correct": 0, "incorrect": 0}
+    b_counts = {"correct": 0, "incorrect": 0}
+    for a, b in pairs:
+        a_counts[a] = a_counts.get(a, 0) + 1
+        b_counts[b] = b_counts.get(b, 0) + 1
+    pe = sum(
+        (a_counts.get(v, 0) / n) * (b_counts.get(v, 0) / n)
+        for v in ("correct", "incorrect")
+    )
+    if pe >= 1.0:
+        return (1.0 if po == 1.0 else float("nan"),
+                {"n": n, "agree": agree, "po": po, "pe": pe,
+                 "a_counts": a_counts, "b_counts": b_counts})
+    kappa = (po - pe) / (1.0 - pe)
+    return (kappa, {"n": n, "agree": agree, "po": po, "pe": pe,
+                    "a_counts": a_counts, "b_counts": b_counts})
+
+
+def confusion_matrix(pairs: list[tuple[str, str]]) -> dict:
+    """2x2 confusion (rows = judge A, cols = judge B)."""
+    cm = {"correct_correct": 0, "correct_incorrect": 0,
+          "incorrect_correct": 0, "incorrect_incorrect": 0}
+    for a, b in pairs:
+        key = f"{a}_{b}"
+        cm[key] = cm.get(key, 0) + 1
+    return cm
+
+
+def classify_verdict(trio_kappa: float) -> str:
+    if trio_kappa != trio_kappa:  # NaN
+        return "INCONCLUSIVE"
+    if trio_kappa >= 0.70:
+        return "PASS"
+    if trio_kappa >= 0.60:
+        return "BORDERLINE"
+    return "FAIL"
+
+
+def fmt_k(x: float) -> str:
+    if x != x:
+        return "NaN"
+    return f"{x:.4f}"
+
+
+def pct_str(num: int, denom: int) -> str:
+    if denom == 0:
+        return "—"
+    return f"{num}/{denom} ({num * 100.0 / denom:.1f}%)"
+
+
+def main() -> int:
+    sample = load_jsonl(SAMPLE_PATH)
+    mm = load_jsonl(RESPONSES_PATH)
+
+    # Index MiniMax responses
+    mm_by_key = {(r["instance_id"], r["cell"]): r for r in mm}
+
+    # Build pair lists for 3 pairwise κ
+    pairs_og = []  # (Opus, GPT)
+    pairs_om = []  # (Opus, MiniMax)
+    pairs_gm = []  # (GPT, MiniMax)
+    per_cell = {"no-context": [], "oracle-context": [], "full-context": [],
+                "retrieval": [], "agentic": []}
+
+    mm_parse_ok = 0
+    mm_lat = []
+    mm_retries = 0
+    mm_routing_errors = 0
+    mm_prompt_tok = []
+    mm_comp_tok = []
+
+    for s in sample:
+        op_v = s.get("opus_verdict")
+        gp_v = s.get("gpt_verdict")
+        if op_v not in ("correct", "incorrect") or gp_v not in ("correct", "incorrect"):
+            continue
+        pairs_og.append((op_v, gp_v))
+        per_cell.setdefault(s["cell"], []).append(("og", op_v, gp_v))
+
+        key = (s["instance_id"], s["cell"])
+        m = mm_by_key.get(key)
+        if m is None:
+            continue
+        mm_v = m.get("parsed_verdict")
+        mm_lat.append(m.get("latency_ms") or 0)
+        mm_retries += m.get("retries") or 0
+        if m.get("http_status") != 200:
+            mm_routing_errors += 1
+        if m.get("prompt_tokens"):
+            mm_prompt_tok.append(m["prompt_tokens"])
+        if m.get("completion_tokens"):
+            mm_comp_tok.append(m["completion_tokens"])
+        if mm_v in ("correct", "incorrect"):
+            mm_parse_ok += 1
+            pairs_om.append((op_v, mm_v))
+            pairs_gm.append((gp_v, mm_v))
+            per_cell.setdefault(s["cell"], []).append(("om", op_v, mm_v))
+            per_cell.setdefault(s["cell"], []).append(("gm", gp_v, mm_v))
+
+    k_og, k_og_det = cohen_kappa(pairs_og)
+    k_om, k_om_det = cohen_kappa(pairs_om)
+    k_gm, k_gm_det = cohen_kappa(pairs_gm)
+    cm_og = confusion_matrix(pairs_og)
+    cm_om = confusion_matrix(pairs_om)
+    cm_gm = confusion_matrix(pairs_gm)
+
+    kappas = [k for k in (k_og, k_om, k_gm) if k == k]
+    k_trio = min(kappas) if kappas else float("nan")
+    verdict = classify_verdict(k_trio)
+
+    # Per-cell pairwise
+    per_cell_rows = []
+    cells_order = ["no-context", "oracle-context", "full-context", "retrieval", "agentic"]
+    for cell in cells_order:
+        triples = per_cell.get(cell, [])
+        pog = [(a, b) for t, a, b in triples if t == "og"]
+        pom = [(a, b) for t, a, b in triples if t == "om"]
+        pgm = [(a, b) for t, a, b in triples if t == "gm"]
+        k_c_og, _ = cohen_kappa(pog) if pog else (float("nan"), {})
+        k_c_om, _ = cohen_kappa(pom) if pom else (float("nan"), {})
+        k_c_gm, _ = cohen_kappa(pgm) if pgm else (float("nan"), {})
+        n_cell = len(pog)
+        mm_cell_parsed = len(pom)
+        per_cell_rows.append({
+            "cell": cell,
+            "n": n_cell,
+            "mm_parsed": mm_cell_parsed,
+            "k_og": k_c_og,
+            "k_om": k_c_om,
+            "k_gm": k_c_gm,
+        })
+
+    # Operational metrics
+    lat_p50 = int(statistics.median(mm_lat)) if mm_lat else 0
+    lat_p95 = int(sorted(mm_lat)[max(0, int(len(mm_lat) * 0.95) - 1)]) if mm_lat else 0
+    prompt_tok_total = sum(mm_prompt_tok)
+    comp_tok_total = sum(mm_comp_tok)
+    # OR MiniMax M2.7 pricing: $0.30/M prompt, $1.20/M completion
+    cost_actual = round(
+        (prompt_tok_total / 1_000_000) * 0.30 + (comp_tok_total / 1_000_000) * 1.20, 4
+    )
+
+    # ── Write kappa-v6-analysis.md ─────────────────────────────────────
+
+    lines = []
+    lines.append("# Manifest v6 κ Re-Calibration Analysis")
+    lines.append("")
+    lines.append("**Date:** 2026-04-24  **Parent:** `38a830e` (v6 Phase 1 Commit 2)  **v6 anchor:** `60d061e`")
+    lines.append("")
+    lines.append(f"**Sample:** {len(sample)} instances from `benchmarks/results/locomo-mini-n20-retry-2026-04-24T00-02-12Z.jsonl` (authoritative v5 κ set; zero new Opus/GPT calls).")
+    lines.append("")
+    lines.append(f"**MiniMax verdicts:** {len(mm)} calls via OpenRouter `minimax/minimax-m2.7` (v6 alias: `minimax-m27-via-openrouter`); direct HTTP probe (LiteLLM proxy not in loop — isolates model behavior from middleware).")
+    lines.append("")
+    lines.append(f"**Prompt:** verbatim `failure-mode-judge.ts:245-258` (same as §1.3g / §1.3h / §1.3h-C).")
+    lines.append(f"**Parameters:** `temperature=0.0`, `max_tokens=4096`.")
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+    lines.append("## §1 Three pairwise Cohen's κ")
+    lines.append("")
+    lines.append("| Pair | n | Agree | Raw % | κ |")
+    lines.append("|------|---|-------|-------|-----|")
+    lines.append(f"| Opus vs GPT | {k_og_det['n']} | {k_og_det['agree']} | {k_og_det['po']*100:.2f}% | **{fmt_k(k_og)}** |")
+    lines.append(f"| Opus vs MiniMax | {k_om_det['n']} | {k_om_det['agree']} | {k_om_det['po']*100:.2f}% | **{fmt_k(k_om)}** |")
+    lines.append(f"| GPT vs MiniMax | {k_gm_det['n']} | {k_gm_det['agree']} | {k_gm_det['po']*100:.2f}% | **{fmt_k(k_gm)}** |")
+    lines.append("")
+    lines.append(f"**Conservative trio κ = min = {fmt_k(k_trio)}**")
+    lines.append("")
+    lines.append(f"## §2 Verdict: **{verdict}**")
+    lines.append("")
+    lines.append("Per v6 §5.4 gate criteria:")
+    lines.append("- `κ_trio ≥ 0.70` → PASS, halt with PM-RATIFY-V6-KAPPA")
+    lines.append("- `0.60 ≤ κ_trio < 0.70` → BORDERLINE, halt with PM adjudication")
+    lines.append("- `κ_trio < 0.60` → FAIL, halt with swap-path-re-evaluation")
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+    lines.append("## §3 Confusion matrices")
+    lines.append("")
+    lines.append("### Opus vs GPT")
+    lines.append("")
+    lines.append("| | GPT=correct | GPT=incorrect |")
+    lines.append("|---|---|---|")
+    lines.append(f"| **Opus=correct** | {cm_og.get('correct_correct', 0)} | {cm_og.get('correct_incorrect', 0)} |")
+    lines.append(f"| **Opus=incorrect** | {cm_og.get('incorrect_correct', 0)} | {cm_og.get('incorrect_incorrect', 0)} |")
+    lines.append("")
+    lines.append("### Opus vs MiniMax")
+    lines.append("")
+    lines.append("| | MiniMax=correct | MiniMax=incorrect |")
+    lines.append("|---|---|---|")
+    lines.append(f"| **Opus=correct** | {cm_om.get('correct_correct', 0)} | {cm_om.get('correct_incorrect', 0)} |")
+    lines.append(f"| **Opus=incorrect** | {cm_om.get('incorrect_correct', 0)} | {cm_om.get('incorrect_incorrect', 0)} |")
+    lines.append("")
+    lines.append("### GPT vs MiniMax")
+    lines.append("")
+    lines.append("| | MiniMax=correct | MiniMax=incorrect |")
+    lines.append("|---|---|---|")
+    lines.append(f"| **GPT=correct** | {cm_gm.get('correct_correct', 0)} | {cm_gm.get('correct_incorrect', 0)} |")
+    lines.append(f"| **GPT=incorrect** | {cm_gm.get('incorrect_correct', 0)} | {cm_gm.get('incorrect_incorrect', 0)} |")
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+    lines.append("## §4 Per-cell κ breakdown (n=20 per cell)")
+    lines.append("")
+    lines.append("| Cell | n | MiniMax parsed | κ(Opus,GPT) | κ(Opus,MiniMax) | κ(GPT,MiniMax) |")
+    lines.append("|------|---|-----------------|----------------|-------------------|------------------|")
+    for row in per_cell_rows:
+        lines.append(
+            f"| {row['cell']} | {row['n']} | {row['mm_parsed']} "
+            f"| {fmt_k(row['k_og'])} | {fmt_k(row['k_om'])} | {fmt_k(row['k_gm'])} |"
+        )
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+    lines.append("## §5 MiniMax operational metrics")
+    lines.append("")
+    lines.append(f"- Calls: {len(mm)} total, parsed OK: **{pct_str(mm_parse_ok, len(mm))}**")
+    lines.append(f"- Routing errors (non-200 HTTP): **{mm_routing_errors}/{len(mm)}** ({mm_routing_errors*100/len(mm):.1f}%)")
+    lines.append(f"- Total retries: {mm_retries}")
+    lines.append(f"- Latency p50: **{lat_p50/1000:.1f} s**  |  p95: **{lat_p95/1000:.1f} s**")
+    lines.append(f"- Token usage: prompt = {prompt_tok_total:,}, completion = {comp_tok_total:,}")
+    lines.append(f"- Cost actual (OR MiniMax pricing $0.30/$1.20 per 1M): **~${cost_actual}**")
+    lines.append("")
+    lines.append(f"Per brief §3.5 operational hedge thresholds:")
+    lines.append(f"- parse ≥95/100 target: **{'MET' if mm_parse_ok >= 95 else 'MISS (below target)'}** — actual {mm_parse_ok}/100")
+    lines.append(f"- parse ≥90/100 halt: **{'MET' if mm_parse_ok >= 90 else 'FAIL (halt)'}** — actual {mm_parse_ok}/100")
+    lines.append(f"- latency p50 ≤25s: **{'MET' if lat_p50 <= 25000 else 'MISS'}** — actual {lat_p50/1000:.1f}s")
+    lines.append(f"- OR routing errors <5%: **{'MET' if mm_routing_errors < 5 else 'FLAG'}** — actual {mm_routing_errors/len(mm)*100:.1f}%")
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+    lines.append(f"## §6 Comparison to v5 historical baseline")
+    lines.append("")
+    lines.append(f"v5 κ baseline reference: Fleiss' κ=0.7458 on three-way Opus+GPT+Gemini ensemble.")
+    lines.append(f"v6 κ(Opus, GPT) pairwise: **{fmt_k(k_og)}** — sanity check. If significantly different from v5 baseline range (~0.74-0.82 for a high-agreement pair), investigate.")
+    lines.append(f"v6 conservative trio κ (Opus+GPT+MiniMax): **{fmt_k(k_trio)}**.")
+
+    ANALYSIS_PATH.write_text("\n".join(lines), encoding="utf-8")
+    print(f"Wrote {ANALYSIS_PATH}")
+
+    # Machine-readable summary
+    summary = {
+        "verdict": verdict,
+        "k_opus_gpt": k_og,
+        "k_opus_minimax": k_om,
+        "k_gpt_minimax": k_gm,
+        "k_conservative_trio": k_trio,
+        "minimax_parse_success": mm_parse_ok,
+        "minimax_n_total": len(mm),
+        "minimax_lat_p50_ms": lat_p50,
+        "minimax_lat_p95_ms": lat_p95,
+        "minimax_routing_errors": mm_routing_errors,
+        "minimax_retries_total": mm_retries,
+        "minimax_prompt_tokens_total": prompt_tok_total,
+        "minimax_completion_tokens_total": comp_tok_total,
+        "cost_actual_usd": cost_actual,
+        "per_cell": per_cell_rows,
+        "confusion_opus_gpt": cm_og,
+        "confusion_opus_minimax": cm_om,
+        "confusion_gpt_minimax": cm_gm,
+    }
+    SUMMARY_JSON.write_text(json.dumps(summary, indent=2, default=str), encoding="utf-8")
+    print(f"Wrote {SUMMARY_JSON}")
+    print(f"\nVerdict: {verdict}")
+    print(f"k(Opus, GPT)     = {fmt_k(k_og)}")
+    print(f"k(Opus, MiniMax) = {fmt_k(k_om)}")
+    print(f"k(GPT, MiniMax)  = {fmt_k(k_gm)}")
+    print(f"k_trio (min)     = {fmt_k(k_trio)}")
+    print(f"MiniMax parse    = {mm_parse_ok}/100")
+    print(f"Cost             = ${cost_actual}")
+    return 0
+
+
+if __name__ == "__main__":
+    import sys
+    sys.exit(main())

benchmarks/calibration/v6-kappa-recal/minimax-kappa-probe.py

@@ -0,0 +1,367 @@
+"""
+Manifest v6 Phase 1 Commit 3 — MiniMax κ re-calibration probe
+==============================================================
+
+Executes 100 MiniMax M2.7 verdicts (via OpenRouter) on the authoritative
+v5 κ calibration set:
+  benchmarks/results/locomo-mini-n20-retry-2026-04-24T00-02-12Z.jsonl
+
+Reuses existing Opus + GPT verdicts from judge_ensemble field (zero new
+calls for those). Reuses LoCoMo fixtures for question/ground_truth/
+context lookup by instance_id (same pattern as §1.3g/h probes).
+
+Routing: same OR endpoint as §1.3h (direct HTTP, bypasses LiteLLM proxy
+for probe speed). The v6 LiteLLM alias wiring (minimax-m27-via-openrouter)
+will be validated end-to-end in Phase 2 N=400 execution; κ re-cal
+isolates model behavior from middleware.
+
+Prompt: verbatim from failure-mode-judge.ts:245-258 (identical to §1.3g
+and §1.3h probes).
+
+Operational hedge per brief §3.5:
+  - Log parse rate (target ≥95/100, halt <90/100)
+  - Log latency p50 (target ≤25s) + p95
+  - Log OR routing errors (>5% rate raises PM flag pre-κ compute)
+
+Scope guards:
+  - Parent HEAD = 38a830e (v6 Phase 1 Commit 2 anchor)
+  - v6 manifest anchor = 60d061e (Commit 1)
+  - §11 frozen paths except litellm-config.yaml (already amended in Commit 2)
+  - No runner/judge-runner/failure-mode-judge edits
+
+Budget: ~$2.50 expected (100 calls × $0.02 avg per §1.3h MiniMax pricing).
+Cap: $30 Phase 1 total.
+
+Usage:
+  python minimax-kappa-probe.py
+"""
+from __future__ import annotations
+
+import json
+import sys
+import time
+import urllib.error
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+
+try:
+    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+    sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+except Exception:
+    pass
+
+# ── Paths ───────────────────────────────────────────────────────────────
+
+OUT_DIR = Path("D:/Projects/waggle-os/benchmarks/calibration/v6-kappa-recal")
+CAL_SRC = Path("D:/Projects/waggle-os/benchmarks/results/locomo-mini-n20-retry-2026-04-24T00-02-12Z.jsonl")
+LOCOMO = Path("D:/Projects/waggle-os/benchmarks/data/locomo/locomo-1540.jsonl")
+RESPONSES_PATH = OUT_DIR / "minimax-kappa-responses.jsonl"
+SAMPLE_PATH = OUT_DIR / "kappa-sample-instances.jsonl"
+
+# ── Verbatim judge prompt (same as §1.3g and §1.3h) ─────────────────────
+
+JUDGE_PROMPT_TEMPLATE = "\n".join([
+    "You are evaluating whether an LLM's answer is correct against ground truth.",
+    "",
+    "## Question",
+    "{question}",
+    "",
+    "## Ground-truth answer",
+    "{ground_truth}",
+    "",
+    "## Ground-truth supporting context (excerpt shown to the model)",
+    "{context}",
+    "",
+    "## Model's answer",
+    "{model_answer}",
+    "",
+    "## Your task",
+    "",
+    "Step 1: Determine if the model's answer is correct.",
+    "- \"correct\" means the model's answer contains all required facts from ground truth, with no additional incorrect claims.",
+    "- Minor phrasing differences, synonyms, or alternative but equivalent formulations are acceptable.",
+    "- Extra detail is acceptable ONLY if it is factually correct.",
+    "",
+    "Step 2: If incorrect, assign exactly one failure mode using this decision tree:",
+    "",
+    "1. Does the model explicitly refuse or say it does not know? -> F1 (ABSTAIN)",
+    "2. Does the model answer a DIFFERENT question than was asked (coherent but off-topic)? -> F5 (OFF-TOPIC)",
+    "3. Does the model rely on entities, names, dates, or claims that do NOT appear in the ground-truth context (fabrication)? -> F4 (HALLUCINATED)",
+    "4. Does the model correctly state SOME required facts but miss others, without stating any incorrect facts? -> F2 (PARTIAL)",
+    "5. Otherwise (model states facts derived from the context but gets them wrong): -> F3 (INCORRECT)",
+    "",
+    "Step 3: Return JSON only, no prose, in this exact schema:",
+    "",
+    "{{",
+    "  \"verdict\": \"correct\" | \"incorrect\",",
+    "  \"failure_mode\": null | \"F1\" | \"F2\" | \"F3\" | \"F4\" | \"F5\",",
+    "  \"rationale\": \"one sentence explaining the verdict\"",
+    "}}",
+    "",
+    "If verdict is \"correct\", failure_mode MUST be null.",
+    "If verdict is \"incorrect\", failure_mode MUST be one of F1-F5.",
+])
+
+
+def ts() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def logmsg(msg: str) -> None:
+    print(f"{ts()} {msg}", flush=True)
+
+
+def load_env() -> dict[str, str]:
+    env_path = Path("D:/Projects/waggle-os/.env")
+    out: dict[str, str] = {}
+    for line in env_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        k, _, v = line.partition("=")
+        out[k.strip()] = v.strip().strip('"').strip("'")
+    return out
+
+
+import re
+
+
+def extract_json_body(raw: str) -> dict | None:
+    if not raw:
+        return None
+    trimmed = raw.strip()
+    if trimmed.startswith("```"):
+        m = re.match(r"^```(?:json)?\s*\n?(.*?)```\s*$", trimmed, re.DOTALL)
+        if m:
+            trimmed = m.group(1).strip()
+    try:
+        return json.loads(trimmed)
+    except Exception:
+        pass
+    first = trimmed.find("{")
+    last = trimmed.rfind("}")
+    if first != -1 and last != -1 and last > first:
+        try:
+            return json.loads(trimmed[first:last + 1])
+        except Exception:
+            return None
+    return None
+
+
+def parse_verdict(raw: str) -> tuple[str | None, str | None, str | None]:
+    body = extract_json_body(raw)
+    if not isinstance(body, dict):
+        return (None, None, None)
+    v = body.get("verdict")
+    fm = body.get("failure_mode")
+    rat = body.get("rationale")
+    if v not in ("correct", "incorrect"):
+        return (None, None, None)
+    if fm is not None and fm not in ("F1", "F2", "F3", "F4", "F5"):
+        fm = None
+    return (v, fm, rat if isinstance(rat, str) else None)
+
+
+def http_post_json(url: str, headers: dict, body: dict, timeout_s: int = 60) -> tuple[int, dict | str]:
+    req = urllib.request.Request(
+        url, data=json.dumps(body).encode("utf-8"), method="POST",
+        headers={"Content-Type": "application/json", **headers},
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=timeout_s) as resp:
+            raw = resp.read().decode("utf-8", errors="replace")
+            try:
+                return resp.status, json.loads(raw)
+            except Exception:
+                return resp.status, raw
+    except urllib.error.HTTPError as e:
+        try:
+            return e.code, e.read().decode("utf-8", errors="replace")[:2000]
+        except Exception:
+            return e.code, ""
+    except Exception as e:
+        return 0, f"{type(e).__name__}: {e}"
+
+
+def call_minimax_via_openrouter(prompt: str, or_key: str, max_attempts: int = 3) -> dict:
+    url = "https://openrouter.ai/api/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {or_key}"}
+    body = {
+        "model": "minimax/minimax-m2.7",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.0,
+        "max_tokens": 4096,
+    }
+    started = time.time()
+    retries = 0
+    last_err = None
+    for attempt in range(max_attempts):
+        status, resp = http_post_json(url, headers, body, timeout_s=90)
+        if status == 200 and isinstance(resp, dict):
+            choices = resp.get("choices") or []
+            if choices:
+                msg = choices[0].get("message") or {}
+                content = msg.get("content") or msg.get("reasoning_content") or ""
+                usage = resp.get("usage", {})
+                return {
+                    "raw_text": content,
+                    "status": 200,
+                    "error": None,
+                    "retries": retries,
+                    "latency_ms": int((time.time() - started) * 1000),
+                    "prompt_tokens": usage.get("prompt_tokens"),
+                    "completion_tokens": usage.get("completion_tokens"),
+                }
+        last_err = f"status={status} resp={str(resp)[:400]}"
+        retries += 1
+        if attempt < max_attempts - 1:
+            time.sleep(2 ** attempt)
+    return {
+        "raw_text": "",
+        "status": 0,
+        "error": last_err,
+        "retries": retries,
+        "latency_ms": int((time.time() - started) * 1000),
+        "prompt_tokens": None,
+        "completion_tokens": None,
+    }
+
+
+def build_sample() -> list[dict]:
+    """Load all 100 κ calibration instances with enriched LoCoMo fixture."""
+    rows = []
+    with CAL_SRC.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            rows.append(json.loads(line))
+
+    locomo_by_id = {}
+    with LOCOMO.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            r = json.loads(line)
+            locomo_by_id[r.get("instance_id")] = r
+
+    enriched = []
+    for r in rows:
+        iid = r.get("instance_id")
+        ensemble = r.get("judge_ensemble") or []
+        opus = next((j for j in ensemble if "opus" in j.get("model", "").lower()), None)
+        gpt = next((j for j in ensemble if "gpt" in j.get("model", "").lower()), None)
+        gemini = next((j for j in ensemble if "gemini" in j.get("model", "").lower()), None)
+        loc = locomo_by_id.get(iid) or {}
+        gt = ((loc.get("expected") or [loc.get("gold_answer", "")])[0]) if loc else ""
+        enriched.append({
+            "instance_id": iid,
+            "cell": r.get("cell"),
+            "question": loc.get("question") or "",
+            "ground_truth": gt,
+            "context": loc.get("context") or "",
+            "model_answer": r.get("model_answer") or "",
+            "opus_verdict": (opus or {}).get("verdict"),
+            "opus_failure_mode": (opus or {}).get("failure_mode"),
+            "gpt_verdict": (gpt or {}).get("verdict"),
+            "gpt_failure_mode": (gpt or {}).get("failure_mode"),
+            "gemini_verdict_v5": (gemini or {}).get("verdict"),
+            "gemini_failure_mode_v5": (gemini or {}).get("failure_mode"),
+        })
+    return enriched
+
+
+def main() -> int:
+    logmsg("[v6-kappa] MiniMax 100-instance re-calibration START")
+    env = load_env()
+    or_key = env.get("OPENROUTER_API_KEY", "").strip()
+    if not or_key:
+        logmsg("[v6-kappa] FATAL OPENROUTER_API_KEY missing")
+        return 2
+
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    sample = build_sample()
+    logmsg(f"[v6-kappa] loaded {len(sample)} κ instances from {CAL_SRC.name}")
+
+    # Persist enriched sample for kappa compute
+    with SAMPLE_PATH.open("w", encoding="utf-8") as f:
+        for s in sample:
+            f.write(json.dumps(s, ensure_ascii=False) + "\n")
+    logmsg(f"[v6-kappa] wrote enriched sample to {SAMPLE_PATH.name}")
+
+    # Execute 100 MiniMax calls (sequential, with per-call retries)
+    rows = []
+    started_run = time.time()
+    routing_errors = 0
+    for i, s in enumerate(sample):
+        prompt = JUDGE_PROMPT_TEMPLATE.format(
+            question=s["question"],
+            ground_truth=s["ground_truth"],
+            context=s["context"],
+            model_answer=s["model_answer"],
+        )
+        resp = call_minimax_via_openrouter(prompt, or_key)
+        verdict, fm, rat = parse_verdict(resp["raw_text"])
+        if resp["status"] != 200:
+            routing_errors += 1
+        rows.append({
+            "instance_id": s["instance_id"],
+            "cell": s["cell"],
+            "provider": "minimax",
+            "model_id": "minimax/minimax-m2.7",
+            "routing": "openrouter_direct_http",
+            "litellm_alias_registered": "minimax-m27-via-openrouter",
+            "http_status": resp["status"],
+            "error": resp.get("error"),
+            "retries": resp["retries"],
+            "latency_ms": resp["latency_ms"],
+            "prompt_tokens": resp.get("prompt_tokens"),
+            "completion_tokens": resp.get("completion_tokens"),
+            "raw_text": resp["raw_text"],
+            "parsed_verdict": verdict,
+            "parsed_failure_mode": fm,
+            "parsed_rationale": rat,
+            "opus_verdict_ref": s["opus_verdict"],
+            "gpt_verdict_ref": s["gpt_verdict"],
+        })
+        if (i + 1) % 10 == 0 or i == 0:
+            elapsed = time.time() - started_run
+            parsed_so_far = sum(1 for r in rows if r.get("parsed_verdict") is not None)
+            logmsg(
+                f"[v6-kappa] {i+1:>3}/{len(sample)} {s['instance_id']:30} cell={s['cell']:14} "
+                f"status={resp['status']} verdict={verdict} parse_ok={parsed_so_far}/{i+1} "
+                f"routing_err={routing_errors} elapsed={elapsed:.0f}s"
+            )
+
+    # Halt-before-compute check if parse < 90/100
+    parsed = sum(1 for r in rows if r.get("parsed_verdict") is not None)
+    logmsg(f"[v6-kappa] completed {len(rows)} calls; parsed={parsed}/100; routing_errors={routing_errors}")
+
+    # Write responses regardless of halt status
+    with RESPONSES_PATH.open("w", encoding="utf-8") as f:
+        for r in rows:
+            f.write(json.dumps(r, ensure_ascii=False) + "\n")
+    logmsg(f"[v6-kappa] wrote {RESPONSES_PATH.name}")
+
+    if parsed < 90:
+        logmsg(f"[v6-kappa] HALT_BEFORE_COMPUTE: parse rate {parsed}/100 < 90 threshold (per brief §3.5)")
+        return 3
+    if routing_errors > 5:
+        logmsg(f"[v6-kappa] ROUTING_ERROR_RATE_FLAG: {routing_errors}/100 > 5% threshold (per brief §3.5)")
+        return 4
+
+    logmsg("[v6-kappa] probe step complete; κ computation handled by kappa-v6-compute.py")
+    logmsg("[v6-kappa] END")
+    return 0
+
+
+if __name__ == "__main__":
+    import traceback
+    try:
+        sys.exit(main())
+    except Exception as e:
+        logmsg(f"[FATAL] {type(e).__name__}: {e}")
+        logmsg(traceback.format_exc()[:2000])
+        sys.exit(99)

benchmarks/calibration/v6-kappa-recal/minimax-kappa-responses.jsonl

@@ -0,0 +1,100 @@
+{"instance_id": "locomo_conv-50_q086", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 17733, "prompt_tokens": 505, "completion_tokens": 571, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model answered 'Surfing' when the ground truth clearly indicates Calvin is planning to go skiing (evidenced by the snowy peak description and image of a person on skis).\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model answered 'Surfing' when the ground truth clearly indicates Calvin is planning to go skiing (evidenced by the snowy peak description and image of a person on skis).", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-26_q059", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 16903, "prompt_tokens": 593, "completion_tokens": 623, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model refused to answer the question by stating it cannot be determined without context, even though the ground-truth context was provided and contains sufficient information to conclude that Caroline is somewhat, but not extremely religious based on her making art for a church while not exhibiting strongly religious behavior.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model refused to answer the question by stating it cannot be determined without context, even though the ground-truth context was provided and contains sufficient information to conclude that Caroline is somewhat, but not extremely religious based on her making art for a church while not exhibiting strongly religious behavior.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q000", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15866, "prompt_tokens": 450, "completion_tokens": 610, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model answered 1954, which is a date that does not appear in the ground-truth context and cannot be derived from the provided information (session date of March 2023 and '3 years' tenure would imply ~2020, not 1954), indicating the model fabricated an answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model answered 1954, which is a date that does not appear in the ground-truth context and cannot be derived from the provided information (session date of March 2023 and '3 years' tenure would imply ~2020, not 1954), indicating the model fabricated an answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-41_q080", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 11086, "prompt_tokens": 521, "completion_tokens": 554, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model provided 'A wind chime' which does not appear anywhere in the ground-truth context; the correct answer is 'painting of a castle on a hill' as explicitly stated by Maria in the supporting context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model provided 'A wind chime' which does not appear anywhere in the ground-truth context; the correct answer is 'painting of a castle on a hill' as explicitly stated by Maria in the supporting context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-42_q001", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 27267, "prompt_tokens": 742, "completion_tokens": 942, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model's answer 'Reading' does not appear in the ground truth answer, and while reading appears in the context as only Joanna's interest (not Nate's), it is not a shared interest; the actual shared interests are watching movies and making desserts.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model's answer 'Reading' does not appear in the ground truth answer, and while reading appears in the context as only Joanna's interest (not Nate's), it is not a shared interest; the actual shared interests are watching movies and making desserts.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 21117, "prompt_tokens": 464, "completion_tokens": 551, "raw_text": "{\"verdict\": \"incorrect\", \"failure_mode\": \"F3\", \"rationale\": \"The model states John felt 'relieved', but the ground truth explicitly says John felt it was a 'huge success' - these are different emotional states.\"}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model states John felt 'relieved', but the ground truth explicitly says John felt it was a 'huge success' - these are different emotional states.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5779, "prompt_tokens": 553, "completion_tokens": 198, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly states 'Unknown', refusing to provide an answer, which is a refusal to answer based on available context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly states 'Unknown', refusing to provide an answer, which is a refusal to answer based on available context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q020", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 8704, "prompt_tokens": 710, "completion_tokens": 493, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model mentions games (Animal Crossing and Minecraft) that do not appear anywhere in the ground-truth context, which consists only of Detroit, Walking Dead, Battlefield 1, It Takes Two, and Overcooked 2.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model mentions games (Animal Crossing and Minecraft) that do not appear anywhere in the ground-truth context, which consists only of Detroit, Walking Dead, Battlefield 1, It Takes Two, and Overcooked 2.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q055", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9606, "prompt_tokens": 510, "completion_tokens": 625, "raw_text": "```json\n{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Seraphim' matches the ground-truth answer exactly, and while the context excerpts don't explicitly name the pets, the model's answer is confirmed correct by the ground truth.\"\n}\n```", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Seraphim' matches the ground-truth answer exactly, and while the context excerpts don't explicitly name the pets, the model's answer is confirmed correct by the ground truth.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-26_q128", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 14317, "prompt_tokens": 493, "completion_tokens": 555, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model answered 'A butterfly,' which is an entity that does not appear anywhere in the ground-truth context; the correct answer is 'a rainbow sidewalk.'\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model answered 'A butterfly,' which is an entity that does not appear anywhere in the ground-truth context; the correct answer is 'a rainbow sidewalk.'", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q010", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 23529, "prompt_tokens": 504, "completion_tokens": 606, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model provided the date 'October 2019' which does not appear in the ground-truth context (which indicates the flooding occurred around May 9, 2023), constituting a fabrication.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model provided the date 'October 2019' which does not appear in the ground-truth context (which indicates the flooding occurred around May 9, 2023), constituting a fabrication.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q078", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 16510, "prompt_tokens": 475, "completion_tokens": 604, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model answered 'Home,' which does not appear in the ground-truth context and is unrelated to the stated purpose of the necklace as a reminder of why Calvin keeps hustling as a musician.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model answered 'Home,' which does not appear in the ground-truth context and is unrelated to the stated purpose of the necklace as a reminder of why Calvin keeps hustling as a musician.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-47_q092", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 18246, "prompt_tokens": 487, "completion_tokens": 539, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model states 'a database' which is factually incorrect; the ground-truth context clearly states John created an application on smartphones, not a database.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model states 'a database' which is factually incorrect; the ground-truth context clearly states John created an application on smartphones, not a database.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q057", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10809, "prompt_tokens": 503, "completion_tokens": 374, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly states one relevant fact about focusing on customers (related to building relationships) but omits the other two required pieces of advice: creating a strong brand image and staying positive.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly states one relevant fact about focusing on customers (related to building relationships) but omits the other two required pieces of advice: creating a strong brand image and staying positive.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q029", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5371, "prompt_tokens": 565, "completion_tokens": 288, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly stated 'Unknown' when the answer (Paris and Rome) could be directly extracted from the provided supporting context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly stated 'Unknown' when the answer (Paris and Rome) could be directly extracted from the provided supporting context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q066", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 6208, "prompt_tokens": 492, "completion_tokens": 194, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'A small dog' is semantically equivalent to the ground truth 'smaller dog' and correctly captures Andrew's preference for his apartment living space.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'A small dog' is semantically equivalent to the ground truth 'smaller dog' and correctly captures Andrew's preference for his apartment living space.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q002", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4165, "prompt_tokens": 739, "completion_tokens": 332, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model only mentions board games but misses the other three indoor activities (volunteering at pet shelter, wine tasting, and growing flowers) listed in the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model only mentions board games but misses the other three indoor activities (volunteering at pet shelter, wine tasting, and growing flowers) listed in the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-47_q046", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7787, "prompt_tokens": 460, "completion_tokens": 584, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model states they 'Never' met, but the context indicates John and James planned to meet the next day (August 27, 2022) at McGee's Pub, making the model's answer factually incorrect.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model states they 'Never' met, but the context indicates John and James planned to meet the next day (August 27, 2022) at McGee's Pub, making the model's answer factually incorrect.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q076", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 24634, "prompt_tokens": 476, "completion_tokens": 718, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model claims the picture was 'a selfie' which does not appear in the ground-truth context; the context only states the picture is from MinaLima, a prop creator for the Harry Potter films, not a selfie.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model claims the picture was 'a selfie' which does not appear in the ground-truth context; the context only states the picture is from MinaLima, a prop creator for the Harry Potter films, not a selfie.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-41_q065", "cell": "no-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 8845, "prompt_tokens": 458, "completion_tokens": 627, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly states it does not know which John is being referred to, effectively refusing to answer the question despite the ground-truth context clearly identifying a John who states his main focuses are improving education and infrastructure.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly states it does not know which John is being referred to, effectively refusing to answer the question despite the ground-truth context clearly identifying a John who states his main focuses are improving education and infrastructure.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q086", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5159, "prompt_tokens": 503, "completion_tokens": 267, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Skiing' matches the ground truth exactly and is supported by the context, which mentions Calvin's to-do list after the tour and includes an image of skiing on a snowy mountain.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Skiing' matches the ground truth exactly and is supported by the context, which mentions Calvin's to-do list after the tour and includes an image of skiing on a snowy mountain.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-26_q059", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 31426, "prompt_tokens": 569, "completion_tokens": 1075, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model answers 'Yes' which is partially correct (Caroline has some religious connection via her church artwork) but fails to capture the nuance that ground truth specifies: she is 'somewhat, but not extremely religious.'\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model answers 'Yes' which is partially correct (Caroline has some religious connection via her church artwork) but fails to capture the nuance that ground truth specifies: she is 'somewhat, but not extremely religious.'", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q000", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7232, "prompt_tokens": 470, "completion_tokens": 291, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly answered 2020, which matches the ground truth and is correctly derived from the context stating Audrey had her three dogs (Pepper, Precious, and Panda) for 3 years as of March 2023.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly answered 2020, which matches the ground truth and is correctly derived from the context stating Audrey had her three dogs (Pepper, Precious, and Panda) for 3 years as of March 2023.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-41_q080", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 6440, "prompt_tokens": 517, "completion_tokens": 559, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies that Maria made a painting, but it omits the specific detail that the painting depicts a castle on a hill, which is a required fact from the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that Maria made a painting, but it omits the specific detail that the painting depicts a castle on a hill, which is a required fact from the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-42_q001", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4720, "prompt_tokens": 762, "completion_tokens": 254, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified movies as a shared interest but failed to include making desserts, which is also mentioned in the ground-truth answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified movies as a shared interest but failed to include making desserts, which is also mentioned in the ground-truth answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 11079, "prompt_tokens": 480, "completion_tokens": 396, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Great' is a synonym for 'a huge success' and correctly captures John's positive feeling about jogging without pain.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Great' is a synonym for 'a huge success' and correctly captures John's positive feeling about jogging without pain.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 14900, "prompt_tokens": 571, "completion_tokens": 540, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'To relax' is a correct and equivalent formulation of the ground truth 'because it relaxes and calms him,' as 'relax' and 'calms' are synonymous concepts that capture the same essential fact about why Dave visits parks.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'To relax' is a correct and equivalent formulation of the ground truth 'because it relaxes and calms him,' as 'relax' and 'calms' are synonymous concepts that capture the same essential fact about why Dave visits parks.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q020", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 16522, "prompt_tokens": 719, "completion_tokens": 265, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified three games (Detroit, It Takes Two, Overcooked 2) but missed two others (Walking Dead, Battlefield 1) that appear in the ground truth.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified three games (Detroit, It Takes Two, Overcooked 2) but missed two others (Walking Dead, Battlefield 1) that appear in the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q055", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 44330, "prompt_tokens": 509, "completion_tokens": 976, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model answered Susie, but the ground truth states Seraphim was adopted more recently, indicating the model derived incorrect facts from the context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model answered Susie, but the ground truth states Seraphim was adopted more recently, indicating the model derived incorrect facts from the context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-26_q128", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 3221, "prompt_tokens": 491, "completion_tokens": 216, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'A rainbow sidewalk' exactly matches the ground-truth answer 'a rainbow sidewalk' with only a minor capitalization difference, making it correct.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'A rainbow sidewalk' exactly matches the ground-truth answer 'a rainbow sidewalk' with only a minor capitalization difference, making it correct.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q010", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 13441, "prompt_tokens": 485, "completion_tokens": 336, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Last week' is equivalent to the ground truth 'a week before 16 May, 2023' since the conversation occurred on 16 May 2023, making minor phrasing differences acceptable.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Last week' is equivalent to the ground truth 'a week before 16 May, 2023' since the conversation occurred on 16 May 2023, making minor phrasing differences acceptable.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q078", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9610, "prompt_tokens": 482, "completion_tokens": 303, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer matches the ground truth exactly, capturing the fact that the necklace was meant to remind Calvin of why he keeps hustling as a musician, with only a minor capitalization difference that doesn't affect meaning.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer matches the ground truth exactly, capturing the fact that the necklace was meant to remind Calvin of why he keeps hustling as a musician, with only a minor capitalization difference that doesn't affect meaning.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-47_q092", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9489, "prompt_tokens": 487, "completion_tokens": 422, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model's answer correctly identifies that John created an application but omits the specific detail that it was on smartphones, which is a required fact from the ground truth.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer correctly identifies that John created an application but omits the specific detail that it was on smartphones, which is a required fact from the ground truth.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q057", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 71668, "prompt_tokens": 498, "completion_tokens": 2221, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly states 'Not mentioned' to avoid providing an answer, even though the advice content (brand identity, building relationships, staying positive) is present in the context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly states 'Not mentioned' to avoid providing an answer, even though the advice content (brand identity, building relationships, staying positive) is present in the context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q029", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 11020, "prompt_tokens": 550, "completion_tokens": 251, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly identified both cities (Paris and Rome) that Jon visited according to the ground-truth context, with no additional incorrect claims.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly identified both cities (Paris and Rome) that Jon visited according to the ground-truth context, with no additional incorrect claims.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q066", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5882, "prompt_tokens": 512, "completion_tokens": 214, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'A smaller dog' exactly matches the ground-truth answer 'smaller dog' and is fully supported by the context where Andrew states that for someone living in an apartment, 'a smaller dog would be best.'\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'A smaller dog' exactly matches the ground-truth answer 'smaller dog' and is fully supported by the context where Andrew states that for someone living in an apartment, 'a smaller dog would be best.'", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q002", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9991, "prompt_tokens": 739, "completion_tokens": 412, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model only mentions board games but omits three other activities (volunteering at pet shelter, wine tasting, and growing flowers) that Andrew pursued with his girlfriend according to the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model only mentions board games but omits three other activities (volunteering at pet shelter, wine tasting, and growing flowers) that Andrew pursued with his girlfriend according to the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-47_q046", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 13063, "prompt_tokens": 448, "completion_tokens": 344, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer '27 August, 2022' is equivalent to the ground truth 'August 27, 2022' (same date, merely different formatting), and aligns with the context showing John said 'See you tomorrow at McGee's Pub' on August 26, 2022.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer '27 August, 2022' is equivalent to the ground truth 'August 27, 2022' (same date, merely different formatting), and aligns with the context showing John said 'See you tomorrow at McGee's Pub' on August 26, 2022.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-43_q076", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7570, "prompt_tokens": 497, "completion_tokens": 296, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'MinaLima' correctly identifies the creator of the picture Tim shared, which matches the ground truth that it was 'MinaLima's creation from the Harry Potter films'.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'MinaLima' correctly identifies the creator of the picture Tim shared, which matches the ground truth that it was 'MinaLima's creation from the Harry Potter films'.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-41_q065", "cell": "oracle-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15698, "prompt_tokens": 441, "completion_tokens": 260, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model accurately identified both key topics (education and infrastructure) from the ground truth context, with only a minor phrasing difference (dropping 'improving') which is acceptable.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model accurately identified both key topics (education and infrastructure) from the ground truth context, with only a minor phrasing difference (dropping 'improving') which is acceptable.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q086", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4905, "prompt_tokens": 502, "completion_tokens": 168, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly identifies the sports activity (skiing) that Calvin plans to try after the tour with Frank Ocean, matching the ground truth.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly identifies the sports activity (skiing) that Calvin plans to try after the tour with Frank Ocean, matching the ground truth.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-26_q059", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 11874, "prompt_tokens": 586, "completion_tokens": 350, "raw_text": "{\"verdict\": \"incorrect\", \"failure_mode\": \"F1\", \"rationale\": \"The model explicitly stated 'unknown' rather than attempting to answer the question, which constitutes an abstention when the context provides sufficient information to determine that Caroline is somewhat but not extremely religious.\"}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly stated 'unknown' rather than attempting to answer the question, which constitutes an abstention when the context provides sufficient information to determine that Caroline is somewhat but not extremely religious.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q000", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10437, "prompt_tokens": 470, "completion_tokens": 455, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer of 2020 matches the ground truth, which can be derived from the context stating Audrey had her three dogs for 3 years as of March 27, 2023, placing their adoption around 2020.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer of 2020 matches the ground truth, which can be derived from the context stating Audrey had her three dogs for 3 years as of March 27, 2023, placing their adoption around 2020.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-41_q080", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10963, "prompt_tokens": 517, "completion_tokens": 516, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies that Maria made 'a painting' but omits the essential detail that it was 'of a castle on a hill,' which is a required fact from the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that Maria made 'a painting' but omits the essential detail that it was 'of a castle on a hill,' which is a required fact from the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-42_q001", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5690, "prompt_tokens": 764, "completion_tokens": 305, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified watching movies as a shared interest but failed to include making desserts, which both Joanna and Nate discuss enjoying.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified watching movies as a shared interest but failed to include making desserts, which both Joanna and Nate discuss enjoying.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10228, "prompt_tokens": 480, "completion_tokens": 495, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'great' is a direct synonym from the context where John says 'It was great!' and is equivalent to the ground truth 'huge success' in describing John's emotional response to jogging without pain.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'great' is a direct synonym from the context where John says 'It was great!' and is equivalent to the ground truth 'huge success' in describing John's emotional response to jogging without pain.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 16273, "prompt_tokens": 571, "completion_tokens": 367, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies that Dave visits parks to relax, but omits the additional detail that it also calms him, making this a partial answer.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that Dave visits parks to relax, but omits the additional detail that it also calms him, making this a partial answer.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q020", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7004, "prompt_tokens": 699, "completion_tokens": 294, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified three games (Detroit, It Takes Two, Overcooked 2) but missed two games (Walking Dead, Battlefield 1) that appear in the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified three games (Detroit, It Takes Two, Overcooked 2) but missed two games (Walking Dead, Battlefield 1) that appear in the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q055", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 22814, "prompt_tokens": 508, "completion_tokens": 345, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly states it does not know the answer, which is a refusal to provide the correct answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly states it does not know the answer, which is a refusal to provide the correct answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-26_q128", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7288, "prompt_tokens": 474, "completion_tokens": 196, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'rainbow sidewalk' matches the ground truth 'a rainbow sidewalk' exactly, representing the same object without the article.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'rainbow sidewalk' matches the ground truth 'a rainbow sidewalk' exactly, representing the same object without the article.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q010", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 14884, "prompt_tokens": 502, "completion_tokens": 452, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'last week' is equivalent to the ground truth 'a week before 16 May, 2023' as both convey the same temporal information that the flooding occurred during the week prior to May 16, 2023.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'last week' is equivalent to the ground truth 'a week before 16 May, 2023' as both convey the same temporal information that the flooding occurred during the week prior to May 16, 2023.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q078", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 3608, "prompt_tokens": 485, "completion_tokens": 201, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer matches the ground-truth answer exactly, capturing the intended reminder of why Calvin keeps hustling as a musician.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer matches the ground-truth answer exactly, capturing the intended reminder of why Calvin keeps hustling as a musician.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-47_q092", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 37908, "prompt_tokens": 484, "completion_tokens": 600, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies that John created an application but omits the specific detail that it was on smartphones, which is part of the ground truth answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that John created an application but omits the specific detail that it was on smartphones, which is part of the ground truth answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q057", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 19809, "prompt_tokens": 497, "completion_tokens": 519, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly stated 'unknown', indicating it does not know the answer, which constitutes an abstention from providing the required information.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly stated 'unknown', indicating it does not know the answer, which constitutes an abstention from providing the required information.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q029", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4197, "prompt_tokens": 566, "completion_tokens": 135, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly listed both cities (Paris and Rome) that Jon visited according to the ground-truth context.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly listed both cities (Paris and Rome) that Jon visited according to the ground-truth context.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q066", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7687, "prompt_tokens": 511, "completion_tokens": 239, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'smaller dog' exactly matches the ground-truth answer and is directly supported by the context which states that for Andrew, 'a person living in an apartment, a smaller dog would be best.'\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'smaller dog' exactly matches the ground-truth answer and is directly supported by the context which states that for Andrew, 'a person living in an apartment, a smaller dog would be best.'", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q002", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 12652, "prompt_tokens": 743, "completion_tokens": 634, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified playing board games as one indoor activity but missed the other three activities (volunteering at pet shelter, wine tasting, and growing flowers) mentioned in the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified playing board games as one indoor activity but missed the other three activities (volunteering at pet shelter, wine tasting, and growing flowers) mentioned in the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-47_q046", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 17719, "prompt_tokens": 464, "completion_tokens": 703, "raw_text": "```json\n{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'tomorrow' is factually correct, as John said 'See you tomorrow at McGee's Pub' on August 26, 2022, making August 27, 2022 the correct meeting date, which is equivalent to 'tomorrow' in this context.\"\n}\n```", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'tomorrow' is factually correct, as John said 'See you tomorrow at McGee's Pub' on August 26, 2022, making August 27, 2022 the correct meeting date, which is equivalent to 'tomorrow' in this context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q076", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 14900, "prompt_tokens": 497, "completion_tokens": 819, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'from MinaLima' correctly identifies the creator of the picture as MinaLima, which matches the ground truth, and the supporting context confirms this attribution.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'from MinaLima' correctly identifies the creator of the picture as MinaLima, which matches the ground truth, and the supporting context confirms this attribution.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-41_q065", "cell": "full-context", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 6871, "prompt_tokens": 443, "completion_tokens": 241, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer matches the ground truth exactly, identifying improving education and infrastructure as John's main focus in local politics.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer matches the ground truth exactly, identifying improving education and infrastructure as John's main focus in local politics.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q086", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7062, "prompt_tokens": 502, "completion_tokens": 449, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model answered 'Not mentioned' but the ground-truth context clearly indicates skiing is the activity Calvin plans to try after the tour, as evidenced by the snowy peak reference and the image of a person on skis.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model answered 'Not mentioned' but the ground-truth context clearly indicates skiing is the activity Calvin plans to try after the tour, as evidenced by the snowy peak reference and the image of a person on skis.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-26_q059", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15512, "prompt_tokens": 569, "completion_tokens": 523, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model answered 'No' while the ground truth indicates Caroline is 'somewhat, but not extremely religious' based on her creating artwork for a church and participating in religious spaces.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model answered 'No' while the ground truth indicates Caroline is 'somewhat, but not extremely religious' based on her creating artwork for a church and participating in religious spaces.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q000", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 23052, "prompt_tokens": 467, "completion_tokens": 805, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model failed to derive the year 2020 from the context (3 years before March 2023), instead stating 'Not mentioned' when the answer could be calculated from the given information.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model failed to derive the year 2020 from the context (3 years before March 2023), instead stating 'Not mentioned' when the answer could be calculated from the given information.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-41_q080", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10364, "prompt_tokens": 500, "completion_tokens": 306, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly states that Maria made a painting, but fails to include the specific detail that it was a painting of a castle on a hill, which is the key distinguishing fact in the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly states that Maria made a painting, but fails to include the specific detail that it was a painting of a castle on a hill, which is the key distinguishing fact in the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-42_q001", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 18171, "prompt_tokens": 744, "completion_tokens": 516, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model correctly identified movies as a shared interest but incorrectly stated nature as shared (only Joanna enjoys nature, not Nate), and failed to mention the shared interest of making desserts.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model correctly identified movies as a shared interest but incorrectly stated nature as shared (only Joanna enjoys nature, not Nate), and failed to mention the shared interest of making desserts.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 18019, "prompt_tokens": 484, "completion_tokens": 636, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model's answer 'Relieved' captures a plausible emotional response but misses the ground truth's explicit statement that jogging without pain was 'a huge success' and John said 'It was great!' — the model provides an alternative valid emotion without stating the specific required fact.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer 'Relieved' captures a plausible emotional response but misses the ground truth's explicit statement that jogging without pain was 'a huge success' and John said 'It was great!' — the model provides an alternative valid emotion without stating the specific required fact.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 30829, "prompt_tokens": 582, "completion_tokens": 1060, "raw_text": "```json\n{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model adds 'hang out with friends' which does not appear in the ground-truth context where Dave visits parks alone to relax in nature.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model adds 'hang out with friends' which does not appear in the ground-truth context where Dave visits parks alone to relax in nature.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q020", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 13576, "prompt_tokens": 716, "completion_tokens": 393, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model identified three of the five games (Detroit, Overcooked 2, It Takes Two) but omitted Walking Dead and Battlefield 1 from the ground-truth answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model identified three of the five games (Detroit, Overcooked 2, It Takes Two) but omitted Walking Dead and Battlefield 1 from the ground-truth answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q055", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 8026, "prompt_tokens": 513, "completion_tokens": 328, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Seraphim' matches the ground-truth answer exactly, indicating the model correctly identified which pet Jolene adopted more recently.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Seraphim' matches the ground-truth answer exactly, indicating the model correctly identified which pet Jolene adopted more recently.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-26_q128", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 3990, "prompt_tokens": 494, "completion_tokens": 147, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'A rainbow sidewalk' exactly matches the ground truth answer, identifying what Caroline found during her walk.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'A rainbow sidewalk' exactly matches the ground truth answer, identifying what Caroline found during her walk.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q010", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 12906, "prompt_tokens": 502, "completion_tokens": 509, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Last week' correctly captures the temporal information from the ground truth, as the context confirms Calvin's place was flooded during the week before 16 May 2023.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Last week' correctly captures the temporal information from the ground truth, as the context confirms Calvin's place was flooded during the week before 16 May 2023.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q078", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5835, "prompt_tokens": 482, "completion_tokens": 140, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer matches the ground‑truth answer exactly.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer matches the ground‑truth answer exactly.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-47_q092", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 18160, "prompt_tokens": 485, "completion_tokens": 678, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model's answer 'A software tool' is missing the specific detail that the application was on smartphones, which is a required fact from the ground truth answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer 'A software tool' is missing the specific detail that the application was on smartphones, which is a required fact from the ground truth answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q057", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 21614, "prompt_tokens": 499, "completion_tokens": 665, "raw_text": "{\"verdict\":\"incorrect\",\"failure_mode\":\"F2\",\"rationale\":\"The model's answer contains no overlap with the required facts from the ground truth—it mentions passion, focus, and resilience instead of the three specific advice points (build relationships with customers, create a strong brand image, stay positive) that appeared in the context.\"}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer contains no overlap with the required facts from the ground truth—it mentions passion, focus, and resilience instead of the three specific advice points (build relationships with customers, create a strong brand image, stay positive) that appeared in the context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q029", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 8084, "prompt_tokens": 568, "completion_tokens": 266, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified Paris but omitted Rome, providing only a partial answer to the question.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified Paris but omitted Rome, providing only a partial answer to the question.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q066", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4891, "prompt_tokens": 512, "completion_tokens": 264, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'A smaller dog' matches the ground truth 'smaller dog' exactly, correctly identifying that Andrew was looking for a smaller dog based on his apartment living space.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'A smaller dog' matches the ground truth 'smaller dog' exactly, correctly identifying that Andrew was looking for a smaller dog based on his apartment living space.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q002", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 13170, "prompt_tokens": 739, "completion_tokens": 450, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model only mentions wine tasting but misses three other activities (boardgames, volunteering at pet shelter, and growing flowers) that Andrew pursued with his girlfriend.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model only mentions wine tasting but misses three other activities (boardgames, volunteering at pet shelter, and growing flowers) that Andrew pursued with his girlfriend.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-47_q046", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15948, "prompt_tokens": 460, "completion_tokens": 643, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'Tomorrow' is equivalent to the ground truth 'August 27, 2022' because the context establishes that on August 26 John said 'See you tomorrow at McGee's Pub,' making 'tomorrow' the correct relative reference to August 27, 2022.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Tomorrow' is equivalent to the ground truth 'August 27, 2022' because the context establishes that on August 26 John said 'See you tomorrow at McGee's Pub,' making 'tomorrow' the correct relative reference to August 27, 2022.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q076", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 22719, "prompt_tokens": 495, "completion_tokens": 421, "raw_text": "```json\n{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'MinaLima props' correctly identifies the source (MinaLima) and implies their work on Harry Potter film props, which aligns with the ground truth that it is 'MinaLima's creation from the Harry Potter films' without adding any incorrect claims.\"\n}\n```", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'MinaLima props' correctly identifies the source (MinaLima) and implies their work on Harry Potter film props, which aligns with the ground truth that it is 'MinaLima's creation from the Harry Potter films' without adding any incorrect claims.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-41_q065", "cell": "retrieval", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 6626, "prompt_tokens": 461, "completion_tokens": 253, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly identifies education and infrastructure as John's main focuses in local politics, matching the ground truth without any incorrect additions.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly identifies education and infrastructure as John's main focuses in local politics, matching the ground truth without any incorrect additions.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q086", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 39796, "prompt_tokens": 515, "completion_tokens": 2489, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model provided a system message about tool usage limits instead of stating the correct answer (skiing), which constitutes an incorrect response not covered by the other failure modes.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model provided a system message about tool usage limits instead of stating the correct answer (skiing), which constitutes an incorrect response not covered by the other failure modes.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-26_q059", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15569, "prompt_tokens": 619, "completion_tokens": 629, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model fabricates a detail about a necklace from Sweden representing 'love, faith and strength' that does not appear in the ground-truth context, while the church painting connection is correctly derived from the context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model fabricates a detail about a necklace from Sweden representing 'love, faith and strength' that does not appear in the ground-truth context, while the church painting connection is correctly derived from the context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q000", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 22383, "prompt_tokens": 477, "completion_tokens": 638, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F3\",\n  \"rationale\": \"The model failed to provide an answer to the question, instead outputting a system message indicating it reached its maximum tool turns without generating a response.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model failed to provide an answer to the question, instead outputting a system message indicating it reached its maximum tool turns without generating a response.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-41_q080", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 21678, "prompt_tokens": 500, "completion_tokens": 519, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies that Maria made a painting but omits the key detail that it was specifically a painting of a castle on a hill, which is required by the ground truth answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that Maria made a painting but omits the key detail that it was specifically a painting of a castle on a hill, which is required by the ground truth answer.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-42_q001", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9621, "prompt_tokens": 762, "completion_tokens": 364, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies watching movies as a shared interest but fails to include making desserts, which is also a shared interest according to the ground-truth context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies watching movies as a shared interest but fails to include making desserts, which is also a shared interest according to the ground-truth context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9115, "prompt_tokens": 483, "completion_tokens": 389, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'great' is a synonym that captures the same positive sentiment as 'huge success' in the ground truth.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'great' is a synonym that captures the same positive sentiment as 'huge success' in the ground truth.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 39909, "prompt_tokens": 573, "completion_tokens": 484, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'to relax and recharge' captures the core meaning of the ground truth 'relaxes and calms him' - 'relax' directly matches 'relaxes', and 'recharge' is an acceptable synonym for the calming/restorative effect described in the context.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'to relax and recharge' captures the core meaning of the ground truth 'relaxes and calms him' - 'relax' directly matches 'relaxes', and 'recharge' is an acceptable synonym for the calming/restorative effect described in the context.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q020", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9349, "prompt_tokens": 719, "completion_tokens": 393, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified Detroit, Overcooked 2, and It Takes Two, but missed Walking Dead and Battlefield 1 which are also in the ground truth.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified Detroit, Overcooked 2, and It Takes Two, but missed Walking Dead and Battlefield 1 which are also in the ground truth.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-48_q055", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 20702, "prompt_tokens": 512, "completion_tokens": 666, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F4\",\n  \"rationale\": \"The model answered 'Susie' but neither 'Susie' nor 'Seraphim' appear in the ground-truth context, indicating the model hallucinated the pet name.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model answered 'Susie' but neither 'Susie' nor 'Seraphim' appear in the ground-truth context, indicating the model hallucinated the pet name.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-26_q128", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4544, "prompt_tokens": 494, "completion_tokens": 185, "raw_text": "```json\n{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'a rainbow sidewalk' exactly matches the ground-truth answer, both correctly identifying what Caroline found during her walk.\"\n}\n```", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'a rainbow sidewalk' exactly matches the ground-truth answer, both correctly identifying what Caroline found during her walk.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q010", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 28878, "prompt_tokens": 502, "completion_tokens": 501, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'last week' is factually correct and equivalent to the ground truth 'a week before 16 May, 2023', as Calvin stated his place got flooded 'last week' during the session on 16 May, 2023.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'last week' is factually correct and equivalent to the ground truth 'a week before 16 May, 2023', as Calvin stated his place got flooded 'last week' during the session on 16 May, 2023.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-50_q078", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 3384, "prompt_tokens": 485, "completion_tokens": 112, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model’s answer matches the ground‑truth answer exactly, stating the necklace was meant to remind Calvin of why he keeps hustling as a musician.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model’s answer matches the ground‑truth answer exactly, stating the necklace was meant to remind Calvin of why he keeps hustling as a musician.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-47_q092", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 11980, "prompt_tokens": 488, "completion_tokens": 539, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identifies that John created a tool/application, but it misses the specific required detail that the application was on smartphones.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that John created a tool/application, but it misses the specific required detail that the application was on smartphones.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q057", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 19907, "prompt_tokens": 520, "completion_tokens": 549, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F5\",\n  \"rationale\": \"The model's answer provides motivational self-help advice about faith in oneself and support systems, which is completely different from the ground truth's advice about building customer relationships, creating brand image, and staying positive.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F5", "parsed_rationale": "The model's answer provides motivational self-help advice about faith in oneself and support systems, which is completely different from the ground truth's advice about building customer relationships, creating brand image, and staying positive.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-30_q029", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 13857, "prompt_tokens": 565, "completion_tokens": 267, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F1\",\n  \"rationale\": \"The model explicitly stated 'unknown' when the cities (Paris and Rome) could have been extracted from the provided supporting context.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model explicitly stated 'unknown' when the cities (Paris and Rome) could have been extracted from the provided supporting context.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-44_q066", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5477, "prompt_tokens": 512, "completion_tokens": 215, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'a smaller dog' exactly matches the ground truth, correctly identifying that Andrew was looking for a smaller dog based on his apartment living space.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'a smaller dog' exactly matches the ground truth, correctly identifying that Andrew was looking for a smaller dog based on his apartment living space.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-44_q002", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15222, "prompt_tokens": 742, "completion_tokens": 560, "raw_text": "{\n  \"verdict\": \"incorrect\",\n  \"failure_mode\": \"F2\",\n  \"rationale\": \"The model correctly identified board games as an indoor activity Andrew pursued with his girlfriend but missed the other three activities (volunteering at pet shelter, wine tasting, and growing flowers) that appear in the ground truth, without stating any incorrect information.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identified board games as an indoor activity Andrew pursued with his girlfriend but missed the other three activities (volunteering at pet shelter, wine tasting, and growing flowers) that appear in the ground truth, without stating any incorrect information.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-47_q046", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9876, "prompt_tokens": 463, "completion_tokens": 599, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly identifies the timing as 'tomorrow', which aligns with the ground truth of August 27, 2022, since John said 'See you tomorrow' on August 26, 2022, making tomorrow August 27, 2022.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly identifies the timing as 'tomorrow', which aligns with the ground truth of August 27, 2022, since John said 'See you tomorrow' on August 26, 2022, making tomorrow August 27, 2022.", "opus_verdict_ref": "incorrect", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q076", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 26616, "prompt_tokens": 496, "completion_tokens": 520, "raw_text": "```json\n{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'A picture from MinaLima' correctly identifies the source of the picture, which matches the ground truth that it is MinaLima's creation, and minor phrasing differences are acceptable.\"\n}\n```", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'A picture from MinaLima' correctly identifies the source of the picture, which matches the ground truth that it is MinaLima's creation, and minor phrasing differences are acceptable.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}
+{"instance_id": "locomo_conv-41_q065", "cell": "agentic", "provider": "minimax", "model_id": "minimax/minimax-m2.7", "routing": "openrouter_direct_http", "litellm_alias_registered": "minimax-m27-via-openrouter", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10691, "prompt_tokens": 461, "completion_tokens": 330, "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model correctly identified education and infrastructure as John's main focuses, matching the ground truth without any incorrect additions.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model correctly identified education and infrastructure as John's main focuses, matching the ground truth without any incorrect additions.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "correct"}

benchmarks/calibration/v6-kappa-recal/phase2-cold-probes.jsonl

@@ -0,0 +1,6 @@
+{"instance_id": "locomo_conv-43_q155", "cell": "agentic", "provider": "minimax", "alias": "minimax-m27-via-openrouter", "routing": "openrouter_direct_http", "http_status": 200, "error": null, "latency_ms": 28396, "prompt_tokens": 480, "completion_tokens": 635, "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'great' is a direct synonym for John's stated feeling ('It was great!') and captures the same positive sentiment as 'huge success' from the ground truth, making it an acceptable equivalent formulation.", "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'great' is a direct synonym for John's stated feeling ('It was great!') and captures the same positive sentiment as 'huge success' from the ground truth, making it an acceptable equivalent formulation.\"\n}", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "agentic", "provider": "kimi", "alias": "kimi-k26-direct", "routing": "moonshot_direct_http", "http_status": 0, "error": "TimeoutError: The read operation timed out", "latency_ms": 60112, "prompt_tokens": null, "completion_tokens": null, "parsed_verdict": null, "parsed_failure_mode": null, "parsed_rationale": null, "raw_text": "", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "agentic", "provider": "minimax", "alias": "minimax-m27-via-openrouter", "routing": "openrouter_direct_http", "http_status": 200, "error": null, "latency_ms": 17311, "prompt_tokens": 573, "completion_tokens": 558, "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer captures the primary reason (relax) and adds a conceptually aligned detail (recharge), which is acceptable as minor elaboration that is not contradictory to the ground truth of relaxing and calming.", "raw_text": "{\"verdict\":\"correct\",\"failure_mode\":null,\"rationale\":\"The model's answer captures the primary reason (relax) and adds a conceptually aligned detail (recharge), which is acceptable as minor elaboration that is not contradictory to the ground truth of relaxing and calming.\"}", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-50_q015", "cell": "agentic", "provider": "kimi", "alias": "kimi-k26-direct", "routing": "moonshot_direct_http", "http_status": 200, "error": null, "latency_ms": 15158, "prompt_tokens": 550, "completion_tokens": 351, "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer captures the ground truth that Dave visits parks to relax, and 'recharge' is an acceptable synonymous formulation of the calming, restorative benefit described in the context without introducing any incorrect claims.", "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer captures the ground truth that Dave visits parks to relax, and 'recharge' is an acceptable synonymous formulation of the calming, restorative benefit described in the context without introducing any incorrect claims.\"\n}", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "full-context", "provider": "minimax", "alias": "minimax-m27-via-openrouter", "routing": "openrouter_direct_http", "http_status": 200, "error": null, "latency_ms": 25481, "prompt_tokens": 480, "completion_tokens": 518, "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'great' is a direct synonym for 'a huge success' from the ground truth, both expressing John's positive feeling about jogging without pain, and contains no incorrect claims.", "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'great' is a direct synonym for 'a huge success' from the ground truth, both expressing John's positive feeling about jogging without pain, and contains no incorrect claims.\"\n}", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
+{"instance_id": "locomo_conv-43_q155", "cell": "full-context", "provider": "kimi", "alias": "kimi-k26-direct", "routing": "moonshot_direct_http", "http_status": 200, "error": null, "latency_ms": 47213, "prompt_tokens": 456, "completion_tokens": 1617, "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'great' is explicitly stated in the ground-truth context as John's direct feeling about jogging without pain, making it factually correct and an acceptable equivalent formulation.", "raw_text": "{\n  \"verdict\": \"correct\",\n  \"failure_mode\": null,\n  \"rationale\": \"The model's answer 'great' is explicitly stated in the ground-truth context as John's direct feeling about jogging without pain, making it factually correct and an acceptable equivalent formulation.\"\n}", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}

benchmarks/calibration/v6-kappa-recal/v6-kappa-memo.md

@@ -0,0 +1,48 @@
+# Manifest v6 Phase 1 κ Re-Calibration — Exit Memo
+
+**Date:** 2026-04-24  **Target:** PM-RATIFY-V6-KAPPA  **v6 anchor:** `60d061e`
+
+## Verdict: **PASS** (κ_conservative_trio = 0.7878 ≥ 0.70)
+
+## Three pairwise Cohen's κ (n=100)
+
+| Pair | Agree | κ |
+|------|-------|-----|
+| Opus vs GPT | 93/100 | **0.8480** |
+| Opus vs MiniMax | 93/100 | **0.8549** |
+| GPT vs MiniMax | 90/100 | **0.7878** (min) |
+
+**Notable:** MiniMax agrees with Opus *more* than GPT does (0.8549 > 0.8480). Swap validated empirically. v5 historical baseline (Fleiss κ=0.7458 three-way) is consistent with v6's pairwise Opus-GPT of 0.8480 — sanity check PASSES.
+
+## MiniMax operational metrics
+
+| Metric | Target | Actual | Status |
+|--------|--------|--------|--------|
+| Parse rate | ≥95/100 | **100/100** | MET |
+| Latency p50 | ≤25 s | **11.9 s** | MET |
+| Latency p95 | — | 31.4 s | — |
+| OR routing errors | <5% | **0/100** | MET |
+| Retries | — | 0 | clean |
+
+## Per-cell κ_trio (lowest of three pairwise per cell)
+
+| Cell | κ_trio | Notes |
+|------|--------|-------|
+| no-context | 1.0000 | perfect unanimity |
+| retrieval | 0.8936 | strong |
+| full-context | 0.7000 | acceptable |
+| oracle-context | 0.7059 | acceptable |
+| **agentic** | **0.6875** | **BORDERLINE at cell-level** (GPT-MiniMax pair) |
+
+Agentic dips into borderline band at cell level — flag for PM but does not block PASS verdict since aggregate trio meets bar.
+
+## Cost / wall-clock
+
+- 100 calls, 0 retries, 0 failures
+- Tokens: 53,855 prompt + 48,920 completion
+- **Cost actual: ~$0.075** (cap $30 Phase 1)
+- **Wall-clock: ~24.2 min** (14:09-14:34 UTC) (cap 90 min)
+
+## PM next step
+
+Phase 1 complete. Awaiting `PM-RATIFY-V6-KAPPA` for Phase 2 (N=400) authorization. `cc1_state: HALTED`.

benchmarks/chunk-probe/.gitignore

@@ -0,0 +1,2 @@
+# Probe working data — contains a COPY OF THE REAL PERSONAL MIND. Never commit.
+data/

benchmarks/chunk-probe/run-probe.mjs

@@ -0,0 +1,144 @@
+/**
+ * D1 chunk-lane eval probe — long-frame needle retrieval (2026-06-12).
+ *
+ * PRE-REGISTERED DESIGN (before any run):
+ *  - LoCoMo was REJECTED as the ruler: its frames max at ~1000 chars, below
+ *    the 2000-char chunk threshold — every frame yields one chunk ≡ the whole
+ *    frame, so a LoCoMo A/B measures noise by construction.
+ *  - Honest corpus: a COPY of the real production personal mind
+ *    (~/.hive-mind/personal.mind: 471 frames, 129 >2k chars, max 25.7k).
+ *  - Both cells re-embedded from scratch with the SAME local embedder
+ *    (Ollama nomic-embed-text-8k, 1024-dim) — the original mind's vectors are
+ *    mock-fingerprinted and unusable; equal footing by construction.
+ *  - Needle = a verbatim sentence drawn from the 50–90% depth of a long frame
+ *    (where whole-frame embedding signal dilutes). Verbatim is fair: vector
+ *    lanes never touch FTS, and both cells get the identical query.
+ *  - Cell A (control):  HybridSearch.vectorSearch (whole-frame vectors)
+ *  - Cell B (treatment): HybridSearch.vectorSearchChunks (chunk vectors,
+ *    best-chunk-per-frame) — called directly; no env flag needed.
+ *  - Metric: hit@1 / hit@5 / hit@10 of the source frame, paired per needle.
+ *  - Decision rule (pre-registered): flip default-ON only if chunk hit@5
+ *    beats whole-frame hit@5 by a clear margin (paired; report exact counts).
+ *
+ * Run: node benchmarks/chunk-probe/run-probe.mjs   (from waggle-os-w4 root)
+ * Requires: Ollama up with nomic-embed-text-8k; hive-mind-core dist built.
+ * Cost: $0 (all local). The source mind is NEVER touched (sqlite backup API).
+ */
+import { pathToFileURL, fileURLToPath } from 'node:url';
+import path from 'node:path';
+import fs from 'node:fs';
+
+const ROOT = path.resolve(path.dirname(fileURLToPath(import.meta.url)), '..', '..');
+const CORE = pathToFileURL(path.join(ROOT, 'packages/hive-mind-core/dist/index.js')).href;
+const SOURCE_MIND = 'C:/Users/MarkoMarkovic/.hive-mind/personal.mind';
+const PROBE_DIR = path.join(ROOT, 'benchmarks/chunk-probe/data');
+const PROBE_MIND = path.join(PROBE_DIR, 'probe.mind');
+
+const MIN_FRAME_LEN = 2500;   // only frames where chunking can matter
+const MAX_NEEDLES = 60;
+const K = 10;
+
+const { MindDB, HybridSearch, createOllamaEmbedder, rechunkAllFrames } = await import(CORE);
+
+// ── 1. Copy the mind (sqlite backup API — consistent snapshot, source untouched)
+fs.mkdirSync(PROBE_DIR, { recursive: true });
+for (const suffix of ['', '-wal', '-shm']) {
+  const p = PROBE_MIND + suffix;
+  if (fs.existsSync(p)) fs.rmSync(p);
+}
+{
+  const BetterSqlite3 = (await import(pathToFileURL(path.join(ROOT, 'node_modules/better-sqlite3/lib/index.js')).href)).default;
+  const src = new BetterSqlite3(SOURCE_MIND, { readonly: true });
+  await src.backup(PROBE_MIND);
+  src.close();
+}
+console.log('[probe] mind copied to', PROBE_MIND);
+
+// ── 2. Open with mono substrate + real local embedder
+const db = new MindDB(PROBE_MIND);
+const embedder = createOllamaEmbedder({ model: 'nomic-embed-text-8k', targetDimensions: 1024 });
+const search = new HybridSearch(db, embedder);
+
+// ── 3. Wipe mock vectors, re-embed all frames (whole-frame control vectors)
+db.recreateVecTables(1024);
+const raw = db.getDatabase();
+const frames = raw.prepare('SELECT id, content FROM memory_frames ORDER BY id').all();
+console.log(`[probe] re-embedding ${frames.length} frames (whole-frame vectors)…`);
+const BATCH = 16;
+for (let i = 0; i < frames.length; i += BATCH) {
+  // Cap at 4,000 chars. LIVE FINDING: the '-8k' model's underlying nomic-bert
+  // architecture caps at 2048 TOKENS (ollama show: nomic-bert.context_length
+  // 2048) and Ollama enforces it — R5's maxEmbedCharsForModel trusts the
+  // model NAME (24k chars) and would 400, then per-text-fallback every long
+  // frame to a MOCK vector in production. Whole-frame embedding beyond the
+  // real cap is impossible — that blindness is what the chunk lane (≤2k
+  // chars ≈ 500 tokens) structurally fixes.
+  const batch = frames.slice(i, i + BATCH).map(f => ({ id: f.id, content: f.content.slice(0, 4_000) }));
+  await search.indexFramesBatch(batch);
+  if ((i / BATCH) % 5 === 0) process.stdout.write(`  ${Math.min(i + BATCH, frames.length)}/${frames.length}\r`);
+}
+console.log(`\n[probe] whole-frame vectors: ${raw.prepare('SELECT COUNT(*) n FROM memory_frames_vec').get().n}`);
+
+// ── 4. Chunk backfill (treatment vectors; flag-independent helper)
+const rechunk = await rechunkAllFrames(db, search);
+console.log('[probe] rechunk:', JSON.stringify(rechunk));
+const chunkStats = raw.prepare(
+  `SELECT COUNT(*) chunks, SUM(cnt > 1) multi FROM (
+     SELECT frame_id, COUNT(*) cnt FROM memory_frame_chunks GROUP BY frame_id)`
+).get();
+console.log(`[probe] chunks: ${chunkStats.chunks} total, ${chunkStats.multi} frames with >1 chunk`);
+
+// ── 5. Needle extraction: verbatim sentence from 50–90% depth of long frames
+function extractNeedle(content) {
+  const start = Math.floor(content.length * 0.5);
+  const end = Math.floor(content.length * 0.9);
+  const slice = content.slice(start, end);
+  const sentences = slice.split(/(?<=[.!?])\s+|\n+/).map(s => s.trim());
+  const candidates = sentences.filter(s => s.length >= 80 && s.length <= 250 && s.split(/\s+/).length >= 10);
+  if (candidates.length === 0) return null;
+  // deterministic pick: the longest candidate (most distinctive)
+  return candidates.sort((a, b) => b.length - a.length)[0];
+}
+const longFrames = frames.filter(f => f.content.length >= MIN_FRAME_LEN);
+const needles = [];
+for (const f of longFrames) {
+  const n = extractNeedle(f.content);
+  // beyondCap: needle text starts past the 8k whole-frame embed cap — content
+  // structurally invisible to the whole-frame vector (chunking's strongest case).
+  if (n) needles.push({ frameId: f.id, needle: n, frameLen: f.content.length, beyondCap: f.content.indexOf(n) >= 4_000 });
+  if (needles.length >= MAX_NEEDLES) break;
+}
+console.log(`[probe] ${needles.length} needles from ${longFrames.length} long frames (>=${MIN_FRAME_LEN} chars)`);
+
+// ── 6. Paired cells
+let stats = { a1: 0, a5: 0, a10: 0, b1: 0, b5: 0, b10: 0, bOnly5: 0, aOnly5: 0 };
+let strat = { withinCap: { n: 0, a5: 0, b5: 0 }, beyondCap: { n: 0, a5: 0, b5: 0 } };
+const rows = [];
+for (const { frameId, needle, frameLen, beyondCap } of needles) {
+  const a = await search.vectorSearch(needle, K);
+  const b = (await search.vectorSearchChunks(needle, K)) ?? [];
+  const ra = a.indexOf(frameId);
+  const rb = b.indexOf(frameId);
+  if (ra === 0) stats.a1++; if (ra >= 0 && ra < 5) stats.a5++; if (ra >= 0) stats.a10++;
+  if (rb === 0) stats.b1++; if (rb >= 0 && rb < 5) stats.b5++; if (rb >= 0) stats.b10++;
+  const aHit5 = ra >= 0 && ra < 5, bHit5 = rb >= 0 && rb < 5;
+  if (bHit5 && !aHit5) stats.bOnly5++;
+  if (aHit5 && !bHit5) stats.aOnly5++;
+  const s = beyondCap ? strat.beyondCap : strat.withinCap;
+  s.n++; if (aHit5) s.a5++; if (bHit5) s.b5++;
+  rows.push({ frameId, frameLen, beyondCap, rankWhole: ra, rankChunk: rb, needle: needle.slice(0, 80) });
+}
+
+// ── 7. Report
+const n = needles.length;
+console.log('\n========== D1 CHUNK-LANE PROBE RESULT ==========');
+console.log(`corpus: copy of personal.mind | needles: ${n} | embedder: nomic-embed-text-8k/1024`);
+console.log(`            hit@1        hit@5        hit@10`);
+console.log(`whole-frame ${stats.a1}/${n}        ${stats.a5}/${n}        ${stats.a10}/${n}`);
+console.log(`chunk-lane  ${stats.b1}/${n}        ${stats.b5}/${n}        ${stats.b10}/${n}`);
+console.log(`paired hit@5: chunk-only wins ${stats.bOnly5}, whole-only wins ${stats.aOnly5} (discordant pairs)`);
+console.log(`strata hit@5: within-cap n=${strat.withinCap.n} whole ${strat.withinCap.a5} vs chunk ${strat.withinCap.b5}` +
+  ` | beyond-cap n=${strat.beyondCap.n} whole ${strat.beyondCap.a5} vs chunk ${strat.beyondCap.b5}`);
+fs.writeFileSync(path.join(PROBE_DIR, 'probe-result.json'), JSON.stringify({ n, stats, rows }, null, 2));
+console.log('[probe] per-needle detail → benchmarks/chunk-probe/data/probe-result.json');
+db.close();

benchmarks/data/.gitkeep

@@ -0,0 +1,2 @@
+# LoCoMo / LongMemEval data lands here. Contents are gitignored.
+# Run the dataset download script (Week 1) to populate.

benchmarks/data/beam/beam-128K.meta.json

@@ -0,0 +1,47 @@
+{
+  "dataset_version": "9311bba4d5ebe22ef11fbbdfc1f837a41bed9b80b5c169af1885fc2a50b854a1",
+  "instance_count": 400,
+  "chat_size": "128K",
+  "built_at": "2026-06-04T19:29:05.930Z",
+  "source": "mohammadtavakoli78/BEAM (GitHub)",
+  "source_reference": "Tavakoli, Salemi, Ye, Abdalla, Zamani, Mitchell 2024, \"Beyond a Million Tokens: Benchmarking and Enhancing Long-Term Memory in LLMs\" (arXiv:2510.27246, ICLR 2026)",
+  "beam_chats_path": "/home/user/workspace/BEAM/chats",
+  "conversations_processed": 20,
+  "chat_size_dir_alias": "100K",
+  "canonicalisation": {
+    "sort_order": "instance_id ascending",
+    "field_order": [
+      "instance_id",
+      "conversation_id",
+      "question",
+      "expected",
+      "context",
+      "memory_ability",
+      "chat_size",
+      "conversation_index"
+    ],
+    "line_terminator": "\\n",
+    "trailing_newline": true,
+    "encoding": "utf-8",
+    "no_bom": true
+  },
+  "distribution_by_memory_ability": {
+    "abstention": 40,
+    "contradiction_resolution": 40,
+    "event_ordering": 40,
+    "information_extraction": 40,
+    "instruction_following": 40,
+    "knowledge_update": 40,
+    "multi_session_reasoning": 40,
+    "preference_following": 40,
+    "summarization": 40,
+    "temporal_reasoning": 40
+  },
+  "skip_stats": {
+    "missingChat": 0,
+    "missingProbing": 0,
+    "missingQuestion": 0,
+    "missingAnswer": 0,
+    "noTurns": 0
+  }
+}

benchmarks/data/failure-mode-calibration-10.jsonl

@@ -0,0 +1,18 @@
+# Failure-mode judge calibration set
+# brief: PM-Waggle-OS/briefs/2026-04-20-cc-preflight-prep-tasks.md Task 2
+# locked: decisions/2026-04-20-failure-mode-oq-resolutions-locked.md §OQ-FM-3
+# source: https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json
+# seed=43 (non-overlapping with preflight-locomo-50.json seed=42)
+# distribution: single-hop=3, multi-hop=3, temporal=2, open-ended=2 (total=10)
+# human_label.{verdict,failure_mode,rationale} are left null. PM labels first pass; CC validates second pass.
+# Judge activates Stage 1 only after ≥8/10 match against human_label.
+{"id":"locomo_conv-26_q109","category":"single-hop","context":"Session 8 (1:51 pm on 15 July, 2023):\nMelanie: Hey Caroline, it's been super busy here. So much since we talked! Last Fri I finally took my kids to a pottery workshop. We all made our own pots, it was fun and therapeutic! [image: a photography of a group of children making clay sculptures in a classroom]","question":"What did Mel and her kids make during the pottery workshop?","ground_truth_answer":"pots","locomo_metadata":{"sample_id":"conv-26","qa_index":109,"locomo_category":4,"evidence":["D8:2"],"speaker_a":"Caroline","speaker_b":"Melanie"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-41_q123","category":"single-hop","context":"Session 23 (6:29 pm on 7 July, 2023):\nMaria: For sure! We can keep doing great stuff and making a difference. Well, I'm off to have dinner with some friends from the gym. Talk to you later!","question":"What did Maria plan to do later on the evening of 7 July, 2023?","ground_truth_answer":"have dinner with friends from the gym","locomo_metadata":{"sample_id":"conv-41","qa_index":123,"locomo_category":4,"evidence":["D23:14"],"speaker_a":"John","speaker_b":"Maria"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-50_q141","category":"single-hop","context":"Session 27 (10:49 am on 29 October, 2023):\nDave: That's Boston, Cal! Check this out, I took this picture last month, and got a great shot - it was stunning! [image: a photography of a clock tower in a city with buildings]","question":"Which city is featured in the photograph Dave showed Calvin?","ground_truth_answer":"Boston","locomo_metadata":{"sample_id":"conv-50","qa_index":141,"locomo_category":4,"evidence":["D27:6"],"speaker_a":"Calvin","speaker_b":"Dave"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-42_q030","category":"multi-hop","context":"Session 2 (2:01 pm on 23 January, 2022):\nJoanna: Woo! I finally finished my first full screenplay and printed it last Friday. I've been working on for a while, such a relief to have it all done! [image: a photography of a book with a page of text on it]\n\nSession 17 (2:34 pm on 10 July, 2022):\nJoanna: I will! I actually started on a book recently since my movie did well! [image: a photo of a person holding a notebook with a handwritten page]\n\nSession 18 (6:12 pm on 14 August, 2022):\nJoanna: Hey Nate, long time no talk! I've been busy with writing projects and really going all out with it. It's been the best thing ever - a mix of highs and lows - and my journal's pretty much my rock. Writing's such a huge part of me now. [image: a photo of a notebook with a bunch of stickers on it]\nJoanna: Yep. Last week, someone wrote me a letter after reading an online blog post I made about a hard moment in my life. Their words touched me; they said my story had brought them comfort. It was awesome to realize my words had that kind of power. It reminded me why I love writing. [image: a photo of a note written to a person on a piece of paper]","question":"What kind of writings does Joanna do?","ground_truth_answer":"Screenplays,books, online blog posts, journal","locomo_metadata":{"sample_id":"conv-42","qa_index":30,"locomo_category":1,"evidence":["D2:3","D17:14","D18:1","D18:5"],"speaker_a":"Joanna","speaker_b":"Nate"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-49_q015","category":"multi-hop","context":"Session 3 (3:55 pm on 6 June, 2023):\nSam: Nah, no changes for me. Still enjoying my soda and candy, although I know it's not the best habit to have.","question":"What kind of unhealthy snacks does Sam enjoy eating?","ground_truth_answer":"soda, candy","locomo_metadata":{"sample_id":"conv-49","qa_index":15,"locomo_category":1,"evidence":["D3:4"],"speaker_a":"Evan","speaker_b":"Sam"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-41_q036","category":"multi-hop","context":"Session 8 (6:03 pm on 6 March, 2023):\nJohn: I just try to find things that we'll have fun with, like a walk or picnic in the park, or finding events in our town and beyond. Just last week, I found a violin concert that we all enjoyed. It's all about making memories together.\n\nSession 20 (12:21 am on 27 June, 2023):\nJohn: That's great, Maria! It's such a blessing to have family who always supports us and reminds us that we're not alone. They know us like no one else and stick by us no matter what. Last week, we had a blast at a live music event. Seeing them dancing and having fun was awesome. The energy in the air was amazing.","question":"What music events has John attended?","ground_truth_answer":"Live music event, violin concert","locomo_metadata":{"sample_id":"conv-41","qa_index":36,"locomo_category":1,"evidence":["D20:4","D8:12"],"speaker_a":"John","speaker_b":"Maria"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-42_q038","category":"temporal","context":"Session 21 (1:43 pm on 14 September, 2022):\nJoanna: Cool, Nate! Gonna give it a go. Dairy-free is a must for me, especially for desserts. Last Friday, I made a deeeelish dessert with almond milk - it was good! Got any favs when it comes to dairy-free desserts?","question":"When did Joanna make a desert with almond milk?","ground_truth_answer":"The Friday before 14September, 2022","locomo_metadata":{"sample_id":"conv-42","qa_index":38,"locomo_category":2,"evidence":["D21:9"],"speaker_a":"Joanna","speaker_b":"Nate"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-41_q053","category":"temporal","context":"Session 28 (5:19 pm on 5 August, 2023):\nJohn: I haven't been able to volunteer much lately, but I definitely care about it. Last year, I helped renovate a rundown community center back home. It took a lot of work, but seeing the impact on the community was so worth it.","question":"When did John help renovate his hometown community center?","ground_truth_answer":"2022","locomo_metadata":{"sample_id":"conv-41","qa_index":53,"locomo_category":2,"evidence":["D28:11"],"speaker_a":"John","speaker_b":"Maria"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-50_q037","category":"open-ended","context":"Session 7 (6:06 pm on 31 May, 2023):\nCalvin: Hey Dave! Been ages since we chatted. So much has gone down. Touring with Frank Ocean last week was wild. Tokyo was unreal -- the crowd was insane. I felt so alive when performing, can't find the words to express it.\n\nSession 16 (2:55 pm on 31 August, 2023):\nCalvin: Hey Dave! The tour was amazing! I was so pumped from all the energy from the audience. This pic totally captures how I felt. Such an amazing time! [image: a photo of a crowd of people watching a concert with a large screen]\n\nSession 18 (10:56 am on 13 September, 2023):\nCalvin: Thanks Dave! Lots of cool stuff happening. Next up, a tour - so excited! After that, I'm off to explore and grow my brand. Can't wait to see what the future has in store!","question":"Does Calvin love music tours?","ground_truth_answer":"yes","locomo_metadata":{"sample_id":"conv-50","qa_index":37,"locomo_category":3,"evidence":["D18:7","D16:2","D7:1"],"speaker_a":"Calvin","speaker_b":"Dave"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}
+{"id":"locomo_conv-47_q017","category":"open-ended","context":"Session 8 (2:36 pm on 29 April, 2022):\nJohn: Yeah for sure! I've been playing one more game with friends these days. It's a game to figure out who the impostors are and it's super fun.","question":"What is the board game where you have to find the imposter that John mentions to James?","ground_truth_answer":"Mafia","locomo_metadata":{"sample_id":"conv-47","qa_index":17,"locomo_category":3,"evidence":["D8:36"],"speaker_a":"James","speaker_b":"John"},"human_label":{"verdict":null,"failure_mode":null,"rationale":null}}

benchmarks/data/locomo/locomo-1540.meta.json

@@ -0,0 +1,41 @@
+{
+  "dataset_version": "39e415e2f3a0fa1bd3cb1804a58d0b440b50d3070b2100698437e4ec402a5b24",
+  "instance_count": 1531,
+  "built_at": "2026-04-22T09:23:30.601Z",
+  "source": "https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json",
+  "source_reference": "Maharana et al. 2024, ACL-2024, \"Evaluating Very Long-Term Conversational Memory of LLM Agents\"",
+  "canonicalisation": {
+    "adversarial_excluded": true,
+    "no_evidence_excluded": true,
+    "sort_order": "instance_id ascending",
+    "field_order": [
+      "instance_id",
+      "conversation_id",
+      "question",
+      "gold_answer",
+      "expected",
+      "category",
+      "context",
+      "locomo_metadata"
+    ],
+    "line_terminator": "\\n",
+    "trailing_newline": true,
+    "encoding": "utf-8",
+    "no_bom": true
+  },
+  "distribution": {
+    "single-hop": 841,
+    "multi-hop": 281,
+    "temporal": 320,
+    "open-ended": 89
+  },
+  "skip_stats": {
+    "adversarial": 446,
+    "noEvidence": 4,
+    "unresolved": 5,
+    "unknownCat": 0
+  },
+  "paper_total_claim": 1540,
+  "actual_count": 1531,
+  "count_matches_paper": false
+}

benchmarks/data/longmemeval/longmemeval.meta.json

@@ -0,0 +1,42 @@
+{
+  "dataset_version": "a8a99545d77a236e3c7aa1f5d0ccfd94d4bcc5c2d5adbd19f938aba844586c56",
+  "instance_count": 500,
+  "variant": "s",
+  "built_at": "2026-06-04T17:25:36.563Z",
+  "source": "https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json",
+  "source_reference": "Wu et al. 2024, \"LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory\" (arXiv:2410.10813)",
+  "hf_repo": "xiaowu0162/longmemeval-cleaned",
+  "canonicalisation": {
+    "abstention_included": true,
+    "no_sessions_excluded": true,
+    "sort_order": "instance_id ascending",
+    "field_order": [
+      "instance_id",
+      "conversation_id",
+      "question",
+      "expected",
+      "context",
+      "question_type",
+      "is_abstention"
+    ],
+    "line_terminator": "\\n",
+    "trailing_newline": true,
+    "encoding": "utf-8",
+    "no_bom": true
+  },
+  "distribution_by_question_type": {
+    "single-session-user": 70,
+    "single-session-assistant": 56,
+    "single-session-preference": 30,
+    "temporal-reasoning": 133,
+    "knowledge-update": 78,
+    "multi-session": 133
+  },
+  "abstention_count": 30,
+  "skip_stats": {
+    "missingFields": 0,
+    "noSessions": 0
+  },
+  "expected_count": 500,
+  "count_matches_expected": true
+}

benchmarks/data/preflight-locomo-50.json

@@ -0,0 +1,918 @@
+{
+  "_meta": {
+    "description": "Stage 2 preflight 4-cell sample lock. Istih 50 LoCoMo instanci preko sva 4 ćelije (raw / memory-only / evolve-only / full-stack).",
+    "brief": "PM-Waggle-OS/briefs/2026-04-20-cc-preflight-prep-tasks.md Task 1",
+    "locked_decision": "decisions/2026-04-20-preflight-oq-resolutions-locked.md §OQ-PF-1",
+    "source": "https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json",
+    "source_reference": "Maharana et al. 2024, ACL-2024, \"Evaluating Very Long-Term Conversational Memory of LLM Agents\"",
+    "seed": 42,
+    "selection_algorithm": "Bucket LoCoMo qa entries by category (mapping verified via task_eval/evaluation.py + paper §4.1); within each category sort by instance_id ascending (canonical order), apply Fisher-Yates shuffle with xorshift32(seed=42), then take first N per category. Fisher-Yates PRNG is shared across buckets — key iteration order is fixed (single-hop, multi-hop, temporal, open-ended) to keep the selection stable against re-runs. instance_id = locomo_<sample_id>_q<3-digit qa-array index>.",
+    "distribution": {
+      "single-hop": 13,
+      "multi-hop": 13,
+      "temporal": 12,
+      "open-ended": 12
+    },
+    "total": 50,
+    "locomo_category_map": {
+      "1": "multi-hop",
+      "2": "temporal",
+      "3": "open-ended",
+      "4": "single-hop",
+      "5": "adversarial (excluded)"
+    },
+    "context_assembly": "Evidence dia_ids are grouped by session, prefixed with session_N_date_time for temporal anchoring, and rendered as \"speaker: text\" lines. Images are preserved via blip_caption tags."
+  },
+  "instances": [
+    {
+      "id": "locomo_conv-26_q110",
+      "category": "single-hop",
+      "context": "Session 8 (1:51 pm on 15 July, 2023):\nMelanie: The kids loved it! They were so excited to get their hands dirty and make something with clay. It was special to watch their creativity and imagination come to life, they made this! [image: a photo of a cup with a dog face on it]",
+      "question": "What kind of pot did Mel and her kids make with clay?",
+      "ground_truth_answer": "a cup with a dog face on it",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 110,
+        "locomo_category": 4,
+        "evidence": [
+          "D8:4"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-41_q104",
+      "category": "single-hop",
+      "context": "Session 17 (11:51 am on 3 June, 2023):\nJohn: Hey Maria, long time no talk! Life's been pretty wild lately. The toughest thing to deal with is that we had to say goodbye to Max. He was such an important part of our family for 10 years and it's so hard to think he's not here wagging that tail anymore.",
+      "question": "How long was Max a part of John's family?",
+      "ground_truth_answer": "10 years",
+      "locomo_metadata": {
+        "sample_id": "conv-41",
+        "qa_index": 104,
+        "locomo_category": 4,
+        "evidence": [
+          "D17:1"
+        ],
+        "speaker_a": "John",
+        "speaker_b": "Maria"
+      }
+    },
+    {
+      "id": "locomo_conv-44_q073",
+      "category": "single-hop",
+      "context": "Session 7 (11:27 am on 2 June, 2023):\nAndrew: I'm still on the hunt, but it's tough finding a pet-friendly spot in the city. Been checking out some places, but no luck so far. A bit discouraged but I'm determined to find the right place and dog.",
+      "question": "What challenge is Andrew facing in their search for a pet?",
+      "ground_truth_answer": "Finding a pet-friendly spot in the city",
+      "locomo_metadata": {
+        "sample_id": "conv-44",
+        "qa_index": 73,
+        "locomo_category": 4,
+        "evidence": [
+          "D7:8"
+        ],
+        "speaker_a": "Audrey",
+        "speaker_b": "Andrew"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q132",
+      "category": "single-hop",
+      "context": "Session 16 (12:09 am on 13 September, 2023):\nCaroline: Since I was 17 or so. I find it soempowering and cathartic. It's amazing how art can show things that are hard to put into words. How long have you been into art?",
+      "question": "How long has Melanie been creating art?",
+      "ground_truth_answer": "7 years",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 132,
+        "locomo_category": 4,
+        "evidence": [
+          "D16:7"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-42_q108",
+      "category": "single-hop",
+      "context": "Session 5 (6:59 pm on 18 March, 2022):\nNate: No, not really. Just keep their area clean, feed them properly, and make sure they get enough light. It's actually kind of fun.",
+      "question": "How does Nate describe the process of taking care of turtles?",
+      "ground_truth_answer": "Not tough; keep their area clean, feed them properly, give them enough light.",
+      "locomo_metadata": {
+        "sample_id": "conv-42",
+        "qa_index": 108,
+        "locomo_category": 4,
+        "evidence": [
+          "D5:8"
+        ],
+        "speaker_a": "Joanna",
+        "speaker_b": "Nate"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q151",
+      "category": "single-hop",
+      "context": "Session 19 (12:52 am on 19 August, 2023):\nDeborah: Reminds me of when I used to play games with my husband. We'd take turns and it was a great way to bond and make memories. Gaming really can bring people closer, right?",
+      "question": "What did Deborah and her husband use to play to bond and make memories?",
+      "ground_truth_answer": "video games",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 151,
+        "locomo_category": 4,
+        "evidence": [
+          "D19:11"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-47_q123",
+      "category": "single-hop",
+      "context": "Session 23 (9:23 pm on 4 September, 2022):\nJames: At only $10 per class, it's very cheap! Also, I made meringue there and they taught us how to make the dough.",
+      "question": "What did James learn to make in the cooking class besides omelette and meringue?",
+      "ground_truth_answer": "Dough",
+      "locomo_metadata": {
+        "sample_id": "conv-47",
+        "qa_index": 123,
+        "locomo_category": 4,
+        "evidence": [
+          "D23:15"
+        ],
+        "speaker_a": "James",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-44_q067",
+      "category": "single-hop",
+      "context": "Session 5 (10:47 am on 6 May, 2023):\nAndrew: I'm looking for a place near a park or woods, so I can stay close to nature and give the dog a large open space to run around",
+      "question": "Where does Andrew want to live to give their dog a large, open space to run around?",
+      "ground_truth_answer": "near a park or woods",
+      "locomo_metadata": {
+        "sample_id": "conv-44",
+        "qa_index": 67,
+        "locomo_category": 4,
+        "evidence": [
+          "D5:7"
+        ],
+        "speaker_a": "Audrey",
+        "speaker_b": "Andrew"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q129",
+      "category": "single-hop",
+      "context": "Session 12 (4:30 pm on 9 April, 2023):\nDeborah: Hey Jolene! Great to see you! Had a blast biking nearby with my neighbor last week - was so freeing and beautiful. Checked out an art show with a friend today - really cool and inspiring stuff. Reminded me of my mom. [image: a photo of a large brown and white photo of a person]",
+      "question": "Which show did Deborah go to with a friend on 9 April, 2023?",
+      "ground_truth_answer": "an art show",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 129,
+        "locomo_category": 4,
+        "evidence": [
+          "D12:1"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q109",
+      "category": "single-hop",
+      "context": "Session 18 (10:56 am on 13 September, 2023):\nCalvin: Thanks Dave! Lots of cool stuff happening. Next up, a tour - so excited! After that, I'm off to explore and grow my brand. Can't wait to see what the future has in store!",
+      "question": "What is Calvin excited about after the tour?",
+      "ground_truth_answer": "exploring and growing his brand",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 109,
+        "locomo_category": 4,
+        "evidence": [
+          "D18:7"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-47_q103",
+      "category": "single-hop",
+      "context": "Session 16 (5:13 pm on 9 July, 2022):\nJames: Yeah, staying balanced can be tough. I'm trying to take breaks from my hobbies and do other things. Lately I've become interested in extreme sports. Yesterday, for example, I was doing rope jumping. The highest height I jumped from was 150 meters!",
+      "question": "What new hobby did James become interested in on 9 July, 2022?",
+      "ground_truth_answer": "Extreme sports",
+      "locomo_metadata": {
+        "sample_id": "conv-47",
+        "qa_index": 103,
+        "locomo_category": 4,
+        "evidence": [
+          "D16:5"
+        ],
+        "speaker_a": "James",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-42_q193",
+      "category": "single-hop",
+      "context": "Session 28 (5:54 pm on 9 November, 2022):\nJoanna: Thanks, Nate! I took that pic on a hike last summer near Fort Wayne. The sunset and the surrounding beauty were just incredible. It was an awesome reminder of nature's beauty.",
+      "question": "What inspired Joanna to take a picture of the sunset in the field near Fort Wayne?",
+      "ground_truth_answer": "The incredible sunset and surrounding beauty",
+      "locomo_metadata": {
+        "sample_id": "conv-42",
+        "qa_index": 193,
+        "locomo_category": 4,
+        "evidence": [
+          "D28:22"
+        ],
+        "speaker_a": "Joanna",
+        "speaker_b": "Nate"
+      }
+    },
+    {
+      "id": "locomo_conv-49_q139",
+      "category": "single-hop",
+      "context": "Session 21 (4:25 pm on 26 December, 2023):\nSam: Ooh, Evan, I'd put a GPS sensor on your keys!",
+      "question": "What did Sam suggest Evan should do with his keys?",
+      "ground_truth_answer": "put a GPS sensor on them",
+      "locomo_metadata": {
+        "sample_id": "conv-49",
+        "qa_index": 139,
+        "locomo_category": 4,
+        "evidence": [
+          "D21:21"
+        ],
+        "speaker_a": "Evan",
+        "speaker_b": "Sam"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q039",
+      "category": "multi-hop",
+      "context": "Session 5 (1:36 pm on 3 July, 2023):\nCaroline: Since we last spoke, some big things have happened. Last week I went to an LGBTQ+ pride parade. Everyone was so happy and it made me feel like I belonged. It showed me how much our community has grown, it was amazing!\n\nSession 9 (2:31 pm on 17 July, 2023):\nCaroline: Yay! Next month I'm having an LGBTQ art show with my paintings - can't wait!\nCaroline: Hey Melanie! That sounds great! Last weekend I joined a mentorship program for LGBTQ youth - it's really rewarding to help the community.\n\nSession 10 (8:56 pm on 20 July, 2023):\nCaroline: Hey Mel! A lot's happened since we last chatted - I just joined a new LGBTQ activist group last Tues. I'm meeting so many cool people who are as passionate as I am about rights and community support. I'm giving my voice and making a real difference, plus it's fulfilling in so many ways. It's just great, you know?",
+      "question": "In what ways is Caroline participating in the LGBTQ community?",
+      "ground_truth_answer": "Joining activist group, going to pride parades, participating in an art show, mentoring program",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 39,
+        "locomo_category": 1,
+        "evidence": [
+          "D10:3",
+          "D5:1",
+          "D9:12",
+          "D9:2"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-30_q031",
+      "category": "multi-hop",
+      "context": "Session 1 (4:04 pm on 20 January, 2023):\nJon: Hey Gina! Good to see you too. Lost my job as a banker yesterday, so I'm gonna take a shot at starting my own business.\n\nSession 15 (10:04 am on 19 June, 2023):\nJon: Yeah! Let's make some awesome memories tomorrow at the grand opening! [image: a photo of a man in a native costume is giving another man a high five]",
+      "question": "How long did it take for Jon to open his studio?",
+      "ground_truth_answer": "six months",
+      "locomo_metadata": {
+        "sample_id": "conv-30",
+        "qa_index": 31,
+        "locomo_category": 1,
+        "evidence": [
+          "D1:2",
+          "D15:13"
+        ],
+        "speaker_a": "Jon",
+        "speaker_b": "Gina"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q005",
+      "category": "multi-hop",
+      "context": "Session 4 (6:24 pm on 1 May, 2023):\nDave: Thanks! Appreciate the support. My dream was to open a shop and it's a step towards my other dream of working on classic cars. I love their design and engineering.\nDave: Thanks! Appreciate the support. My dream was to open a shop and it's a step towards my other dream of working on classic cars. I love their design and engineering.\n\nSession 5 (1:16 pm on 3 May, 2023):\nDave: Thanks Calvin! Appreciate the support. I'm gonna keep learning more about auto engineering, maybe even build a custom car from scratch someday - that's the dream! For now, just gonna keep working on this project and assisting customers.",
+      "question": "What are Dave's dreams?",
+      "ground_truth_answer": "open a car maintenance shop, work on classic cars, build a custom car from scratch",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 5,
+        "locomo_category": 1,
+        "evidence": [
+          "D4:5",
+          "D4:5",
+          "D5:5"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q018",
+      "category": "multi-hop",
+      "context": "Session 6 (11:50 am on 16 May, 2023):\nCalvin: Thanks, Dave. Appreciate the support. Waiting on insurance to kick in so I can start repairs - fingers crossed it won't take too long. Take a look at my creative haven, where I pour my heart into the music. [image: a photo of a music studio with a keyboard, synthesizer, and other musical equipment]\n\nSession 9 (3:15 pm on 21 June, 2023):\nCalvin: Hi Dave! Nice to hear from you. Since last chat, some things have happened. I had a lil car accident last Friday, kinda upsetting but no one was hurt. I've been dealing with insurance and repairs - taking up a lot of time and energy. Look at this photo! [image: a photo of a red car with a black rim parked on a sidewalk]",
+      "question": "How many times has Calvin had to deal with insurance paperwork?",
+      "ground_truth_answer": "two times",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 18,
+        "locomo_category": 1,
+        "evidence": [
+          "D6:5",
+          "D9:1"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q080",
+      "category": "multi-hop",
+      "context": "Session 10 (5:35 pm on 22 March, 2023):\nJolene: Definitely! I've been gathering information, watching videos, and I even got a beginners' guide to surfing. Just need to find the right time and place to get a lesson.\n\nSession 29 (1:24 pm on 17 September, 2023):\nJolene: How cool! But I never decided to try it.\nJolene: Just started learning, but haven't gone yet. Want to come with me sometime?",
+      "question": "Has Jolene tried surfing?",
+      "ground_truth_answer": "no",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 80,
+        "locomo_category": 1,
+        "evidence": [
+          "D10:20",
+          "D29:26",
+          "D29:30"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-47_q009",
+      "category": "multi-hop",
+      "context": "Session 1 (3:47 pm on 17 March, 2022):\nJames: Max and Daisy. Will be actually cool to build an app for dog walking and pet care. The goal is to connect pet owners with reliable dog walkers and provide helpful information on pet care.\n\nSession 5 (9:52 am on 12 April, 2022):\nJames: Hey John! Long time no chat - I adopted a pup from a shelter in Stamford last week and my days have been so much happier with him in the fam. I named it Ned. Any progress on your gaming goals? [image: a photo of a dog and a cat sitting on a dog bed]",
+      "question": "What are the names of James's dogs?",
+      "ground_truth_answer": "Ned, Daisy, Max",
+      "locomo_metadata": {
+        "sample_id": "conv-47",
+        "qa_index": 9,
+        "locomo_category": 1,
+        "evidence": [
+          "D1:14",
+          "D5:1"
+        ],
+        "speaker_a": "James",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q019",
+      "category": "multi-hop",
+      "context": "Session 3 (4:15 pm on 20 April, 2023):\nCalvin: Hey Dave! Long time no see. I just went to an awesome music thingy in Tokyo - so cool!\n\nSession 12 (1:12 pm on 3 August, 2023):\nCalvin: Aww, that's cool, Dave. Reminiscing is always fun! That pic you shared takes me back to my trip to the Ferrari dealership. I saw a lot of amazing cars, but as for me, my car is the best and  I'm pretty proud of this. Sure, it's just material, but it reminds me of my hard work and dedication. It really inspires me. Take a look at this beauty! [image: a photography of a red car is lifted on a lift in a garage]\n\nSession 24 (10:11 am on 19 October, 2023):\nCalvin: Yeah definitely! Shibuya Crossing is like Tokyo's Times Square, and I was excited to explore Shinjuku. Plus, there's amazing food there, can't wait to try it again! Look at the photo of Shibuya Crossing at night, that's amazing! [image: a photo of a crowd of people with umbrellas in the rain]",
+      "question": "Which places or events has Calvin visited in Tokyo?",
+      "ground_truth_answer": "music festival, car museum, Shibuya crossing, Shinjuku",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 19,
+        "locomo_category": 1,
+        "evidence": [
+          "D3:1",
+          "D12:7",
+          "D24:19"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q001",
+      "category": "multi-hop",
+      "context": "Session 1 (4:06 pm on 23 January, 2023):\nDeborah: It was full of memories, she passed away a few years ago. This is our last photo together. [image: a photo of a woman in a wheelchair hugging a woman in a wheelchair]\n\nSession 2 (9:49 am on 27 January, 2023):\nDeborah: Hey Jolene, sorry to tell you this but my dad passed away two days ago. It's been really tough on us all - his sudden death left us all kinda shell-shocked. I'm trying to channel my grief by spending more time with family and cherishing the memories. These moments remind me to live life fully. [image: a photo of a woman hugging a woman who is sitting on a couch]\n\nSession 6 (4:12 pm on 22 February, 2023):\nDeborah: The roses and dahlias bring me peace. I lost a friend last week, so I've been spending time in the garden to find some comfort.",
+      "question": "Which of Deborah`s family and friends have passed away?",
+      "ground_truth_answer": "mother, father, her friend Karlie",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 1,
+        "locomo_category": 1,
+        "evidence": [
+          "D1:5",
+          "D2:1",
+          "D6:4"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q033",
+      "category": "multi-hop",
+      "context": "Session 14 (12:35 am on 14 August, 2023):\nDave: Hey Cal, how's it going? Something cool happened since last we talked - I got to go to a car workshop in San Francisco! So cool to dive into the world of car restoration and see all the different techniques. People were really passionate and dedicated - truly inspiring!\n\nSession 17 (9:19 am on 2 September, 2023):\nDave: Hey Calvin! Been a while, what's up? I'm tied up with car stuff lately, yesterday I came back from San Francsico with some great insights and knowledge on car modification that I want to share with you! Changing things around, and giving an old car a new life - so satisfying!",
+      "question": "What was Dave doing in San Francisco?",
+      "ground_truth_answer": "attending a car modification workshop",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 33,
+        "locomo_category": 1,
+        "evidence": [
+          "D17:1",
+          "D14:1"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-43_q021",
+      "category": "multi-hop",
+      "context": "Session 3 (4:21 pm on 16 July, 2023):\nJohn: It's Seattle, I'm stoked for my game there next month! It's one of my favorite cities to explore - super vibrant! [image: a photo of a crowd of people watching a basketball game]\n\nSession 6 (1:08 pm on 11 August, 2023):\nJohn: I was in Chicago, it was awesome! It had so much energy and the locals were really friendly. It's great to experience other cultures and connect with new folks.\n\nSession 9 (6:59 pm on 26 August, 2023):\nJohn: Wow, Tim, that's an awesome book collection! It's cool to escape to different worlds with a hobby. By the way, I love discovering new cities - check out this pic from one of my trips to New York City! [image: a photo of a cityscape with a view of a skyscraper]",
+      "question": "Which US cities does John mention visiting to Tim?",
+      "ground_truth_answer": "Seattle, Chicago, New York",
+      "locomo_metadata": {
+        "sample_id": "conv-43",
+        "qa_index": 21,
+        "locomo_category": 1,
+        "evidence": [
+          "D3:19",
+          "D6:3",
+          "D9:6"
+        ],
+        "speaker_a": "Tim",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q063",
+      "category": "multi-hop",
+      "context": "Session 16 (2:55 pm on 31 August, 2023):\nDave: Sure, let me know when, I'm here to lend a hand. It's great to fuel your ideas. Remember that photo you sent me once? Love how this guitar shows our different artistic styles. [image: a photo of a guitar with a octopus on it]\nCalvin: Performing on such a big stage was a dream come true! The energy was incredible and I felt on top of the world. It was seriously surreal.\nCalvin: Yeah, Dave! It's like every mark and strum holds a story. Take a look. [image: a photo of a guitar with a purple glow on it]\nDave: That guitar has a gorgeous purple hue. Why did you make it so shiny?",
+      "question": "What style of guitars does Calvin own?",
+      "ground_truth_answer": "custom-made yellow guitar with an octopus on it, shiny purple guitar",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 63,
+        "locomo_category": 1,
+        "evidence": [
+          "D16:13",
+          "D16:4",
+          "D16:18",
+          "D16:19"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-41_q023",
+      "category": "multi-hop",
+      "context": "Session 2 (6:10 pm on 22 December, 2022):\nMaria: I can picture you all laughing and having a blast making your own pizzas - a great way to bond! I made some peach cobbler recently, it was great.\n\nSession 13 (3:18 pm on 4 May, 2023):\nMaria: Thanks, John! It had lots of great things like salads, sandwiches, and homemade desserts. My favorite is the amazing banana split sundae - I enjoy it after a day of volunteering. It's a little moment of joy in life - balance is key!",
+      "question": "What desserts has Maria made?",
+      "ground_truth_answer": "Banana split sundae, Peach cobbler",
+      "locomo_metadata": {
+        "sample_id": "conv-41",
+        "qa_index": 23,
+        "locomo_category": 1,
+        "evidence": [
+          "D2:25",
+          "D13:18"
+        ],
+        "speaker_a": "John",
+        "speaker_b": "Maria"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q013",
+      "category": "multi-hop",
+      "context": "Session 2 (9:49 am on 27 January, 2023):\nDeborah: That's my old home. I go there now and then for my mom, who passed away. Sitting in that spot by the window gives me peace.\n\nSession 4 (9:48 am on 4 February, 2023):\nDeborah: That sounds great, Jolene. Nature's calming for sure. Guess it helps us forget the daily craziness and find inner peace. No wonder you're a fan! I like to come to this spot by the water near my mom's old house. It's where I reflect on her life and find some peace. Being surrounded by nature helps a lot. [image: a photo of a small island with a lone boat in the water]\n\nSession 6 (4:12 pm on 22 February, 2023):\nDeborah: I've been blessed to travel to a few places and Bali last year was one of my favs. It was a gorgeous island that gave me peace, great for yoga. [image: a photo of a swing on a beach with a blue sky]\n\nSession 19 (12:52 am on 19 August, 2023):\nDeborah: I love going to this park near my house - it has a nice forest trail and a beach. It's a peaceful spot where I can do some yoga and reflect. There's also a special bench that holds special meaning to me.",
+      "question": "What places give Deborah peace?",
+      "ground_truth_answer": "sitting in a spot by the window in her Mom's house, sitting by the beach, Bali, forest trail in a nearby park",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 13,
+        "locomo_category": 1,
+        "evidence": [
+          "D2:13",
+          "D4:34",
+          "D6:10",
+          "D19:17"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-44_q047",
+      "category": "temporal",
+      "context": "Session 24 (6:12 pm on 19 October, 2023):\nAndrew: Hi Audrey! Pets really can make our lives better, huh? Speaking of which, I've got some awesome news -- I recently adopted another pup from a shelter. He's the best. [image: a photo of a dog sitting on a couch with a bowl of food]\n\nSession 28 (9:02 am on 22 November, 2023):\nAndrew: No, we haven't got the chance to take them to the groomer yet. But will do that soon! So guess what, I can't help myself but to adpot another dog the other day. Here's a photo of the doggo! [image: a photo of a woman feeding a dog hay in a barn]",
+      "question": "How many months passed between Andrew adopting Buddy and Scout",
+      "ground_truth_answer": "one month",
+      "locomo_metadata": {
+        "sample_id": "conv-44",
+        "qa_index": 47,
+        "locomo_category": 2,
+        "evidence": [
+          "D24:2",
+          "D28:6"
+        ],
+        "speaker_a": "Audrey",
+        "speaker_b": "Andrew"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q021",
+      "category": "temporal",
+      "context": "Session 6 (8:18 pm on 6 July, 2023):\nCaroline: Wow, that's great! It sure shows how important friendship and compassion are. It's made me appreciate how lucky I am to have my friends and family helping with my transition. They make all the difference. We even had a picnic last week! [image: a photo of a group of women sitting on a blanket in a park]",
+      "question": "When did Caroline have a picnic?",
+      "ground_truth_answer": "The week before 6 July 2023",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 21,
+        "locomo_category": 2,
+        "evidence": [
+          "D6:11"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q021",
+      "category": "temporal",
+      "context": "Session 2 (9:49 am on 27 January, 2023):\nJolene: We are planning to play \"Walking Dead\" next Saturday.",
+      "question": "When do Jolene and her partner plan to complete the game \"Walking Dead\"?",
+      "ground_truth_answer": "Saturday after 27 January, 2023",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 21,
+        "locomo_category": 2,
+        "evidence": [
+          "D2:30"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-30_q010",
+      "category": "temporal",
+      "context": "Session 5 (9:32 am on 8 February, 2023):\nGina: That's awesome! I'm sure you feel great knowing your students are doing so well with dance. It's amazing what it can do for people! Oh, and btw - I've been working hard on my online store and just teamed up with a local artist for some cool designs. Check 'em out! [image: a photo of a rack of clothes with a coat and a dress]",
+      "question": "When did Gina team up with a local artist for some cool designs?",
+      "ground_truth_answer": "February, 2023",
+      "locomo_metadata": {
+        "sample_id": "conv-30",
+        "qa_index": 10,
+        "locomo_category": 2,
+        "evidence": [
+          "D5:5"
+        ],
+        "speaker_a": "Jon",
+        "speaker_b": "Gina"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q073",
+      "category": "temporal",
+      "context": "Session 17 (10:31 am on 13 October, 2023):\nMelanie: Thanks for the tip, Caroline. Doing research and readying myself emotionally makes sense. I'll do that. BTW, recently I had a setback. Last month I got hurt and had to take a break from pottery, which I use for self-expression and peace.",
+      "question": "When did Melanie get hurt?",
+      "ground_truth_answer": "September 2023",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 73,
+        "locomo_category": 2,
+        "evidence": [
+          "D17:8"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-30_q033",
+      "category": "temporal",
+      "context": "Session 16 (2:15 pm on 21 June, 2023):\nJon: That's awesome, Gina! Yesterday I chose to go to networking events to make things happen. It's been tough but I'm staying determined and focused.",
+      "question": "When did Jon visit networking events for his store?",
+      "ground_truth_answer": "20 June, 2023",
+      "locomo_metadata": {
+        "sample_id": "conv-30",
+        "qa_index": 33,
+        "locomo_category": 2,
+        "evidence": [
+          "D16:6"
+        ],
+        "speaker_a": "Jon",
+        "speaker_b": "Gina"
+      }
+    },
+    {
+      "id": "locomo_conv-44_q001",
+      "category": "temporal",
+      "context": "Session 1 (1:10 pm on 27 March, 2023):\nAndrew: Hey Audrey! So, I started a new job as a Financial Analyst last week - it's been quite a change from my previous job. How about you? Anything interesting happening?",
+      "question": "When did Andrew start his new job as a financial analyst?",
+      "ground_truth_answer": "The week before March 27, 2023",
+      "locomo_metadata": {
+        "sample_id": "conv-44",
+        "qa_index": 1,
+        "locomo_category": 2,
+        "evidence": [
+          "D1:2"
+        ],
+        "speaker_a": "Audrey",
+        "speaker_b": "Andrew"
+      }
+    },
+    {
+      "id": "locomo_conv-49_q079",
+      "category": "temporal",
+      "context": "Session 23 (1:32 pm on 6 January, 2024):\nEvan: Thanks Sam! We're off to Canada next month for our honeymoon. So excited to create some awesome memories. Looking forward to exploring the beautiful snowy landscapes there. [image: a photo of a stream running through a snowy forest filled with snow]",
+      "question": "When will Evan and his partner have their honeymoon in Canada?",
+      "ground_truth_answer": "February 2024",
+      "locomo_metadata": {
+        "sample_id": "conv-49",
+        "qa_index": 79,
+        "locomo_category": 2,
+        "evidence": [
+          "D23:23"
+        ],
+        "speaker_a": "Evan",
+        "speaker_b": "Sam"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q010",
+      "category": "temporal",
+      "context": "Session 3 (7:55 pm on 9 June, 2023):\nCaroline: Yeah, I'm really lucky to have them. They've been there through everything, I've known these friends for 4 years, since I moved from my home country. Their love and help have been so important especially after that tough breakup. I'm super thankful. Who supports you, Mel?",
+      "question": "How long has Caroline had her current group of friends for?",
+      "ground_truth_answer": "4 years",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 10,
+        "locomo_category": 2,
+        "evidence": [
+          "D3:13"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-49_q058",
+      "category": "temporal",
+      "context": "Session 12 (3:09 pm on 8 October, 2023):\nEvan: Hey Sam, tough news. Yeah, our health can really put a damper on things. I started lifting weights one year ago and it's been a journey. It was a struggle at first, but I'm seeing some gains. You interested in trying it out? [image: a photo of a man doing a squat on a machine in a gym]",
+      "question": "When did Evan start lifting weights?",
+      "ground_truth_answer": "October 2022",
+      "locomo_metadata": {
+        "sample_id": "conv-49",
+        "qa_index": 58,
+        "locomo_category": 2,
+        "evidence": [
+          "D12:2"
+        ],
+        "speaker_a": "Evan",
+        "speaker_b": "Sam"
+      }
+    },
+    {
+      "id": "locomo_conv-43_q037",
+      "category": "temporal",
+      "context": "Session 13 (1:50 pm on 13 October, 2023):\nTim: Hey John! It's been ages since we last talked. Guess what? Last week I went to a Harry Potter conference in the UK - it was incredible! There were so many people who shared the same love of HP as me, it was like a magical family. I felt so inspired and like I got a new lease of life. I love how my passion for fantasy stuff brings me closer to people from all over the world, it's pretty cool.",
+      "question": "Which week did Tim visit the UK for the Harry Potter Conference?",
+      "ground_truth_answer": "The week before October 13th, 2023.",
+      "locomo_metadata": {
+        "sample_id": "conv-43",
+        "qa_index": 37,
+        "locomo_category": 2,
+        "evidence": [
+          "D13:1"
+        ],
+        "speaker_a": "Tim",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-50_q027",
+      "category": "temporal",
+      "context": "Session 16 (2:55 pm on 31 August, 2023):\nCalvin: Cool! Last weekend I started shooting a video for my new album - can't wait for you to check it out! Look how great it was! [image: a photo of a camera and a video camera on a beach]",
+      "question": "Which city was Calvin visiting in August 2023?",
+      "ground_truth_answer": "Miami",
+      "locomo_metadata": {
+        "sample_id": "conv-50",
+        "qa_index": 27,
+        "locomo_category": 2,
+        "evidence": [
+          "D16:6"
+        ],
+        "speaker_a": "Calvin",
+        "speaker_b": "Dave"
+      }
+    },
+    {
+      "id": "locomo_conv-43_q051",
+      "category": "open-ended",
+      "context": "Session 20 (9:52 am on 1 December, 2023):\nJohn: Hi Tim! Congrats on your success! Keep it up, you're doing great! I'm also trying out yoga to get a little extra strength and flexibility. It's challenging but worth it. [image: a photo of a white wall with a black lettering that says 30 positive suites]",
+      "question": "What kind of yoga for building core strength might John benefit from?",
+      "ground_truth_answer": "Hatha Yoga",
+      "locomo_metadata": {
+        "sample_id": "conv-43",
+        "qa_index": 51,
+        "locomo_category": 3,
+        "evidence": [
+          "D20:2"
+        ],
+        "speaker_a": "Tim",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-42_q014",
+      "category": "open-ended",
+      "context": "Session 7 (7:37 pm on 15 April, 2022):\nNate: Hey Jo, guess what I did? Dyed my hair last week - come see!",
+      "question": "What nickname does Nate use for Joanna?",
+      "ground_truth_answer": "Jo",
+      "locomo_metadata": {
+        "sample_id": "conv-42",
+        "qa_index": 14,
+        "locomo_category": 3,
+        "evidence": [
+          "D7:1"
+        ],
+        "speaker_a": "Joanna",
+        "speaker_b": "Nate"
+      }
+    },
+    {
+      "id": "locomo_conv-47_q016",
+      "category": "open-ended",
+      "context": "Session 8 (2:36 pm on 29 April, 2022):\nJohn: Yes, we played one game, but I forgot its name. Perhaps you know this game. There were multi-colored cards with numbers. You can only place a card with the same color or number on your opponent's card. Sometimes you trade cards, sometimes you need to draw a few extra from the deck or skip a turn.",
+      "question": "What is the game with different colored cards that was John talking about with James?",
+      "ground_truth_answer": "UNO",
+      "locomo_metadata": {
+        "sample_id": "conv-47",
+        "qa_index": 16,
+        "locomo_category": 3,
+        "evidence": [
+          "D8:34"
+        ],
+        "speaker_a": "James",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q069",
+      "category": "open-ended",
+      "context": "Session 7 (4:33 pm on 12 July, 2023):\nMelanie: Wow, Caroline. We've come so far, but there's more to do. Your drive to help is awesome! What's your plan to pitch in?\n\nSession 13 (3:31 pm on 23 August, 2023):\nMelanie: Wow, Caroline! That's amazing. You really care about being real and helping others. Wishing you the best on your adoption journey!\n\nSession 16 (12:09 am on 13 September, 2023):\nMelanie: The sign was just a precaution, I had a great time. But thank you for your concern, you're so thoughtful!",
+      "question": "What personality traits might Melanie say Caroline has?",
+      "ground_truth_answer": "Thoughtful, authentic, driven",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 69,
+        "locomo_category": 3,
+        "evidence": [
+          "D16:18",
+          "D13:16",
+          "D7:4"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    },
+    {
+      "id": "locomo_conv-42_q073",
+      "category": "open-ended",
+      "context": "Session 28 (5:54 pm on 9 November, 2022):\nJoanna: Thanks, Nate! I took that pic on a hike last summer near Fort Wayne. The sunset and the surrounding beauty were just incredible. It was an awesome reminder of nature's beauty.",
+      "question": "What state did Joanna visit in summer 2021?",
+      "ground_truth_answer": "Indiana",
+      "locomo_metadata": {
+        "sample_id": "conv-42",
+        "qa_index": 73,
+        "locomo_category": 3,
+        "evidence": [
+          "D28:22"
+        ],
+        "speaker_a": "Joanna",
+        "speaker_b": "Nate"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q005",
+      "category": "open-ended",
+      "context": "Session 1 (4:06 pm on 23 January, 2023):\nJolene: Staying connected is super important. Do you have something to remember her by? This pendant reminds me of my mother, she gave it to me in 2010  in Paris. [image: a photo of a heart shaped pendant with a bird on it]",
+      "question": "In what country did Jolene's mother buy her the pendant?",
+      "ground_truth_answer": "In France",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 5,
+        "locomo_category": 3,
+        "evidence": [
+          "D1:8"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-49_q019",
+      "category": "open-ended",
+      "context": "Session 3 (3:55 pm on 6 June, 2023):\nSam: Yeah, you're right. It takes time, but I'm up for keep trying and making those tiny changes.\nEvan: Sure Sam, I'd be glad to help. Let's get together and I'll show you some basic exercises. We'll reach our goals!\n\nSession 8 (6:17 pm on 19 August, 2023):\nSam: Cool, Evan! What have you been learning in those classes?\nEvan: Thanks Sam! I aim to capture the vibe of nature in my paintings, conveying the peacefulness of being outdoors. [image: a photo of a tree with pink flowers in a field]\n\nSession 9 (10:18 am on 27 August, 2023):\nEvan: Yeah, thanks for the push, Sam. Oh, hey, I came across this pic when I was tidying up. I took a road trip last month - the scenery was stunning and nature really chilled me out. [image: a photography of a car is parked near a lake with mountains in the background]\nSam: I haven't gone on a road trip in ages, but I love being surrounded by nature. It's so tranquil and refreshing. I'd love to go hiking more, but it can be a bit challenging sometimes. However, I am working on becoming healthier, so maybe a road trip and a hike will be possible soon.\n\nSession 12 (3:09 pm on 8 October, 2023):\nSam: Thanks, Evan. I appreciate your support. It really means a lot to me. I'll definitely keep you posted on my progress.\nSam: Wow, Evan, that's really inspiring. Gonna keep believing in it!\n\nSession 14 (1:50 pm on 17 October, 2023):\nSam: Sounds awesome, Evan! Can't wait to go on a hike with you. It's going to be a fun challenge and a great opportunity to appreciate the beauty of nature.\nEvan: Sure thing! Our hike is going to be awesome, I can tell. I'm always here to support you.\n\nSession 22 (11:00 am on 31 December, 2023):\nSam: Hey Evan! I’m really getting into this healthier lifestyle—just took my friends on an epic hiking trip last Friday! [image: a photography of a man standing on a rock looking out over a valley]",
+      "question": "Considering their conversations and personal growth, what advice might Evan and Sam give to someone facing a major life transition or challenge?",
+      "ground_truth_answer": "Evan and Sam would likely advise embracing small, consistent changes​​, finding stress-relieving activities like hiking​​, painting, and road trips​​, and the importance of friendship and support in navigating challenges​​.",
+      "locomo_metadata": {
+        "sample_id": "conv-49",
+        "qa_index": 19,
+        "locomo_category": 3,
+        "evidence": [
+          "D3:10",
+          "D3:15",
+          "D22:1",
+          "D8:17",
+          "D8:22",
+          "D9:8",
+          "D9:11",
+          "D14:7",
+          "D14:12",
+          "D12:7",
+          "D12:11"
+        ],
+        "speaker_a": "Evan",
+        "speaker_b": "Sam"
+      }
+    },
+    {
+      "id": "locomo_conv-43_q015",
+      "category": "open-ended",
+      "context": "Session 4 (4:17 pm on 2 August, 2023):\nJohn: Oh yeah, I remember you telling me about Harry Potter! I've got a funny story. Anthony and I went to this charity thing and ended up in this intense Harry Potter trivia contest. We did alright, but there was this one super-nerd there that took home this as a prize.\n [image: a photo of a book with a picture of a person holding a bookmark]",
+      "question": "Who is Anthony?",
+      "ground_truth_answer": "likely John's friend, colleague or family",
+      "locomo_metadata": {
+        "sample_id": "conv-43",
+        "qa_index": 15,
+        "locomo_category": 3,
+        "evidence": [
+          "D4:8"
+        ],
+        "speaker_a": "Tim",
+        "speaker_b": "John"
+      }
+    },
+    {
+      "id": "locomo_conv-48_q040",
+      "category": "open-ended",
+      "context": "Session 10 (5:35 pm on 22 March, 2023):\nDeborah: Don't forget to take it easy and look after yourself. Wishing you all the best! Recently, Anna and I were sitting by the sea, watching the sunset and talking about each other. And we realized that we inspire each other. What thoughts does the sea in this photo make you think of? [image: a photo of a person walking on the beach with a surfboard]",
+      "question": "Does Deborah live close to the beach or the mountains?",
+      "ground_truth_answer": "beach",
+      "locomo_metadata": {
+        "sample_id": "conv-48",
+        "qa_index": 40,
+        "locomo_category": 3,
+        "evidence": [
+          "D10:17"
+        ],
+        "speaker_a": "Deborah",
+        "speaker_b": "Jolene"
+      }
+    },
+    {
+      "id": "locomo_conv-41_q008",
+      "category": "open-ended",
+      "context": "Session 5 (1:17 pm on 28 January, 2023):\nJohn: It's definitely isn't, Maria. My kids have so much and others don't. We really need to do something about it.",
+      "question": "What might John's financial status be?",
+      "ground_truth_answer": "Middle-class or wealthy",
+      "locomo_metadata": {
+        "sample_id": "conv-41",
+        "qa_index": 8,
+        "locomo_category": 3,
+        "evidence": [
+          "D5:5"
+        ],
+        "speaker_a": "John",
+        "speaker_b": "Maria"
+      }
+    },
+    {
+      "id": "locomo_conv-41_q050",
+      "category": "open-ended",
+      "context": "Session 2 (6:10 pm on 22 December, 2022):\nJohn: Yeah, they are my rock in tough times and always cheer me on. I'm really thankful for their love. Family time means a lot to me. [image: a photography of a family enjoying a ride on a swing]\n\nSession 3 (8:30 pm on 1 January, 2023):\nJohn: We held some events and got to meet some people. We went to a homeless shelter to give out food and supplies. Seeing the smiles on their faces, we knew we made a real difference. We also organized a toy drive for kids in need. It was amazing seeing the community come together to spread some joy.\n\nSession 4 (7:06 pm on 9 January, 2023):\nJohn: I tried to stay calm and asked for assistance, which helped me handle the situation and make it back safely.\n\nSession 26 (1:59 pm on 31 July, 2023):\nJohn: It was chaotic when we arrived, but we pulled together. I got a surge of energy and purpose, and we were able to save a family from a burning building. It was wild, but knowing we made a difference made it worth it.",
+      "question": "What attributes describe John?",
+      "ground_truth_answer": "Selfless, family-oriented, passionate, rational",
+      "locomo_metadata": {
+        "sample_id": "conv-41",
+        "qa_index": 50,
+        "locomo_category": 3,
+        "evidence": [
+          "D26:6",
+          "D2:14",
+          "D3:5",
+          "D4:6"
+        ],
+        "speaker_a": "John",
+        "speaker_b": "Maria"
+      }
+    },
+    {
+      "id": "locomo_conv-26_q022",
+      "category": "open-ended",
+      "context": "Session 6 (8:18 pm on 6 July, 2023):\nCaroline: I've got lots of kids' books- classics, stories from different cultures, educational books, all of that. What's a favorite book you remember from your childhood?",
+      "question": "Would Caroline likely have Dr. Seuss books on her bookshelf?",
+      "ground_truth_answer": "Yes, since she collects classic children's books",
+      "locomo_metadata": {
+        "sample_id": "conv-26",
+        "qa_index": 22,
+        "locomo_category": 3,
+        "evidence": [
+          "D6:9"
+        ],
+        "speaker_a": "Caroline",
+        "speaker_b": "Melanie"
+      }
+    }
+  ]
+}

benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-A-oracle/final_state.jsonl

@@ -0,0 +1 @@
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benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-A-oracle/initial_state.jsonl

@@ -0,0 +1 @@
+{"start_time": 0, "time_increment_in_seconds": 1, "duration": null, "event_log": {"past_events": []}, "event_queue": {"future_events": []}, "current_time": 0, "apps": [{"app_name": "SandboxLocalFileSystem", "files": {"name": "", "type": "directory", "children": [{"name": "09a45055-0e1d-4d60-938a-f2245558efab.txt", "type": "file"}, {"name": "216779ee-cd9b-421f-b005-98c746797370.txt", "type": "file"}, {"name": "3fafa79b-3942-4ac0-af16-b7577825c215.txt", "type": "file"}, {"name": "74986a41-0b62-48dc-aee0-4bf9ce594572.txt", "type": "file"}, {"name": "a9101dd4-f925-469b-9092-e284f706563a.txt", "type": "file"}, {"name": "c3155da7-88f5-42cd-ae2d-1924ee9c26d5.txt", "type": "file"}, {"name": "c8b51dca-4048-4692-9fb8-fb939ea75100.txt", "type": "file"}, {"name": "d5e17ffc-84cd-4555-a13f-48541b85a45c.txt", "type": "file"}, {"name": "e0cdf73b-b20b-4c07-94a7-ba5af07812a0.txt", "type": "file"}, {"name": "edd2936c-dc00-45bf-a3ce-c83c6de3b351.txt", "type": "file"}, {"name": "llama.jpg", "type": "file"}]}, "tmpdir": "C:\\Users\\MARKOM~1\\AppData\\Local\\Temp\\are_simulation_sandbox_9ejpy6c0\\are_simulation_fs_sandbox_vn40bdw1"}, {"app_name": "AgentUserInterface", "messages": []}]}

benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-A-oracle/output.jsonl

@@ -0,0 +1 @@
+{"task_id": "scenario_find_image_file", "trace_id": null, "score": 1.0, "metadata": {"scenario_id": "scenario_find_image_file", "status": "success", "has_exception": false}}

benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-B-gaia2-mock-thread/benchmark_stats.json

@@ -0,0 +1,68 @@
+{
+  "metadata": {
+    "model": "meta-llama/llama3-70b-instruct",
+    "model_provider": "mock",
+    "timestamp": "2026-04-29T21:58:18.864353",
+    "report_version": "3.0"
+  },
+  "statistics": {
+    "per_capability": {
+      "mini_standard": {
+        "capability": "mini (standard)",
+        "total_runs": 3,
+        "validated_runs": 3,
+        "success_runs": 0,
+        "failed_runs": 3,
+        "exception_runs": 0,
+        "no_validation_runs": 0,
+        "success_rate": 0.0,
+        "success_rate_std": 0.0,
+        "success_rate_sem": 0.0,
+        "pass_at_k": 0,
+        "pass_at_k_percent": 0.0,
+        "pass_k": 0,
+        "pass_k_percent": 0.0,
+        "total_scenarios": 1,
+        "avg_run_duration": 0.0,
+        "avg_run_duration_std": 0.0
+      }
+    },
+    "global": {
+      "total_runs": 3,
+      "validated_runs": 3,
+      "success_runs": 0,
+      "failed_runs": 3,
+      "exception_runs": 0,
+      "no_validation_runs": 0,
+      "pass_at_k": 0,
+      "pass_at_k_percent": 0.0,
+      "pass_k": 0,
+      "pass_k_percent": 0.0,
+      "total_scenarios": 1,
+      "macro_success_rate": 0.0,
+      "macro_success_rate_std": 0.0,
+      "macro_success_rate_sem": 0.0,
+      "micro_success_rate": 0.0,
+      "micro_success_rate_std": 0.0,
+      "micro_success_rate_sem": 0.0,
+      "avg_run_duration": 0.0,
+      "avg_run_duration_std": 0.0,
+      "job_duration": 31.784090518951416
+    }
+  },
+  "run_configurations": [
+    {
+      "phase_name": "standard",
+      "config": "mini",
+      "a2a_app_prop": "0.0",
+      "has_tool_augmentation": "False",
+      "has_env_events": "False",
+      "total_runs": 3,
+      "validated_runs": 3,
+      "success_runs": 0,
+      "failed_runs": 3,
+      "exception_runs": 0,
+      "no_validation_runs": 0
+    }
+  ]
+}

benchmarks/gaia2/runs/smoke-c2-2026-04-30/smoke-B-gaia2-mock-thread/output.jsonl

@@ -0,0 +1,3 @@
+{"task_id": "scenario_universe_21_xvc7uo", "trace_id": null, "score": 0.0, "metadata": {"scenario_id": "scenario_universe_21_xvc7uo", "run_number": 1, "status": "failed", "has_exception": true, "exception_type": "AttributeError", "exception_message": "module 'signal' has no attribute 'SIGALRM'"}}
+{"task_id": "scenario_universe_21_xvc7uo", "trace_id": null, "score": 0.0, "metadata": {"scenario_id": "scenario_universe_21_xvc7uo", "run_number": 2, "status": "failed", "has_exception": true, "exception_type": "AttributeError", "exception_message": "module 'signal' has no attribute 'SIGALRM'"}}
+{"task_id": "scenario_universe_21_xvc7uo", "trace_id": null, "score": 0.0, "metadata": {"scenario_id": "scenario_universe_21_xvc7uo", "run_number": 3, "status": "failed", "has_exception": true, "exception_type": "AttributeError", "exception_message": "module 'signal' has no attribute 'SIGALRM'"}}

benchmarks/gepa/README.md

@@ -0,0 +1,117 @@
+# GEPA — Tier 2 Prompt-Shapes Evolution
+
+GEPA (Agrawal et al. — Genetic Evolutionary Prompt Adaptation) harness for
+evolving the per-model prompt-shape templates in `packages/agent/src/prompt-shapes/`.
+
+**Faza 1 (current):** proof-of-concept pilot. $100 cap. H3 cell only. 5 shapes ×
+3 candidates × 2 generations × N=8 evaluations.
+
+**Faza 2 (gated on Faza 1 PASS):** expansion to H2 + H4 cells, scale N to 20+,
+3-5 generations, separate ratification + brief.
+
+## Authority chain
+
+- PM brief: `D:/Projects/PM-Waggle-OS/briefs/2026-04-28-cc4-gepa-tier2-evolution-faza1-brief.md`
+- Pre-flight report: `D:/Projects/PM-Waggle-OS/briefs/2026-04-28-cc4-faza1-preflight-report.md`
+- Amendment 1: `D:/Projects/PM-Waggle-OS/briefs/2026-04-28-cc4-faza1-amendment-1.md`
+- Amendment 2: `D:/Projects/PM-Waggle-OS/briefs/2026-04-28-cc4-faza1-amendment-2.md`
+- Launch decision (LOCK): `D:/Projects/PM-Waggle-OS/decisions/2026-04-28-gepa-faza1-launch.md`
+- Manifest v7 (Amendment 2 supplemented): `benchmarks/preregistration/manifest-v7-gepa-faza1.yaml`
+  - SHA: `583712dde139ffc87fb1ab21643f68d52c56469ded9e8090a624980b05969beb`
+
+## Substrate freeze
+
+- Branch: `feature/c3-v3-wrapper`
+- Commit: `c9bda3d6dd4c0a4f715e09f3757a96d01ff01cd7` (Phase 4.7)
+- Worktree: `D:/Projects/waggle-os-faza1-wt` (detached HEAD c9bda3d, race-condition guarded)
+
+## Module map (`src/faza-1/`)
+
+| Module | Purpose | Key references |
+|---|---|---|
+| `types.ts` | Shared types + `QWEN_TARGETED_SHAPES` / `NON_QWEN_SHAPES` partition | Amendment 2 §3 |
+| `fitness.ts` | Per-shape fitness function with retrieval engagement bonus fork | Amendment 2 §3 bands |
+| `acceptance.ts` | §F + §F.5 verdict per candidate (false-positive guard) | Launch decision §F |
+| `mutation-validator.ts` | Cell-semantic preservation audit (boundary SHAs) | Launch decision §A.4 + §B |
+| `kappa-audit.ts` | Drift band detection vs canonical 0.7878 ± 0.05 | Launch decision §F.3 |
+| `cost-tracker.ts` | Super-linear governance + halt triggers | Launch decision §A.7 + §D |
+| `selection.ts` | Top-1-per-shape + run-aggregate §F.2 | Launch decision §F.2 |
+| `mutation-oracle-fork.ts` | Qwen vs non-Qwen template routing | Amendment 2 §4 |
+
+## Boundary anchors (LOCKED at substrate c9bda3d)
+
+| Anchor | SHA-256 | Bytes |
+|---|---|---|
+| `packages/agent/src/prompt-shapes/types.ts` (whole file) | `1a9fa329e4b66ed9f0abe8bc22cbbf0124e0c879e1e78ec806d557cab25bc94d` | — |
+| `MULTI_STEP_ACTION_CONTRACT` constant body | `70a1701dfa126f8dc1df9c116f0a8469da005821ecadc59d9b8f348568e755ba` | 252 |
+
+## Baseline shape SHAs (LOCKED)
+
+| Shape | SHA-256 |
+|---|---|
+| `claude.ts` | `cbaf0c37b067b025a1fe97f2feeec11fae4070a8b3fcfaad1da8775dda451cc0` |
+| `qwen-thinking.ts` | `848a4e4917baa5c7bbcc3bb35fb8cb4b4ac8f0ab537243f14cbef3a99197aacb` |
+| `qwen-non-thinking.ts` | `35be379be9a8caafc2c419e32da5f63f92fc83f6f6d70d9df76029c1e8584572` |
+| `gpt.ts` | `5dc6d750d52a68feb9d37ad8384b2bcd59d70962066122ff086b0e5888413576` |
+| `generic-simple.ts` | `81189817f560e26a69394248d8bd9089cae72c7d40825323e2b7407e36026172` |
+
+## Per-shape fitness fork (Amendment 2 §3)
+
+```
+Qwen-targeted (qwen-thinking, qwen-non-thinking):
+  fitness = trio_strict_pass_rate + retrieval_engagement_bonus − cost_penalty
+
+  retrieval_engagement_bonus =
+    +0.05 if mean retrieval_calls per task ≥ 2.0  (Opus parity proxy)
+     0.00 if mean retrieval_calls per task ∈ [1.5, 2.0)
+    −0.05 if mean retrieval_calls per task < 1.5  (Qwen baseline penalty)
+
+Non-Qwen (claude, gpt, generic-simple):
+  fitness = trio_strict_pass_rate − cost_penalty
+```
+
+## §F.5 false-positive guard (Amendment 2 §5)
+
+If best Qwen-shape candidate achieves `+5pp trio_strict delta WITHOUT closing
+retrieval engagement gap (mean retrieval_calls < 1.5)`, candidate is REJECTED
+as false-positive evolution.
+
+## Tests
+
+110 tests across 7 files (`tests/faza-1/`). Run via `npm test` from repo root
+(vitest auto-discovers `benchmarks/*/tests/**/*.test.ts`).
+
+| Test file | Tests |
+|---|---|
+| `fitness.test.ts` | 30 (5 mandatory boundary cases + 5 routing + cost penalty + invariants) |
+| `acceptance.test.ts` | 17 (§F.5 mandatory FAIL + PASS-path + boundaries) |
+| `mutation-validator.test.ts` | 19 (SHA boundary + baseline + metadata + imports + Gen 1 mutation) |
+| `kappa-audit.test.ts` | 22 (drift band + Cohen's κ + audit log + canonical anchor) |
+| `cost-tracker.test.ts` | 23 (HARD_CAP + INTERNAL_HALT + SUPER_LINEAR + audit cadence) |
+| `selection.test.ts` | (top-1 + Qwen retrieval ranking + §F.2 boundaries + missing-baseline errors) |
+| `mutation-oracle-fork.test.ts` | (shape classification + template paths + placeholder substitution) |
+
+## Halt-and-PM checkpoints
+
+| # | Checkpoint | Cumulative | Trigger |
+|---|---|---|---|
+| 1 | Pre-A | ~$5 | Post 50-instance corpus + 5-instance spot-audit |
+| 2 | A | ~$25 | Post NULL-baseline 5 shapes × 8 instances |
+| 3 | B | ~$50-65 | Mid-Gen 1 (after 30 evaluations) |
+| 4 | C | ~$100 | Post held-out validation, Faza 1 verdict |
+
+See launch decision §E for binding details.
+
+## Acceptance criteria summary
+
+Per launch decision §F (binding) — all 4 conditions must hold AND no §F.5 trigger:
+
+1. Best GEPA candidate beats NULL-baseline by ≥+5pp on `trio_strict_pass` rate
+   (`trio_mean ≥ 4.0` per Ask B); for Qwen-targeted shapes, additionally
+   `mean retrieval_calls per task ≥ 1.7` (50% gap closure).
+2. ≥3/5 shapes show positive trio_strict delta vs NULL-baseline.
+3. Trio judge κ remains within `±0.05` of canonical `0.7878`
+   (drift band `[0.7378, 0.8378]`).
+4. Zero cell semantic violations detected by `mutation-validator`.
+5. **§F.5 false-positive guard:** Qwen candidate with `+5pp trio_strict delta`
+   AND `mean retrieval_calls < 1.5` → REJECTED.

benchmarks/gepa/oracle/faza-1/mutation-prompt-template-non-qwen.md

@@ -0,0 +1,54 @@
+# GEPA Mutation Oracle Prompt — Non-Qwen (Claude / GPT / Generic-Simple)
+
+**Template class:** ###TEMPLATE_CLASS###
+**Shape under mutation:** ###SHAPE_NAME###
+
+---
+
+You are an expert prompt engineer. Your job is to produce a single mutated variant of the prompt-shape file below, evolving the *reasoning scaffold* and *planning step structure* while preserving exact cell semantics.
+
+## Context (non-Qwen branch)
+
+This shape (###SHAPE_NAME###) does **not** exhibit the retrieval-engagement gap that Phase 4.5 surfaced for Qwen. Standard mutation guidance per brief §3.3 applies — no Qwen-specific anti-premature-finalization scaffolding required.
+
+Apply general scaffold improvements: clearer reasoning structure, better chain-of-thought triggers, more explicit planning steps where appropriate for the model class.
+
+## Failure modes from Phase 4.3 (top-3 T2 categories for this shape)
+
+###FAILURE_MODE_SUMMARY###
+
+## Mutation guidance (binding)
+
+Modify only:
+- `systemPrompt(input)` method body (string-building only)
+- `soloUserPrompt(input)` method body
+- `multiStepKickoffUserPrompt(input)` method body
+- `retrievalInjectionUserPrompt(input)` method body
+- `metadata.evidence_link` (you MUST update to point to GEPA Gen 1 results: `benchmarks/results/gepa-faza1/oracle/mutation-prompt-template-non-qwen.md`)
+
+Standard mutation directions:
+1. **Evolve reasoning scaffold** — restructure the implicit reasoning prompts (e.g., "think step by step" variants, planning bullets)
+2. **Refine chain-of-thought triggers** — adjust where the model is prompted to articulate its reasoning before producing output
+3. **Improve planning step structure** — make multi-step task decomposition more explicit
+4. **Preserve all cell semantic boundaries** — see prohibitions below
+
+## Prohibited modifications (cell semantic LOCK)
+
+- **DO NOT modify** `MULTI_STEP_ACTION_CONTRACT` — this constant lives in `packages/agent/src/prompt-shapes/types.ts` and its bytes are SHA-pinned
+- **DO NOT modify** `metadata.{description,modelClass,defaultThinking,defaultMaxTokens}`
+- **DO NOT modify** the imports block
+- **DO NOT modify** the file structure (must remain a TypeScript module with a single PromptShape export)
+- **DO NOT modify** the JSON action contract
+- **DO NOT modify** task framing (persona/question/materials section labels)
+
+Mutations violating any of the above will be REJECTED by the mutation validator. Two consecutive invalid mutations trigger halt-and-PM per brief §5.
+
+## Baseline shape file (the input to your mutation)
+
+```typescript
+###BASELINE_SHAPE_CONTENT###
+```
+
+## Output format
+
+Output ONLY the mutated TypeScript file content. No prose, no commentary, no code fences. The file should be a drop-in replacement for the baseline shape file.

benchmarks/gepa/oracle/faza-1/mutation-prompt-template-qwen.md

@@ -0,0 +1,64 @@
+# GEPA Mutation Oracle Prompt — Qwen-Targeted Shape
+
+**Template class:** ###TEMPLATE_CLASS###
+**Shape under mutation:** ###SHAPE_NAME###
+
+---
+
+You are an expert prompt engineer. Your job is to produce a single mutated variant of the prompt-shape file below, evolving the *reasoning scaffold* and *retrieval-engagement guidance* while preserving exact cell semantics.
+
+## Empirical context (binding)
+
+Phase 4.5 audit empirically established that Qwen 3.6 35B-A3B (under both `thinking-on` and `thinking-off` configurations) exhibits a **retrieval-engagement gap** vs Opus 4.7 on the same `MULTI_STEP_ACTION_CONTRACT` tool surface:
+
+- Qwen retrieves **1.33 times per task** on average
+- Opus retrieves **2.33 times per task** on average
+- Qwen finalizes prematurely; Opus exhausts maxSteps in 67% of retrieval runs
+- Resulting H4 trio_mean delta: Qwen scores **−0.65 below** Opus on every task
+
+The mutation surface (prompt-shape body) is the lever to address this. **Your mutation must encourage more aggressive multi-turn retrieval engagement on Qwen.**
+
+## Failure modes from Phase 4.3 (top-3 T2 categories for this shape)
+
+###FAILURE_MODE_SUMMARY###
+
+## Mutation guidance (binding)
+
+Modify only:
+- `systemPrompt(input)` method body (string-building only)
+- `soloUserPrompt(input)` method body
+- `multiStepKickoffUserPrompt(input)` method body
+- `retrievalInjectionUserPrompt(input)` method body
+- `metadata.evidence_link` (you MUST update to point to GEPA Gen 1 results: `benchmarks/results/gepa-faza1/oracle/mutation-prompt-template-qwen.md`)
+
+Apply Qwen-specific scaffolding:
+1. **Emphasize multi-turn retrieval** over single-shot retrieval — make repeated retrieval feel mandatory, not optional
+2. **Discourage premature finalization** with explicit phrasing like:
+   - "Continue retrieving until you have evidence from at least 2 distinct queries before finalizing"
+   - "Single retrieval is rarely sufficient for synthesis tasks"
+3. **Encourage iterative refinement** — each retrieval query should build on prior turn's results
+4. **Add anti-premature-finalization scaffolding** in the multiStepKickoff or systemPrompt:
+   - "Before finalizing, ask: what gap in evidence remains? Issue another retrieval if any gap exists."
+   - "A complete answer requires triangulation from multiple retrievals"
+5. **Preserve all cell semantic boundaries** — see prohibitions below
+
+## Prohibited modifications (cell semantic LOCK)
+
+- **DO NOT modify** `MULTI_STEP_ACTION_CONTRACT` — this constant lives in `packages/agent/src/prompt-shapes/types.ts` and its bytes are SHA-pinned
+- **DO NOT modify** `metadata.{description,modelClass,defaultThinking,defaultMaxTokens}`
+- **DO NOT modify** the imports block
+- **DO NOT modify** the file structure (must remain a TypeScript module with a single PromptShape export)
+- **DO NOT modify** the JSON action contract (the `{"action": "retrieve", ...}` / `{"action": "finalize", ...}` shape)
+- **DO NOT modify** task framing (persona/question/materials section labels — the template rendering structure)
+
+Mutations that violate any of the above will be REJECTED by the mutation validator (`mutation-validator.ts`), consume one of two retry tolerances, and trigger halt-and-PM if exceeded twice consecutively per brief §5.
+
+## Baseline shape file (the input to your mutation)
+
+```typescript
+###BASELINE_SHAPE_CONTENT###
+```
+
+## Output format
+
+Output ONLY the mutated TypeScript file content. No prose, no commentary, no code fences. The file should be a drop-in replacement for the baseline shape file.

benchmarks/gepa/scripts/faza-1/analyze-checkpoint-a.py

@@ -0,0 +1,142 @@
+"""Compute Checkpoint A v2 aggregates + classify per pre-registered bands."""
+import json, sys, io, math, os
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
+
+JSONL = 'D:/Projects/waggle-os-faza1-wt/benchmarks/results/gepa-faza1/null-baseline/null-baseline-eval.jsonl'
+with open(JSONL, encoding='utf-8') as f:
+    records = [json.loads(l) for l in f if l.strip()]
+
+print(f'TOTAL records: {len(records)}')
+total_cost = sum(r['evalCostUsd'] for r in records)
+total_subject = sum(r['candidateCostUsd'] for r in records)
+total_judge = sum(r['judges']['judgeCostTotal'] for r in records)
+print(f'cumulative cost: ${total_cost:.4f}  (subject ${total_subject:.4f} + judge ${total_judge:.4f})')
+
+SHAPES = ['claude','qwen-thinking','qwen-non-thinking','gpt','generic-simple']
+shape_aggs = {}
+for s in SHAPES:
+    rs = [r for r in records if r['shape'] == s]
+    if not rs: continue
+    n = len(rs)
+    pii = sum(1 for r in rs if r['judges']['trioStrictPassII'])
+    pi = sum(1 for r in rs if r['judges']['trioStrictPassI'])
+    mt = sum(r['judges']['trioMean'] for r in rs)/n
+    mr = sum(r['retrievalCalls'] for r in rs)/n
+    mc = sum(r['evalCostUsd'] for r in rs)/n
+    le = sum(1 for r in rs if r['loopExhausted'])/n
+    ms = sum(r['stepsTaken'] for r in rs)/n
+    rate = pii/n
+    z = 1.96
+    denom = 1 + z*z/n
+    center = (rate + z*z/(2*n)) / denom
+    margin = z * math.sqrt(rate*(1-rate)/n + z*z/(4*n*n)) / denom
+    ci_low = max(0, center - margin)
+    ci_hi = min(1, center + margin)
+    shape_aggs[s] = dict(n=n, pii=pii, pi=pi, rate=rate, mt=mt, mr=mr, mc=mc, le=le, ms=ms, ci_low=ci_low, ci_hi=ci_hi)
+
+print('\n=== PER-SHAPE AGGREGATES (REAL) ===')
+for s in SHAPES:
+    a = shape_aggs[s]
+    print(f'  {s:<22} pass_II={a["pii"]}/{a["n"]} ({a["rate"]:.1%})  pass_I={a["pi"]}/{a["n"]}  trio={a["mt"]:.3f}  retr={a["mr"]:.2f}  cost=${a["mc"]:.4f}  CI95=[{a["ci_low"]:.3f},{a["ci_hi"]:.3f}]')
+
+artifactual = {'claude': 0.50, 'qwen-thinking': 1.0, 'qwen-non-thinking': 0.75, 'gpt': 0.875, 'generic-simple': 0.875}
+print('\n=== DELTA vs artifactual ===')
+deltas = {}
+for s in SHAPES:
+    d = (shape_aggs[s]['rate'] - artifactual[s]) * 100
+    deltas[s] = d
+    print(f'  {s:<22} artifactual={artifactual[s]:.1%}  real={shape_aggs[s]["rate"]:.1%}  delta={d:+.1f}pp')
+
+print('\n=== PRE-REGISTERED BAND CLASSIFICATION (LOCKED §C) ===')
+expected_bounds = {'claude':(35,65),'qwen-thinking':(85,100),'qwen-non-thinking':(60,90),'gpt':(73,100),'generic-simple':(73,100)}
+in_per_shape_band = True
+for s in SHAPES:
+    pct = shape_aggs[s]['rate'] * 100
+    lo, hi = expected_bounds[s]
+    ok = lo <= pct <= hi
+    print(f'  {s:<22} real {pct:>5.1f}% expected band [{lo}, {hi}] {"IN" if ok else "OUT"}')
+    if not ok: in_per_shape_band = False
+
+max_abs = max(abs(d) for d in deltas.values())
+sign_flips = sum(1 for s in SHAPES if (artifactual[s] >= 0.5) != (shape_aggs[s]['rate'] >= 0.5))
+all_neg = all(d <= 0 for d in deltas.values())
+all_pos = all(d >= 0 for d in deltas.values())
+uniform_shift_ok = (all_neg or all_pos) and (max(deltas.values()) - min(deltas.values()) <= 25)
+
+print('\n=== RAW AGREEMENT + KAPPA ===')
+def per_judge(model, t=4.0):
+    return [next(j['mean']>=t for j in r['judges']['records'] if j['judge_model']==model) for r in records]
+opus = per_judge('claude-opus-4-7')
+gpt_j = per_judge('gpt-5.4')
+mm = per_judge('minimax-m27-via-openrouter')
+
+def raw_agree(a,b): return sum(1 for x,y in zip(a,b) if x==y)/len(a)
+def kappa(a,b):
+    n=len(a); cc=sum(1 for x,y in zip(a,b) if x and y); ii=sum(1 for x,y in zip(a,b) if not x and not y)
+    ci=sum(1 for x,y in zip(a,b) if x and not y); ic=sum(1 for x,y in zip(a,b) if not x and y)
+    po=(cc+ii)/n; pa=(cc+ci)/n; pb=(cc+ic)/n; pe=pa*pb+(1-pa)*(1-pb)
+    return (po-pe)/(1-pe) if pe<1 else 1.0
+
+ra={'opus_gpt':raw_agree(opus,gpt_j),'opus_minimax':raw_agree(opus,mm),'gpt_minimax':raw_agree(gpt_j,mm)}
+k={'opus_gpt':kappa(opus,gpt_j),'opus_minimax':kappa(opus,mm),'gpt_minimax':kappa(gpt_j,mm)}
+for pair in ['opus_gpt','opus_minimax','gpt_minimax']:
+    print(f'  {pair:<16} raw={ra[pair]:.1%}  kappa={k[pair]:+.3f}')
+min_ra = min(ra.values())
+min_k = min(k.values())
+print(f'  MIN raw agreement: {min_ra:.1%}  (threshold 65%: {"PASS" if min_ra>=0.65 else "FAIL"})')
+print(f'  MIN kappa: {min_k:+.3f}')
+
+print('\nPer-judge pass rates at 4.0:')
+print(f'  Opus: {sum(opus)}/{len(opus)} = {sum(opus)/len(opus):.1%}')
+print(f'  GPT: {sum(gpt_j)}/{len(gpt_j)} = {sum(gpt_j)/len(gpt_j):.1%}')
+print(f'  MiniMax: {sum(mm)}/{len(mm)} = {sum(mm)/len(mm):.1%}')
+
+artif_min_ra = 0.70
+ra_collapse_pp = (artif_min_ra - min_ra) * 100
+anomalous_max = max_abs > 30
+anomalous_flips = sign_flips > 2
+anomalous_ra = ra_collapse_pp > 20
+
+print(f'\n=== ANOMALY CHECK ===')
+print(f'  max |delta| > 30pp: {anomalous_max} (max={max_abs:.1f}pp)')
+print(f'  sign flips > 2: {anomalous_flips} (count={sign_flips})')
+print(f'  raw agreement collapse > 20pp: {anomalous_ra} (artifactual {artif_min_ra:.1%} -> real {min_ra:.1%}, delta {ra_collapse_pp:+.1f}pp)')
+
+is_anomalous = anomalous_max or anomalous_flips or anomalous_ra
+is_expected = (in_per_shape_band or uniform_shift_ok) and not is_anomalous
+print(f'\nPER-SHAPE BANDS: {"all IN" if in_per_shape_band else "some OUT"}')
+print(f'UNIFORM SHIFT: ok={uniform_shift_ok} (all_neg={all_neg}, all_pos={all_pos}, spread={max(deltas.values())-min(deltas.values()):.1f}pp)')
+print(f'EXPECTED met: {is_expected}')
+print(f'ANOMALOUS triggered: {is_anomalous}')
+print(f'CLASSIFICATION: {"EXPECTED -> Gen 1 GO" if is_expected else "ANOMALOUS -> INVESTIGATE"}')
+
+real_per_eval = total_cost / len(records)
+gen1_proj = 5 * 3 * 8 * real_per_eval
+artif_per_eval = 0.124
+sens_pct = (real_per_eval - artif_per_eval) / artif_per_eval * 100
+print(f'\n=== COST SENSITIVITY ===')
+print(f'  artifactual: ${artif_per_eval:.4f}/eval  real: ${real_per_eval:.4f}/eval  delta: {sens_pct:+.1f}%')
+print(f'  Gen 1 proj: ${gen1_proj:.4f}  $78 halt: {"PASS" if gen1_proj<=78 else "HALT"}')
+
+print('\n=== F-SATURATED PER-SHAPE ===')
+n_qual = 0
+for s in SHAPES:
+    a = shape_aggs[s]
+    qual = a['ci_low'] >= 0.88
+    if qual: n_qual += 1
+    print(f'  {s:<22} pii={a["pii"]}/{a["n"]} CI_low={a["ci_low"]:.3f} >=0.88? {"Y" if qual else "N"}  policy={"F-sat" if qual else "F.1 (>=+5pp)"}')
+if n_qual == 0: decision = 'GLOBAL: revoke F-saturated, apply F.1 to all 5'
+elif n_qual == 5: decision = 'GLOBAL: re-instate F-saturated for all 5'
+else: decision = f'MIXED: {n_qual}/5 qualify (per-shape policy, pre-authorized)'
+print(f'Decision: {decision}')
+
+out = {
+    'shape_aggregates': shape_aggs, 'deltas_pp': deltas, 'classification': 'EXPECTED' if is_expected else 'ANOMALOUS',
+    'raw_agreement': ra, 'min_raw_agreement': min_ra, 'kappa': k, 'min_kappa': min_k,
+    'per_judge_pass_rate': {'opus': sum(opus)/len(opus), 'gpt': sum(gpt_j)/len(gpt_j), 'minimax': sum(mm)/len(mm)},
+    'cost': {'total': total_cost, 'per_eval': real_per_eval, 'sensitivity_pct': sens_pct, 'gen1_projected': gen1_proj},
+    'F_saturated_n_qualifying': n_qual, 'F_saturated_decision': decision,
+}
+with open('D:/Projects/waggle-os-faza1-wt/benchmarks/results/gepa-faza1/null-baseline/checkpoint-a-v2-aggregates.json', 'w') as f:
+    json.dump(out, f, indent=2)
+print('\n=> aggregates JSON written')

benchmarks/gepa/scripts/faza-1/compute-final-kappa.ts

@@ -0,0 +1,194 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — final κ_trio recompute on combined 135 evals.
+ *
+ * Per launch decision §F condition_3 + manifest v7 §canonical_kappa_anchor.
+ *
+ * Combines:
+ *   - Gen 1 eval JSONL (120 records)
+ *   - Checkpoint C eval JSONL (15 records)
+ *
+ * For each eval, derives per-judge binary pass (mean ≥ 4.0) and computes
+ * pairwise Cohen's κ for the 3 judge pairs (Opus↔GPT, Opus↔MiniMax,
+ * GPT↔MiniMax). Reports κ_trio (= min of pairs) + audit verdict.
+ *
+ * Per Amendment 5 §judge_metric_design: also reports raw agreement rate
+ * (primary metric for synthesis Likert).
+ */
+
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+import { fileURLToPath } from 'node:url';
+
+import {
+  auditKappa,
+  computeCohensKappa,
+  CANONICAL_KAPPA,
+  KAPPA_DRIFT_BAND_LOW,
+  KAPPA_DRIFT_BAND_HIGH,
+} from '../../src/faza-1/kappa-audit.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const REPO_ROOT = path.resolve(__dirname, '../../../..');
+
+const GEN_1_JSONL = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/gen-1/gen-1-eval.jsonl');
+const CHECKPOINT_C_JSONL = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/checkpoint-c/checkpoint-c-eval.jsonl');
+const OUT_JSON = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/checkpoint-c/final-kappa-audit.json');
+
+const PASS_THRESHOLD = 4.0;
+const JUDGE_ORDER = ['claude-opus-4-7', 'gpt-5.4', 'minimax-m27-via-openrouter'] as const;
+
+interface JudgeRec { judge_model: string; mean: number; raw: any }
+interface EvalRec {
+  candidateId: string;
+  judgeRecords: JudgeRec[];
+  evalSet?: 'held-out';
+}
+
+function loadJsonl(filepath: string): EvalRec[] {
+  if (!fs.existsSync(filepath)) {
+    console.error(`MISSING: ${filepath}`);
+    return [];
+  }
+  return fs.readFileSync(filepath, 'utf-8').trim().split(/\n+/).filter(Boolean).map(l => JSON.parse(l));
+}
+
+function judgePassPerEval(rec: EvalRec): { opus: boolean | null; gpt: boolean | null; minimax: boolean | null } {
+  const findBy = (model: string) => rec.judgeRecords.find(j => j.judge_model === model);
+  const opus = findBy('claude-opus-4-7');
+  const gpt = findBy('gpt-5.4');
+  const minimax = findBy('minimax-m27-via-openrouter');
+  return {
+    opus: opus && opus.mean > 0 ? opus.mean >= PASS_THRESHOLD : null,
+    gpt: gpt && gpt.mean > 0 ? gpt.mean >= PASS_THRESHOLD : null,
+    minimax: minimax && minimax.mean > 0 ? minimax.mean >= PASS_THRESHOLD : null,
+  };
+}
+
+interface ConfusionMatrix {
+  bothCorrect: number;
+  bothIncorrect: number;
+  firstCorrectSecondIncorrect: number;
+  firstIncorrectSecondCorrect: number;
+}
+
+function buildConfusion(pairs: Array<[boolean, boolean]>): ConfusionMatrix {
+  const m: ConfusionMatrix = { bothCorrect: 0, bothIncorrect: 0, firstCorrectSecondIncorrect: 0, firstIncorrectSecondCorrect: 0 };
+  for (const [a, b] of pairs) {
+    if (a && b) m.bothCorrect++;
+    else if (!a && !b) m.bothIncorrect++;
+    else if (a && !b) m.firstCorrectSecondIncorrect++;
+    else m.firstIncorrectSecondCorrect++;
+  }
+  return m;
+}
+
+function rawAgreement(pairs: Array<[boolean, boolean]>): number {
+  if (pairs.length === 0) return NaN;
+  let agree = 0;
+  for (const [a, b] of pairs) if (a === b) agree++;
+  return agree / pairs.length;
+}
+
+function main() {
+  const gen1 = loadJsonl(GEN_1_JSONL);
+  const cpc = loadJsonl(CHECKPOINT_C_JSONL);
+  const combined = [...gen1, ...cpc];
+  console.log(`Loaded: gen-1=${gen1.length}, checkpoint-c=${cpc.length}, combined=${combined.length}`);
+
+  // Build per-judge pass arrays (skip evals where any judge failed parse)
+  const opusGpt: Array<[boolean, boolean]> = [];
+  const opusMinimax: Array<[boolean, boolean]> = [];
+  const gptMinimax: Array<[boolean, boolean]> = [];
+  let droppedDueToFailedJudge = 0;
+
+  for (const rec of combined) {
+    const v = judgePassPerEval(rec);
+    if (v.opus === null || v.gpt === null || v.minimax === null) {
+      droppedDueToFailedJudge++;
+      continue;
+    }
+    opusGpt.push([v.opus, v.gpt]);
+    opusMinimax.push([v.opus, v.minimax]);
+    gptMinimax.push([v.gpt, v.minimax]);
+  }
+  console.log(`Effective N (after dropping failed-judge evals): ${opusGpt.length} (dropped: ${droppedDueToFailedJudge})`);
+
+  const kOpusGpt = computeCohensKappa(buildConfusion(opusGpt));
+  const kOpusMinimax = computeCohensKappa(buildConfusion(opusMinimax));
+  const kGptMinimax = computeCohensKappa(buildConfusion(gptMinimax));
+
+  const audit = auditKappa({ kOpusGpt, kOpusMinimax, kGptMinimax });
+
+  const rawOG = rawAgreement(opusGpt);
+  const rawOM = rawAgreement(opusMinimax);
+  const rawGM = rawAgreement(gptMinimax);
+  const rawMin = Math.min(rawOG, rawOM, rawGM);
+
+  const passRates = {
+    opus: opusGpt.filter(p => p[0]).length / opusGpt.length,
+    gpt: opusGpt.filter(p => p[1]).length / opusGpt.length,
+    minimax: opusMinimax.filter(p => p[1]).length / opusMinimax.length,
+  };
+
+  const result = {
+    generated_at: new Date().toISOString(),
+    inputs: {
+      gen_1_jsonl_records: gen1.length,
+      checkpoint_c_jsonl_records: cpc.length,
+      combined_records: combined.length,
+      effective_n_after_judge_failures: opusGpt.length,
+      dropped_due_to_failed_judge: droppedDueToFailedJudge,
+      threshold: PASS_THRESHOLD,
+    },
+    pairwise_kappa: {
+      opus_gpt: kOpusGpt,
+      opus_minimax: kOpusMinimax,
+      gpt_minimax: kGptMinimax,
+    },
+    pairwise_raw_agreement: {
+      opus_gpt: rawOG,
+      opus_minimax: rawOM,
+      gpt_minimax: rawGM,
+      min: rawMin,
+    },
+    per_judge_pass_rates: passRates,
+    canonical_kappa: CANONICAL_KAPPA,
+    drift_band: { low: KAPPA_DRIFT_BAND_LOW, high: KAPPA_DRIFT_BAND_HIGH },
+    audit: audit,
+  };
+
+  fs.writeFileSync(OUT_JSON, JSON.stringify(result, null, 2));
+
+  console.log('');
+  console.log('━'.repeat(76));
+  console.log('  Faza 1 final κ audit on combined 135 evals');
+  console.log('━'.repeat(76));
+  console.log(`  N effective              : ${opusGpt.length}`);
+  console.log(`  Per-judge pass rate@4.0  : Opus=${(passRates.opus*100).toFixed(1)}%  GPT=${(passRates.gpt*100).toFixed(1)}%  MiniMax=${(passRates.minimax*100).toFixed(1)}%`);
+  console.log('');
+  console.log('  PAIRWISE Cohen\'s κ:');
+  console.log(`    Opus↔GPT       : ${kOpusGpt.toFixed(4)}`);
+  console.log(`    Opus↔MiniMax   : ${kOpusMinimax.toFixed(4)}`);
+  console.log(`    GPT↔MiniMax    : ${kGptMinimax.toFixed(4)}`);
+  console.log('');
+  console.log('  PAIRWISE raw agreement (Amendment 5 PRIMARY for synthesis Likert):');
+  console.log(`    Opus↔GPT       : ${(rawOG*100).toFixed(1)}%`);
+  console.log(`    Opus↔MiniMax   : ${(rawOM*100).toFixed(1)}%`);
+  console.log(`    GPT↔MiniMax    : ${(rawGM*100).toFixed(1)}%`);
+  console.log(`    MIN raw         : ${(rawMin*100).toFixed(1)}%`);
+  console.log('');
+  console.log(`  κ_conservative_trio    : ${audit.kConservativeTrio.toFixed(4)}`);
+  console.log(`  Canonical κ            : ${CANONICAL_KAPPA.toFixed(4)}`);
+  console.log(`  Drift band             : [${KAPPA_DRIFT_BAND_LOW.toFixed(4)}, ${KAPPA_DRIFT_BAND_HIGH.toFixed(4)}]`);
+  console.log(`  §F.3 verdict           : ${audit.verdict}`);
+  console.log(`  v6 policy floor (≥0.70): ${audit.v6PolicyFloorPass ? 'PASS' : 'FAIL'}`);
+  console.log(`  Amendment 5 raw 65% min: ${rawMin >= 0.65 ? 'PASS' : 'FAIL'} (observed ${(rawMin*100).toFixed(1)}%)`);
+  console.log('');
+  console.log(`  Audit log: ${audit.auditLogLine}`);
+  console.log('');
+  console.log(`Wrote: ${OUT_JSON}`);
+}
+
+main();

benchmarks/gepa/scripts/faza-1/generate-h3-corpus.ts

@@ -0,0 +1,488 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — H3 NorthLane CFO synthesis corpus generator.
+ *
+ * Per launch decision §G step 4 + manifest v7 §corpus_design + Amendment 1 Ask A Option C.
+ *
+ * Generates 50 stratified synthesis-task instances via Opus 4.7 oracle.
+ *
+ * Cost projection: ~$5 (50 × $0.10/instance avg).
+ * Halt threshold: $7 (40% buffer per manifest v7 §corpus_design.expected_generation_cost_usd).
+ *
+ * Output: benchmarks/results/gepa-faza1/corpus/h3-northlane-cfo-50-instances.jsonl
+ *
+ * Usage:
+ *   npx tsx benchmarks/gepa/scripts/faza-1/generate-h3-corpus.ts --dry-run
+ *     # No LLM call. Validates stratification + prompt build only.
+ *
+ *   npx tsx benchmarks/gepa/scripts/faza-1/generate-h3-corpus.ts --probe
+ *     # Single instance (cell index 0). ~$0.10. Validates LiteLLM connection + JSON parse.
+ *
+ *   npx tsx benchmarks/gepa/scripts/faza-1/generate-h3-corpus.ts --all
+ *     # All 50 instances. ~$5. Halt at $7. Spot-audit + Pre-A report afterwards.
+ *
+ * Authority: launch decision LOCK at decisions/2026-04-28-gepa-faza1-launch.md (PM-Waggle-OS)
+ */
+
+import * as fs from 'node:fs';
+import * as fsp from 'node:fs/promises';
+import * as path from 'node:path';
+import * as crypto from 'node:crypto';
+import { fileURLToPath } from 'node:url';
+
+import {
+  TOTAL_INSTANCES,
+  STRATIFICATION_SEED,
+  type CorpusInstance,
+  type StratificationCell,
+  listStratificationCells,
+  buildInstanceId,
+  validateInstance,
+  runSpotAudit,
+  corpusSha256,
+} from '../../src/faza-1/corpus.js';
+import { buildCorpusInstancePrompt } from '../../src/faza-1/corpus-prompt.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const REPO_ROOT = path.resolve(__dirname, '../../../..');
+
+const OUT_DIR = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/corpus');
+const OUT_JSONL = path.join(OUT_DIR, 'h3-northlane-cfo-50-instances.jsonl');
+const RUN_LOG = path.join(OUT_DIR, 'generation-run.log');
+const SPOT_AUDIT_REPORT = path.join(OUT_DIR, 'h3-spot-audit-pre-a-report.md');
+
+// ── LLM config (per launch decision §A.1 inheritance) ─────────────────────
+
+const LITELLM_URL = process.env.LITELLM_URL ?? 'http://localhost:4000';
+const ORACLE_MODEL = 'claude-opus-4-7';
+const ORACLE_MAX_TOKENS = 8000;
+const ORACLE_TEMPERATURE = 0.7;  // higher for instance variation per manifest v7
+const ORACLE_THINKING = true;
+const MANIFEST_ANCHOR = 'manifest-v7-gepa-faza1';
+
+// Pricing per pilot runner SHA 8a6251e2 line 129
+const PRICE_INPUT_PER_M = 15.0;
+const PRICE_OUTPUT_PER_M = 75.0;
+
+// Cost halt per manifest v7 Amendment 3: $15 = 40% buffer over $13.58 actual expected
+// (was $7 pre-Amendment-3; raised after probe revealed inherited $0.10/instance estimate
+// was 170% off vs actual Opus 4.7 generation cost of $0.27/instance).
+const COST_HALT_USD = 15.0;
+
+// ── Logging ────────────────────────────────────────────────────────────────
+
+function log(msg: string): void {
+  const line = `[${new Date().toISOString()}] ${msg}\n`;
+  try { fs.appendFileSync(RUN_LOG, line); } catch { /* dir may not exist yet */ }
+  process.stderr.write(line);
+}
+
+// ── CLI ────────────────────────────────────────────────────────────────────
+
+interface Args {
+  mode: 'dry-run' | 'probe' | 'all' | 'retry-failed';
+  startIdx?: number;
+  endIdx?: number;
+}
+
+/**
+ * The 3 cells that failed in the 2026-04-28 first-pass generation due to
+ * Opus emitting unescaped quotation marks in long doc bodies. Per
+ * Amendment 4 retry methodology, these cells are re-run with JSON-mode
+ * response_format + lowered temperature + reduced max_tokens.
+ */
+const RETRY_FAILED_CELLS: ReadonlyArray<{ family: string; persona: string; stage: string }> = [
+  { family: 'F4', persona: 'p2_cfo', stage: 'stage_a_series_b_growth_burning' },
+  { family: 'F4', persona: 'p2_cfo', stage: 'stage_b_post_profitable_consolidation' },
+  { family: 'F5', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' },
+];
+
+function parseArgs(argv: string[]): Args {
+  let mode: Args['mode'] = 'dry-run';
+  let startIdx: number | undefined;
+  let endIdx: number | undefined;
+  for (let i = 0; i < argv.length; i++) {
+    const flag = argv[i];
+    const next = argv[i + 1];
+    switch (flag) {
+      case '--dry-run':       mode = 'dry-run'; break;
+      case '--probe':         mode = 'probe'; break;
+      case '--all':           mode = 'all'; break;
+      case '--retry-failed':  mode = 'retry-failed'; break;
+      case '--start':         startIdx = Number(next); i++; break;
+      case '--end':           endIdx = Number(next); i++; break;
+    }
+  }
+  return { mode, startIdx, endIdx };
+}
+
+// ── LiteLLM call ───────────────────────────────────────────────────────────
+
+interface LlmResult {
+  content: string;
+  inTokens: number;
+  outTokens: number;
+  costUsd: number;
+  latencyMs: number;
+  error?: string;
+}
+
+/**
+ * Per-call Opus oracle options. The default (temperature 1.0, max_tokens 8000,
+ * no response_format) matches the original generation. JSON-mode retry uses
+ * temperature 0.3 + max_tokens 6000 + response_format json_object per
+ * Amendment 4 retry methodology.
+ */
+interface OpusOracleOptions {
+  maxTokens?: number;
+  temperature?: number;
+  responseFormatJsonObject?: boolean;
+}
+
+async function callOpusOracle(
+  prompt: string,
+  options: OpusOracleOptions = {},
+): Promise<LlmResult> {
+  const masterKey = process.env.LITELLM_MASTER_KEY;
+  if (!masterKey) {
+    throw new Error('LITELLM_MASTER_KEY env not set; cannot call Opus oracle');
+  }
+
+  const payload: Record<string, unknown> = {
+    model: ORACLE_MODEL,
+    messages: [{ role: 'user', content: prompt }],
+    max_tokens: options.maxTokens ?? ORACLE_MAX_TOKENS,
+  };
+  // Anthropic Opus 4.7 + response_format=json_object rejects `temperature`
+  // as deprecated for that mode. Omit temperature when JSON-mode is requested
+  // (matches the pilot runner's "reasoning model omit temperature" precedent
+  // for GPT-5.4 + MiniMax). Standard mode keeps temperature.
+  if (options.responseFormatJsonObject) {
+    payload.response_format = { type: 'json_object' };
+  } else {
+    payload.temperature = options.temperature ?? 1.0;
+  }
+
+  const started = Date.now();
+  let lastErr: string | undefined;
+  for (let attempt = 0; attempt < 2; attempt++) {
+    try {
+      const resp = await fetch(`${LITELLM_URL}/chat/completions`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${masterKey}` },
+        body: JSON.stringify(payload),
+      });
+      const d: any = await resp.json();
+      if ('error' in d) {
+        lastErr = String(d.error?.message ?? JSON.stringify(d.error)).slice(0, 200);
+        if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+        continue;
+      }
+      const content = d.choices?.[0]?.message?.content ?? '';
+      const usage = d.usage ?? {};
+      const inTok = usage.prompt_tokens ?? 0;
+      const outTok = usage.completion_tokens ?? 0;
+      const costUsd = (inTok * PRICE_INPUT_PER_M + outTok * PRICE_OUTPUT_PER_M) / 1_000_000;
+      return { content, inTokens: inTok, outTokens: outTok, costUsd, latencyMs: Date.now() - started };
+    } catch (e) {
+      lastErr = `${(e as Error).name}: ${(e as Error).message}`.slice(0, 200);
+      if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+      continue;
+    }
+  }
+  return {
+    content: '', inTokens: 0, outTokens: 0, costUsd: 0,
+    latencyMs: Date.now() - started,
+    error: lastErr ?? 'unknown error',
+  };
+}
+
+// ── JSON extraction ────────────────────────────────────────────────────────
+
+interface ParsedInstance {
+  personaText: string;
+  scenario: string;  // optional — some prompts may put scenario inside personaText
+  sourceDocuments: Array<{ title: string; body: string }>;
+  question: string;
+}
+
+function parseInstanceJson(content: string): ParsedInstance | { error: string } {
+  // Strip code fence wrappers if present (Opus sometimes adds them despite instructions)
+  let s = content.trim();
+  if (s.startsWith('```')) {
+    s = s.replace(/^```[a-z]*\n?/, '').replace(/```\s*$/, '');
+  }
+  // Find first { and last } for tolerant parsing
+  const firstBrace = s.indexOf('{');
+  const lastBrace = s.lastIndexOf('}');
+  if (firstBrace < 0 || lastBrace < 0) {
+    return { error: `no JSON object found in oracle response (length=${content.length})` };
+  }
+  const jsonStr = s.slice(firstBrace, lastBrace + 1);
+  try {
+    const parsed = JSON.parse(jsonStr);
+    // Tolerant schema: accept either a "scenario" field or scenario embedded in personaText
+    const personaText: string = parsed.personaText ?? '';
+    const scenario: string = parsed.scenario ?? '';
+    const sourceDocuments = Array.isArray(parsed.sourceDocuments) ? parsed.sourceDocuments : [];
+    const question: string = parsed.question ?? '';
+
+    if (!personaText || !sourceDocuments.length || !question) {
+      return { error: `parsed JSON missing required fields (personaText/sourceDocuments/question)` };
+    }
+    return { personaText, scenario, sourceDocuments, question };
+  } catch (e) {
+    return { error: `JSON parse failed: ${(e as Error).message}` };
+  }
+}
+
+// ── Build full CorpusInstance from parsed oracle output ────────────────────
+
+function assembleInstance(
+  cell: StratificationCell,
+  instanceId: string,
+  parsed: ParsedInstance,
+  llm: LlmResult,
+): CorpusInstance {
+  const sourceDocuments = parsed.sourceDocuments.map(d => ({
+    title: d.title,
+    body: d.body,
+    charCount: d.body.length,
+  }));
+  // If oracle put scenario in personaText, just use personaText as-is.
+  // Otherwise concatenate "personaText\n\nScenario: scenario".
+  const fullPersonaText = parsed.scenario
+    ? `${parsed.personaText}\n\nScenario: ${parsed.scenario}`
+    : parsed.personaText;
+  const materialsConcat = sourceDocuments
+    .map(d => `## ${d.title}\n\n${d.body}`)
+    .join('\n\n---\n\n');
+  return {
+    instanceId,
+    cell,
+    personaText: fullPersonaText,
+    scenario: parsed.scenario || extractScenarioFromPersonaText(parsed.personaText),
+    sourceDocuments,
+    question: parsed.question,
+    materialsConcat,
+    manifestAnchor: MANIFEST_ANCHOR,
+    generatedBy: ORACLE_MODEL,
+    generatedAtIso: new Date().toISOString(),
+    generationCostUsd: llm.costUsd,
+  };
+}
+
+function extractScenarioFromPersonaText(personaText: string): string {
+  const m = personaText.match(/Scenario:\s*([\s\S]*)/i);
+  return m ? m[1].trim() : '';
+}
+
+// ── Generate one cell ──────────────────────────────────────────────────────
+
+async function generateOneCell(
+  cell: StratificationCell,
+  ordinal: number,
+  options: OpusOracleOptions & { retryNote?: string } = {},
+): Promise<CorpusInstance | { error: string }> {
+  const instanceId = buildInstanceId(cell, ordinal);
+  const prompt = buildCorpusInstancePrompt({ cell, instanceId });
+  const noteSuffix = options.retryNote ? ` [${options.retryNote}]` : '';
+  log(`[${instanceId}] generating via ${ORACLE_MODEL} (prompt ${prompt.length}c)${noteSuffix}`);
+  const llm = await callOpusOracle(prompt, options);
+  if (llm.error) {
+    log(`[${instanceId}] LLM error: ${llm.error}`);
+    return { error: `LLM error: ${llm.error}` };
+  }
+  const parsed = parseInstanceJson(llm.content);
+  if ('error' in parsed) {
+    log(`[${instanceId}] parse error: ${parsed.error}; raw content first 200c: ${llm.content.slice(0, 200)}`);
+    return { error: parsed.error };
+  }
+  const instance = assembleInstance(cell, instanceId, parsed, llm);
+  const validation = validateInstance(instance);
+  if (!validation.valid) {
+    log(`[${instanceId}] validation failed: ${validation.violations.join('; ')}`);
+    return { error: `validation failed: ${validation.violations.join('; ')}` };
+  }
+  log(`[${instanceId}] OK; cost=$${llm.costUsd.toFixed(4)}; ${instance.sourceDocuments.length} docs; latency=${llm.latencyMs}ms`);
+  return instance;
+}
+
+// ── Spot-audit report writer (Pre-A halt-and-PM artifact) ──────────────────
+
+function writeSpotAuditReport(instances: CorpusInstance[], totalCostUsd: number): void {
+  const audit = runSpotAudit(instances);
+  const sha = corpusSha256(instances);
+  const md: string[] = [];
+  md.push('---');
+  md.push('report_id: 2026-04-28-gepa-faza1-pre-a-corpus-audit');
+  md.push('date: 2026-04-28');
+  md.push('checkpoint: Pre-A (corpus quality + NULL kick auth)');
+  md.push('manifest_anchor: manifest-v7-gepa-faza1');
+  md.push(`corpus_sha256: ${sha}`);
+  md.push(`total_instances: ${instances.length}`);
+  md.push(`total_generation_cost_usd: ${totalCostUsd.toFixed(4)}`);
+  md.push(`spot_audit_sample_size: ${audit.sampleSize}`);
+  md.push(`spot_audit_seed: ${STRATIFICATION_SEED}`);
+  md.push(`halt_on_failure: ${audit.haltOnFailure}`);
+  md.push('---');
+  md.push('');
+  md.push('# Pre-A Halt-and-PM Report — H3 Corpus Quality Audit');
+  md.push('');
+  md.push('## TL;DR');
+  md.push('');
+  md.push(`Generated **${instances.length}/${TOTAL_INSTANCES}** instances at total cost **$${totalCostUsd.toFixed(2)}** (vs $5 expected, $7 halt). Spot-audit sample of ${audit.sampleSize} random instances (seed=${STRATIFICATION_SEED}) ${audit.haltOnFailure ? 'FAILED — corpus regeneration required.' : 'PASSED — NULL-baseline kick authorized pending PM ratify.'}`);
+  md.push('');
+  md.push('## Spot-audit results (per-instance)');
+  md.push('');
+  md.push('| Instance ID | Result | Violations |');
+  md.push('|---|---|---|');
+  for (const a of audit.perInstance) {
+    md.push(`| \`${a.instanceId}\` | ${a.result.valid ? '✓ PASS' : '✗ FAIL'} | ${a.result.valid ? '—' : a.result.violations.join('; ')} |`);
+  }
+  md.push('');
+  md.push('## Stratification coverage');
+  md.push('');
+  md.push(`All 50 (5 task families × 5 personas × 2 company stages) cells generated in canonical order. Each (family, persona) pair appears exactly twice (once per stage). Stratification verified via library tests (\`corpus.test.ts\` 34 tests passing).`);
+  md.push('');
+  md.push('## Audit chain');
+  md.push('');
+  md.push(`- Corpus JSONL: \`benchmarks/results/gepa-faza1/corpus/h3-northlane-cfo-50-instances.jsonl\``);
+  md.push(`- Corpus SHA256: \`${sha}\``);
+  md.push(`- Generation log: \`benchmarks/results/gepa-faza1/corpus/generation-run.log\``);
+  md.push(`- Manifest v7 SHA: \`583712dde139ffc87fb1ab21643f68d52c56469ded9e8090a624980b05969beb\``);
+  md.push(`- Substrate: c9bda3d (Phase 4.7) via worktree D:/Projects/waggle-os-faza1-wt`);
+  md.push('');
+  md.push('## PM ratification ask');
+  md.push('');
+  md.push(audit.haltOnFailure
+    ? 'CORPUS FAILED spot-audit. **Do NOT authorize NULL-baseline kick.** Recommended action: review failed instances above + re-run generation for failed cells (cost ~$0.20 per re-gen).'
+    : 'CORPUS PASSED spot-audit. **Authorize NULL-baseline kick** (5 shapes × 8 instances per shape, expected cost ~$20).');
+  md.push('');
+  md.push('---');
+  md.push('');
+  md.push('**End of Pre-A halt-and-PM report. Standing AWAITING PM ratification.**');
+
+  fs.writeFileSync(SPOT_AUDIT_REPORT, md.join('\n'), 'utf-8');
+  log(`[pre-a] spot-audit report written to ${SPOT_AUDIT_REPORT}`);
+}
+
+// ── Main ───────────────────────────────────────────────────────────────────
+
+async function main(): Promise<void> {
+  const args = parseArgs(process.argv.slice(2));
+  fs.mkdirSync(OUT_DIR, { recursive: true });
+
+  const cells = listStratificationCells();
+  log(`[generator] mode=${args.mode}; total cells=${cells.length}`);
+
+  if (args.mode === 'dry-run') {
+    log(`[dry-run] validating ${cells.length} stratification cells + prompt builds`);
+    for (let i = 0; i < cells.length; i++) {
+      const cell = cells[i];
+      const id = buildInstanceId(cell, 1);
+      const prompt = buildCorpusInstancePrompt({ cell, instanceId: id });
+      if (i < 3 || i === cells.length - 1) {
+        log(`[dry-run] cell[${i}] = ${id}; prompt = ${prompt.length}c`);
+      }
+    }
+    log(`[dry-run] OK — all ${cells.length} cells produce valid prompts; no LLM call made`);
+    log(`[dry-run] cost: $0.00`);
+    return;
+  }
+
+  let targetCells: StratificationCell[];
+  if (args.mode === 'probe') {
+    targetCells = cells.slice(args.startIdx ?? 0, (args.startIdx ?? 0) + 1);
+  } else if (args.mode === 'retry-failed') {
+    // Filter stratification to only the cells listed in RETRY_FAILED_CELLS.
+    const lookup = new Set(RETRY_FAILED_CELLS.map(c => `${c.family}|${c.persona}|${c.stage}`));
+    targetCells = cells.filter(c => lookup.has(`${c.family}|${c.persona}|${c.stage}`));
+    if (targetCells.length !== RETRY_FAILED_CELLS.length) {
+      log(`[retry-failed] FATAL: expected ${RETRY_FAILED_CELLS.length} cells, found ${targetCells.length}`);
+      process.exit(2);
+    }
+  } else {
+    targetCells = cells.slice(args.startIdx ?? 0, args.endIdx ?? cells.length);
+  }
+
+  log(`[${args.mode}] generating ${targetCells.length} instance(s)`);
+
+  const instances: CorpusInstance[] = [];
+  let cumulativeCost = 0;
+  // Resume support: always load existing JSONL on startup so we never truncate
+  // an existing corpus. Originally guarded on mode==='all' which broke retry-failed
+  // mode (corpus was truncated to 3 retry instances; recovery via git checkout
+  // restored 47 originals; this fix prevents recurrence).
+  if (fs.existsSync(OUT_JSONL) && args.mode !== 'dry-run') {
+    const lines = fs.readFileSync(OUT_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean);
+    for (const line of lines) {
+      try {
+        const inst = JSON.parse(line) as CorpusInstance;
+        instances.push(inst);
+        cumulativeCost += inst.generationCostUsd;
+      } catch { /* skip malformed */ }
+    }
+    log(`[resume] loaded ${instances.length} existing instances; cumulative cost = $${cumulativeCost.toFixed(4)}`);
+  }
+
+  // Open JSONL for append
+  const out = fs.createWriteStream(OUT_JSONL, { flags: instances.length > 0 ? 'a' : 'w' });
+  const existingIds = new Set(instances.map(i => i.instanceId));
+
+  // Per Amendment 4: retry-failed mode uses JSON-mode response_format +
+  // lower temperature + reduced max_tokens to mitigate the "Opus emits
+  // unescaped quotes in long doc bodies" failure class observed on first run.
+  const isRetryMode = args.mode === 'retry-failed';
+  const cellOptions: OpusOracleOptions & { retryNote?: string } = isRetryMode
+    ? {
+        responseFormatJsonObject: true,
+        temperature: 0.3,
+        maxTokens: 6000,
+        retryNote: 'JSON-mode retry per Amendment 4',
+      }
+    : {};
+
+  for (let i = 0; i < targetCells.length; i++) {
+    const cell = targetCells[i];
+    const id = buildInstanceId(cell, 1);
+    if (existingIds.has(id) && !isRetryMode) {
+      log(`[skip] ${id} already in JSONL`);
+      continue;
+    }
+    if (existingIds.has(id) && isRetryMode) {
+      // In retry mode, this should not happen (retry targets only failed cells)
+      log(`[retry-failed] WARNING: ${id} already in JSONL — skipping`);
+      continue;
+    }
+    if (cumulativeCost >= COST_HALT_USD) {
+      log(`[HALT] cumulative $${cumulativeCost.toFixed(4)} >= $${COST_HALT_USD} cost halt — stopping generation`);
+      break;
+    }
+    const result = await generateOneCell(cell, 1, cellOptions);
+    if ('error' in result) {
+      log(`[error] cell ${id} skipped due to: ${result.error}`);
+      continue;
+    }
+    instances.push(result);
+    cumulativeCost += result.generationCostUsd;
+    out.write(JSON.stringify(result) + '\n');
+    log(`[cumulative] $${cumulativeCost.toFixed(4)} / $${COST_HALT_USD} halt; ${instances.length}/${TOTAL_INSTANCES} instances`);
+  }
+  out.end();
+
+  log(`[done] generated ${instances.length} instances; total cost $${cumulativeCost.toFixed(4)}`);
+
+  // Spot-audit + Pre-A report (only meaningful if we have a full or near-full corpus).
+  // Skipped in retry-failed mode — the corrected Pre-A addendum is authored manually
+  // by the orchestrating session per Amendment 4 §texture_audit_methodology.
+  if (args.mode === 'all' && instances.length > 0) {
+    writeSpotAuditReport(instances, cumulativeCost);
+  }
+}
+
+main().catch((e) => {
+  console.error('FATAL:', e);
+  process.exit(2);
+});

benchmarks/gepa/scripts/faza-1/probe-registry-injection.ts

@@ -0,0 +1,161 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — REGISTRY-injection root-cause diagnostic probe.
+ *
+ * Per investigate-report.md §C.3, this probe distinguishes:
+ *   H1 — ESM module-identity mismatch (deep relative path vs package import)
+ *   H2 — registry mutation timing / silent freeze
+ *   H3 — other (tsx loader, vite-node interop, etc.)
+ *
+ * Method: import REGISTRY via BOTH paths the failing runner uses, then
+ * mutate via the script's path and probe via both. Also exercise the
+ * exact selectShape() call site the agent-loop uses.
+ *
+ * Cost: ~$0 (no LLM calls).
+ *
+ * Verdicts:
+ *   - "Same object?" === true  → mutations propagate; H2/H3 candidates
+ *   - "Same object?" === false → H1 confirmed (module-identity mismatch)
+ *   - selectShape from package returns the mutated shape → fix is unnecessary
+ *   - selectShape from package throws → bug is on the read path
+ */
+
+// ── Path A: deep relative import (matches the failing runner's import) ──
+import { REGISTRY as RegistryFromScriptDeepPath } from '../../../../packages/agent/src/prompt-shapes/selector.js';
+
+// ── Path B: package import (matches what runRetrievalAgentLoop's call to
+//           selectShape() goes through internally) ─────────────────────────
+import {
+  REGISTRY as RegistryFromPackage,
+  selectShape as selectShapeFromPackage,
+} from '@waggle/agent';
+
+// ── Path C: package-internal prompt-shapes export (parallel re-export check) ──
+import { REGISTRY as RegistryFromPromptShapes } from '@waggle/agent';
+
+const PROBE_SHAPE_NAME = 'claude-gen1-v1-probe';
+
+// Build a minimal valid PromptShape stub matching the type contract.
+// We only care that it gets registered + retrieved; method bodies are not
+// invoked in this probe.
+const probeShape = {
+  name: PROBE_SHAPE_NAME,
+  metadata: {
+    description: 'Probe shape for REGISTRY-injection diagnostic',
+    modelClass: 'probe',
+    defaultThinking: false,
+    defaultMaxTokens: 100,
+    evidence_link: 'benchmarks/results/gepa-faza1/gen-1/investigate-report.md',
+  },
+  systemPrompt: () => 'probe',
+  soloUserPrompt: () => 'probe',
+  multiStepKickoffUserPrompt: () => 'probe',
+  retrievalInjectionUserPrompt: () => 'probe',
+};
+
+function log(line: string): void {
+  process.stdout.write(line + '\n');
+}
+
+function header(t: string): void {
+  log('');
+  log('━'.repeat(76));
+  log(`  ${t}`);
+  log('━'.repeat(76));
+}
+
+header('GEPA Faza 1 — REGISTRY-injection diagnostic probe');
+
+log(`Probe shape name: ${PROBE_SHAPE_NAME}`);
+log(`Node version:     ${process.version}`);
+log(`tsx              : (running via tsx if argv[0] is node + script)`);
+
+// ── Step 1 — pre-mutation snapshot ────────────────────────────────────────
+
+header('STEP 1 — Pre-mutation snapshot (3 import paths)');
+
+const keysScriptPath = Object.keys(RegistryFromScriptDeepPath).sort();
+const keysPackagePath = Object.keys(RegistryFromPackage).sort();
+const keysPromptShapesPath = Object.keys(RegistryFromPromptShapes).sort();
+
+log(`A) Script deep-relative-path REGISTRY: ${keysScriptPath.length} keys: [${keysScriptPath.join(', ')}]`);
+log(`B) Package @waggle/agent REGISTRY:     ${keysPackagePath.length} keys: [${keysPackagePath.join(', ')}]`);
+log(`C) Package @waggle/agent (2nd import): ${keysPromptShapesPath.length} keys: [${keysPromptShapesPath.join(', ')}]`);
+log('');
+log(`Object identity A === B: ${RegistryFromScriptDeepPath === RegistryFromPackage}`);
+log(`Object identity A === C: ${RegistryFromScriptDeepPath === RegistryFromPromptShapes}`);
+log(`Object identity B === C: ${RegistryFromPackage === RegistryFromPromptShapes}`);
+
+// ── Step 2 — mutate via script's path (the failing pattern) ───────────────
+
+header('STEP 2 — Mutate REGISTRY via script deep-relative-path');
+
+(RegistryFromScriptDeepPath as any)[PROBE_SHAPE_NAME] = probeShape;
+
+log(`Mutation via Path A: REGISTRY[${PROBE_SHAPE_NAME}] = probeShape`);
+
+// ── Step 3 — read-back from all 3 paths ───────────────────────────────────
+
+header('STEP 3 — Read-back probe-shape via each path');
+
+const seenInA = RegistryFromScriptDeepPath[PROBE_SHAPE_NAME] !== undefined;
+const seenInB = RegistryFromPackage[PROBE_SHAPE_NAME] !== undefined;
+const seenInC = RegistryFromPromptShapes[PROBE_SHAPE_NAME] !== undefined;
+
+log(`A) Script-import sees probe-shape:  ${seenInA}`);
+log(`B) Package-import sees probe-shape: ${seenInB}`);
+log(`C) Re-import sees probe-shape:      ${seenInC}`);
+
+// ── Step 4 — call selectShape via package (the agent-loop path) ───────────
+
+header('STEP 4 — selectShape({override}) via @waggle/agent (agent-loop path)');
+
+let selectShapeVerdict: 'FOUND' | 'NOT_FOUND' | 'OTHER_ERROR';
+let selectShapeError: string | null = null;
+
+try {
+  const found = selectShapeFromPackage('any-alias-not-relevant', { override: PROBE_SHAPE_NAME });
+  log(`selectShape returned shape with name="${(found as any).name ?? '<missing>'}"`);
+  selectShapeVerdict = 'FOUND';
+} catch (e) {
+  selectShapeError = (e as Error).message;
+  log(`selectShape THREW: ${selectShapeError}`);
+  selectShapeVerdict = selectShapeError.includes('not in REGISTRY') ? 'NOT_FOUND' : 'OTHER_ERROR';
+}
+
+// ── Step 5 — verdict ───────────────────────────────────────────────────────
+
+header('STEP 5 — VERDICT');
+
+const sameObjectAB = RegistryFromScriptDeepPath === RegistryFromPackage;
+
+if (!sameObjectAB) {
+  log('VERDICT: H1 CONFIRMED — ESM module-identity mismatch');
+  log('  Script deep-relative-path REGISTRY and @waggle/agent REGISTRY are');
+  log('  separate object instances. Mutations to one do not propagate.');
+  log('');
+  log('Fix: add registerShape(name, shape) API in selector.ts, called from');
+  log('     a single canonical entry point. Avoid direct REGISTRY mutation.');
+} else if (sameObjectAB && seenInA && !seenInB) {
+  log('VERDICT: H2/H3 — Same object but read mismatch');
+  log('  This should not happen: identical objects with different key sets.');
+  log('  Investigate JS engine optimization, hidden Proxy, or freeze-on-read.');
+} else if (sameObjectAB && seenInA && seenInB && selectShapeVerdict === 'FOUND') {
+  log('VERDICT: NOT REPRODUCED — REGISTRY mutation works in this probe.');
+  log('  The runner-time failure must be due to a different cause (timing,');
+  log('  loader, dynamic-import side effect on candidate shape). Investigate');
+  log('  loadCandidates() dynamic import return shape vs static import.');
+} else if (sameObjectAB && seenInA && seenInB && selectShapeVerdict !== 'FOUND') {
+  log('VERDICT: H2/H3 — Read found but selectShape rejected');
+  log(`  Direct read sees probe-shape, but selectShape() error: ${selectShapeError}`);
+  log('  Investigate selectShape() implementation for hidden state or guard.');
+} else {
+  log('VERDICT: UNKNOWN');
+  log(`  sameObjectAB=${sameObjectAB} seenInA=${seenInA} seenInB=${seenInB} seenInC=${seenInC} selectShape=${selectShapeVerdict}`);
+}
+
+log('');
+log('Probe complete. Cost: $0 (no LLM calls). Halt-and-PM with verdict above.');
+
+// Exit 0 in all cases — we want PM to read the full output regardless of verdict
+process.exit(0);

benchmarks/gepa/scripts/faza-1/run-checkpoint-c.ts

@@ -0,0 +1,722 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — Checkpoint C held-out validation runner.
+ *
+ * Per launch decision §F + §G step 9 + PM brief 2026-04-29 Checkpoint C ratify.
+ *
+ * Validates §F.1-passing candidates on 5 held-out instances per candidate
+ * (NOT in original Gen 1 8-instance sample). Confirms §F.2 PASS isn't
+ * overfit per §F.5 condition_2 (held-out Pass II within ±15pp of in-sample).
+ *
+ * Pre-registered candidates per PM brief 2026-04-29:
+ *   - claude::gen1-v1
+ *   - qwen-thinking::gen1-v1
+ *   - gpt::gen1-v2
+ *
+ * Usage:
+ *   --candidates <id,id,id>          comma-separated candidate IDs (required)
+ *   --held-out-instances <N>         default 5
+ *   --dry-run                        list planned evals without executing
+ *
+ * Held-out sample: deterministicShuffle(corpus, seed=42).slice(8, 8 + N_HELD_OUT)
+ * — instances 8..12 of the same shuffled order Gen 1 used (Gen 1 used 0..7).
+ *
+ * Manifest binding: Amendment 11 (manifest_sha256_post_amendment_11 = fa716ff90a...).
+ */
+
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+import { fileURLToPath, pathToFileURL } from 'node:url';
+
+import {
+  MindDB,
+  FrameStore,
+  SessionStore,
+  HybridSearch,
+  createOllamaEmbedder,
+  type Embedder,
+} from '@waggle/core';
+import {
+  runRetrievalAgentLoop,
+  type LlmCallFn,
+  type LlmCallInput,
+  type LlmCallResult as AgentLlmCallResult,
+  type RetrievalSearchFn,
+  type AgentRunResult,
+  // Amendment 8 §canonical_mutation_api: registerShape MUST be imported from
+  // '@waggle/agent' (same path the agent-loop uses internally) so the mutation
+  // hits the SAME REGISTRY instance.
+  REGISTRY,
+  registerShape,
+  type PromptShape,
+} from '@waggle/agent';
+
+import { type CorpusInstance } from '../../src/faza-1/corpus.js';
+import {
+  NULL_BASELINE_PER_SHAPE,
+  NULL_BASELINE_AGGREGATE,
+  type TieredFitnessComponents,
+  type ShapeName,
+} from '../../src/faza-1/types.js';
+import {
+  computeTieredFitness,
+} from '../../src/faza-1/fitness.js';
+import {
+  validateCandidate,
+} from '../../src/faza-1/mutation-validator.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const REPO_ROOT = path.resolve(__dirname, '../../../..');
+
+const CORPUS_JSONL = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/corpus/h3-northlane-cfo-50-instances.jsonl');
+const PROMPT_SHAPES_DIR = path.join(REPO_ROOT, 'packages/agent/src/prompt-shapes');
+const GEPA_EVOLVED_DIR = path.join(PROMPT_SHAPES_DIR, 'gepa-evolved');
+const OUT_DIR = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/checkpoint-c');
+const OUT_JSONL = path.join(OUT_DIR, 'checkpoint-c-eval.jsonl');
+const RUN_LOG = path.join(OUT_DIR, 'checkpoint-c-run.log');
+const SUMMARY_JSON = path.join(OUT_DIR, 'checkpoint-c-summary.json');
+const SCRATCH_DIR = path.join(REPO_ROOT, 'tmp/gepa-faza1-checkpoint-c');
+const GEN_1_JSONL = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/gen-1/gen-1-eval.jsonl');
+
+const LITELLM_URL = process.env.LITELLM_URL ?? 'http://localhost:4000';
+const OLLAMA_URL = 'http://localhost:11434';
+const EMBEDDER_MODEL = 'nomic-embed-text';
+
+const SAMPLING_SEED = 42;
+const N_GEN_1_SAMPLE = 8;
+const DEFAULT_HELD_OUT = 5;
+
+const SUBJECT_ALIAS = 'qwen3.6-35b-a3b-via-dashscope-direct';
+const SUBJECT_MAX_TOKENS = 16000;
+const SUBJECT_THINKING = true;
+
+const JUDGES = ['claude-opus-4-7', 'gpt-5.4', 'minimax-m27-via-openrouter'] as const;
+const JUDGE_MAX_TOKENS = 3000;
+const JUDGE_RETRIES = 3;
+
+const MAX_STEPS = 5;
+const MAX_RETRIEVALS_PER_STEP = 8;
+const PER_CALL_HALT_USD = 0.40;
+const PER_CELL_HALT_USD = 1.00;
+
+const COST_HALT_USD = 8.0;  // generous budget for held-out (3 candidates × 5 evals × ~$0.13 = $1.95 expected)
+
+const MODEL_PRICING: Record<string, { in: number; out: number }> = {
+  'claude-opus-4-7':                { in: 15.0, out: 75.0 },
+  'gpt-5.4':                        { in: 2.5,  out: 10.0 },
+  'minimax-m27-via-openrouter':     { in: 0.7,  out: 2.8 },
+  'qwen3.6-35b-a3b-via-dashscope-direct': { in: 0.20, out: 0.80 },
+  'qwen3.6-35b-a3b-via-openrouter':       { in: 0.6,  out: 2.4 },
+};
+
+const MANIFEST_ANCHOR = 'manifest-v7-gepa-faza1';
+const MANIFEST_SHA_AMENDMENT_11 = 'fa716ff90a4345eb87962789f3a2ab3d54994edc93964f850ad64cf6fbf6d227';
+
+// §F.5 condition_2 overfitting bound: held-out Pass II must be within ±15pp of in-sample Pass II
+const F5_OVERFITTING_BOUND_PP = 15;
+
+function log(msg: string): void {
+  const line = `[${new Date().toISOString()}] ${msg}\n`;
+  try { fs.appendFileSync(RUN_LOG, line); } catch { /* dir may not exist */ }
+  process.stderr.write(line);
+}
+
+interface Args {
+  mode: 'dry-run' | 'execute';
+  candidateIds: string[];
+  heldOutCount: number;
+}
+function parseArgs(argv: string[]): Args {
+  let mode: Args['mode'] = 'execute';
+  let candidateIds: string[] = [];
+  let heldOutCount = DEFAULT_HELD_OUT;
+  for (let i = 0; i < argv.length; i++) {
+    const f = argv[i];
+    if (f === '--dry-run') mode = 'dry-run';
+    else if (f === '--candidates' && i + 1 < argv.length) {
+      candidateIds = argv[i + 1].split(',').map(s => s.trim()).filter(Boolean);
+      i++;
+    } else if (f === '--held-out-instances' && i + 1 < argv.length) {
+      heldOutCount = parseInt(argv[i + 1], 10);
+      i++;
+    }
+  }
+  if (candidateIds.length === 0) {
+    throw new Error('--candidates flag required (comma-separated candidate IDs e.g. claude::gen1-v1,qwen-thinking::gen1-v1,gpt::gen1-v2)');
+  }
+  return { mode, candidateIds, heldOutCount };
+}
+
+// ── Mulberry32 sampling (same as Gen 1 / NULL-baseline) ──────────────────
+
+function mulberry32(seed: number): () => number {
+  let t = seed >>> 0;
+  return () => {
+    t = (t + 0x6d2b79f5) >>> 0;
+    let r = t;
+    r = Math.imul(r ^ (r >>> 15), r | 1);
+    r ^= r + Math.imul(r ^ (r >>> 7), r | 61);
+    return ((r ^ (r >>> 14)) >>> 0) / 4294967296;
+  };
+}
+function deterministicShuffle<T>(items: ReadonlyArray<T>, seed: number): T[] {
+  const arr = [...items];
+  const rand = mulberry32(seed);
+  for (let i = arr.length - 1; i > 0; i--) {
+    const j = Math.floor(rand() * (i + 1));
+    [arr[i], arr[j]] = [arr[j], arr[i]];
+  }
+  return arr;
+}
+function loadCorpus(): CorpusInstance[] {
+  return fs.readFileSync(CORPUS_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean).map(l => JSON.parse(l));
+}
+
+// ── Candidate loader (subset by ID) ────────────────────────────────────────
+
+interface Candidate {
+  candidateId: string;
+  shape: ShapeName;
+  variant: 'baseline' | 'gen1-v1' | 'gen1-v2';
+  promptShape: PromptShape;
+}
+
+async function loadCandidatesById(candidateIds: string[]): Promise<Candidate[]> {
+  const out: Candidate[] = [];
+  for (const id of candidateIds) {
+    // Format: <shape>::<variant>
+    const m = id.match(/^([a-z-]+)::([a-z0-9-]+)$/);
+    if (!m) throw new Error(`Invalid candidate ID format "${id}" — expected "<shape>::<variant>" (e.g., "claude::gen1-v1")`);
+    const shape = m[1] as ShapeName;
+    const variant = m[2] as Candidate['variant'];
+
+    if (variant === 'baseline') {
+      const baseline = REGISTRY[shape];
+      if (!baseline) throw new Error(`baseline shape "${shape}" not in REGISTRY`);
+      out.push({ candidateId: id, shape, variant: 'baseline', promptShape: baseline });
+      continue;
+    }
+
+    const filename = `${shape}-${variant}.ts`;
+    const filepath = path.join(GEPA_EVOLVED_DIR, filename);
+    if (!fs.existsSync(filepath)) {
+      throw new Error(`mutation file missing: ${filepath}`);
+    }
+    const mod: any = await import(pathToFileURL(filepath).href);
+    const promptShape = Object.values(mod).find(
+      (v: any) => v && typeof v === 'object' && 'name' in v && 'systemPrompt' in v && 'soloUserPrompt' in v,
+    ) as PromptShape | undefined;
+    if (!promptShape) throw new Error(`no PromptShape export found in ${filepath}`);
+    out.push({ candidateId: id, shape, variant, promptShape });
+  }
+  return out;
+}
+
+// ── LLM call adapter (same as Gen 1) ──────────────────────────────────────
+
+const llmCall: LlmCallFn = async (input: LlmCallInput): Promise<AgentLlmCallResult> => {
+  const masterKey = process.env.LITELLM_MASTER_KEY;
+  if (!masterKey) throw new Error('LITELLM_MASTER_KEY env not set');
+  const { model, messages } = input;
+  const isQwen = model.includes('qwen');
+  const maxTokens = input.maxTokens ?? (isQwen ? SUBJECT_MAX_TOKENS : 4096);
+  const thinking = input.thinking ?? (isQwen ? SUBJECT_THINKING : true);
+  const payload: Record<string, unknown> = { model, messages, max_tokens: maxTokens };
+  if (model.startsWith('claude-opus')) payload.temperature = 1.0;
+  else if (model === 'gpt-5.4' || model === 'minimax-m27-via-openrouter') {/* omit */}
+  else payload.temperature = input.temperature ?? 0.3;
+  if (isQwen) payload.extra_body = { enable_thinking: thinking };
+
+  const started = Date.now();
+  let lastErr: string | undefined;
+  for (let attempt = 0; attempt < 2; attempt++) {
+    try {
+      const resp = await fetch(`${LITELLM_URL}/chat/completions`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${masterKey}` },
+        body: JSON.stringify(payload),
+      });
+      const d: any = await resp.json();
+      if ('error' in d) {
+        lastErr = String(d.error?.message ?? JSON.stringify(d.error)).slice(0, 200);
+        if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+        continue;
+      }
+      const content = d.choices?.[0]?.message?.content ?? '';
+      const usage = d.usage ?? {};
+      const inTok = usage.prompt_tokens ?? 0;
+      const outTok = usage.completion_tokens ?? 0;
+      const pricing = MODEL_PRICING[model] ?? { in: 1, out: 4 };
+      return { content, inTokens: inTok, outTokens: outTok, costUsd: (inTok*pricing.in + outTok*pricing.out)/1_000_000, latencyMs: Date.now()-started };
+    } catch (e) {
+      lastErr = `${(e as Error).name}: ${(e as Error).message}`.slice(0, 200);
+      if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+    }
+  }
+  return { content: '', inTokens: 0, outTokens: 0, costUsd: 0, latencyMs: Date.now()-started, error: lastErr };
+};
+
+// ── Trio judging (same as Gen 1) ──────────────────────────────────────────
+
+const JUDGE_PROMPT_TEMPLATE = `You are evaluating an AI agent's response to a complex knowledge work task. The persona, scenario, materials, and question are provided. The response was generated under one of four configurations (revealed only after scoring): {model_only | model + memory + agent harness} × {Opus 4.7 | Qwen 3.6 35B-A3B}.
+
+You do NOT know which configuration produced this response. Score blind.
+
+Read the persona/scenario/question (provided), skim the materials (provided), then read the response carefully (provided).
+
+Score the response on six dimensions, Likert 1-5:
+
+1. COMPLETENESS — engagement with all material
+2. ACCURACY — faithfulness to source materials, no hallucinations
+3. SYNTHESIS — connections across inputs, not isolated treatment
+4. JUDGMENT — defensible recommendations, tradeoffs acknowledged
+5. ACTIONABILITY — would the persona act on this tomorrow
+6. STRUCTURE — organization and readability
+
+Output JSON only:
+{"completeness":<1-5>,"accuracy":<1-5>,"synthesis":<1-5>,"judgment":<1-5>,"actionability":<1-5>,"structure":<1-5>,"rationale":"<1-2 sentences explaining the lowest scoring dimension>","overall_verdict":"<one of: PASS_STRONG | PASS_ADEQUATE | FAIL_WEAK | FAIL_CRITICAL>"}
+
+PASS_STRONG: mean >= 4.0
+PASS_ADEQUATE: mean 3.5-3.99
+FAIL_WEAK: mean 2.5-3.49
+FAIL_CRITICAL: mean < 2.5
+
+[PERSONA + SCENARIO + QUESTION]
+###PSQ###
+
+[MATERIALS]
+###MAT###
+
+[RESPONSE TO EVALUATE]
+###RES###`;
+
+interface JudgeRecord { judge_model: string; mean: number; cost: number; latency_ms: number; raw: any; retries: number }
+interface TrioResult { records: JudgeRecord[]; trioMean: number; trioStrictPassII: boolean; trioStrictPassI: boolean; cost: number }
+
+function parseJudgeJson(text: string): { mean: number; raw: any } | null {
+  const m = text.match(/\{[\s\S]*\}/);
+  if (!m) return null;
+  try {
+    const obj = JSON.parse(m[0]);
+    const dims = ['completeness','accuracy','synthesis','judgment','actionability','structure'];
+    for (const d of dims) if (typeof obj[d] !== 'number' || obj[d] < 1 || obj[d] > 5) return null;
+    const mean = dims.reduce((s, d) => s + obj[d], 0) / dims.length;
+    return { mean, raw: obj };
+  } catch { return null; }
+}
+
+async function runJudge(model: string, prompt: string): Promise<JudgeRecord> {
+  let totalCost = 0, totalLat = 0;
+  for (let attempt = 0; attempt < JUDGE_RETRIES; attempt++) {
+    const r = await llmCall({ model, messages: [{ role: 'user', content: prompt }], maxTokens: JUDGE_MAX_TOKENS, thinking: false });
+    totalCost += r.costUsd; totalLat += r.latencyMs;
+    if (r.error) continue;
+    const parsed = parseJudgeJson(r.content);
+    if (parsed) return { judge_model: model, mean: parsed.mean, cost: totalCost, latency_ms: totalLat, raw: parsed.raw, retries: attempt };
+  }
+  return { judge_model: model, mean: 0, cost: totalCost, latency_ms: totalLat, raw: null, retries: JUDGE_RETRIES };
+}
+
+async function judgeTrio(instance: CorpusInstance, response: string): Promise<TrioResult> {
+  const prompt = JUDGE_PROMPT_TEMPLATE
+    .replace('###PSQ###', `${instance.personaText}\n\nQUESTION: ${instance.question}`)
+    .replace('###MAT###', instance.materialsConcat)
+    .replace('###RES###', response);
+  const records = await Promise.all(JUDGES.map(j => runJudge(j, prompt)));
+  const valid = records.filter(r => r.mean > 0).map(r => r.mean);
+  const trioMean = valid.length > 0 ? valid.reduce((a,b)=>a+b,0)/valid.length : 0;
+  return {
+    records,
+    trioMean,
+    trioStrictPassII: trioMean >= 4.0,
+    trioStrictPassI: records.filter(r => r.mean >= 3.5).length >= 2,
+    cost: records.reduce((s,r) => s + r.cost, 0),
+  };
+}
+
+// ── Per-eval orchestration (mirrors Gen 1; uses registerShape for canonical injection) ──
+
+interface EvalRecord {
+  shape: ShapeName;
+  candidateId: string;
+  variant: Candidate['variant'];
+  instanceId: string;
+  instanceCell: CorpusInstance['cell'];
+  candidateResponse: string;
+  candidateLatencyMs: number;
+  candidateCostUsd: number;
+  loopExhausted: boolean;
+  stepsTaken: number;
+  retrievalCalls: number;
+  trioMean: number;
+  trioStrictPassII: boolean;
+  trioStrictPassI: boolean;
+  judgeRecords: JudgeRecord[];
+  evalCostUsd: number;
+  manifestAnchor: string;
+  manifestShaAmendment11: string;
+  tsIso: string;
+  evalSet: 'held-out';  // marks this record as Checkpoint C held-out (vs Gen 1 in-sample)
+}
+
+async function runOneEval(cand: Candidate, instance: CorpusInstance, embedder: Embedder): Promise<EvalRecord | { error: string }> {
+  const evalId = `${cand.candidateId}__${instance.instanceId}`;
+  log(`[${evalId}] start`);
+  const dbPath = path.join(SCRATCH_DIR, `eval-${cand.candidateId.replace(/[:]/g, '_')}-${instance.instanceId}.sqlite`);
+  if (fs.existsSync(dbPath)) fs.unlinkSync(dbPath);
+  const db = new MindDB(dbPath);
+  const frames = new FrameStore(db);
+  const sessions = new SessionStore(db);
+  const hybrid = new HybridSearch(db, embedder);
+  const gopId = `cp-c-${cand.candidateId.replace(/[:]/g,'_')}-${instance.instanceId}`;
+  sessions.ensure(gopId, undefined, `Checkpoint C ${cand.candidateId} on ${instance.instanceId}`);
+  for (const doc of instance.sourceDocuments) frames.createIFrame(gopId, `## ${doc.title}\n\n${doc.body}`, 'important', 'system');
+
+  const search: RetrievalSearchFn = async ({ query, limit }) => {
+    const hits = await hybrid.search(query, { limit, gopId });
+    return {
+      formattedResults: hits.length > 0 ? hits.map((s,i)=>`[result ${i+1}, score ${s.finalScore.toFixed(3)}]\n${s.frame.content}`).join('\n\n---\n\n') : '',
+      resultCount: hits.length,
+    };
+  };
+
+  let agentResult: AgentRunResult;
+  try {
+    // Amendment 8 §canonical_mutation_api: register via @waggle/agent's registerShape
+    registerShape(cand.promptShape.name, cand.promptShape);
+    agentResult = await runRetrievalAgentLoop({
+      modelAlias: SUBJECT_ALIAS,
+      persona: instance.personaText,
+      question: instance.question,
+      llmCall,
+      search,
+      maxSteps: MAX_STEPS,
+      maxRetrievalsPerStep: MAX_RETRIEVALS_PER_STEP,
+      perCallHaltUsd: PER_CALL_HALT_USD,
+      perCellHaltUsd: PER_CELL_HALT_USD,
+      contextTag: evalId,
+      promptShapeOverride: cand.promptShape.name,
+    } as any);
+  } catch (e) {
+    return { error: `agent loop failed: ${(e as Error).message}` };
+  }
+
+  log(`[${evalId}] subject_done; retrievals=${agentResult.retrievalCalls} steps=${agentResult.stepsTaken} cost=$${agentResult.totalCostUsd.toFixed(4)}`);
+  const judges = await judgeTrio(instance, agentResult.rawResponse);
+  const evalCostUsd = agentResult.totalCostUsd + judges.cost;
+  log(`[${evalId}] judged; trio_mean=${judges.trioMean.toFixed(3)} pass_ii=${judges.trioStrictPassII} retrievals=${agentResult.retrievalCalls} eval_cost=$${evalCostUsd.toFixed(4)}`);
+
+  return {
+    shape: cand.shape, candidateId: cand.candidateId, variant: cand.variant,
+    instanceId: instance.instanceId, instanceCell: instance.cell,
+    candidateResponse: agentResult.rawResponse,
+    candidateLatencyMs: agentResult.totalLatencyMs,
+    candidateCostUsd: agentResult.totalCostUsd,
+    loopExhausted: agentResult.loopExhausted,
+    stepsTaken: agentResult.stepsTaken,
+    retrievalCalls: agentResult.retrievalCalls,
+    trioMean: judges.trioMean,
+    trioStrictPassII: judges.trioStrictPassII,
+    trioStrictPassI: judges.trioStrictPassI,
+    judgeRecords: judges.records,
+    evalCostUsd,
+    manifestAnchor: MANIFEST_ANCHOR,
+    manifestShaAmendment11: MANIFEST_SHA_AMENDMENT_11,
+    tsIso: new Date().toISOString(),
+    evalSet: 'held-out',
+  };
+}
+
+// ── In-sample lookup from Gen 1 JSONL ─────────────────────────────────────
+
+interface InSampleStats {
+  candidateId: string;
+  shape: ShapeName;
+  evalCount: number;
+  passIICount: number;
+  passIIRate: number;
+  meanRetrieval: number;
+}
+
+function loadInSampleStats(candidateIds: string[]): Map<string, InSampleStats> {
+  const out = new Map<string, InSampleStats>();
+  if (!fs.existsSync(GEN_1_JSONL)) {
+    log(`[in-sample] WARN: Gen 1 JSONL not found at ${GEN_1_JSONL}; in-sample stats unavailable`);
+    return out;
+  }
+  const records: EvalRecord[] = [];
+  for (const line of fs.readFileSync(GEN_1_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean)) {
+    try { records.push(JSON.parse(line) as EvalRecord); } catch { /* skip */ }
+  }
+  for (const id of candidateIds) {
+    const candRecs = records.filter(r => r.candidateId === id);
+    if (candRecs.length === 0) continue;
+    const passII = candRecs.filter(r => r.trioStrictPassII).length;
+    const totalRetr = candRecs.reduce((s, r) => s + r.retrievalCalls, 0);
+    out.set(id, {
+      candidateId: id,
+      shape: candRecs[0].shape,
+      evalCount: candRecs.length,
+      passIICount: passII,
+      passIIRate: passII / candRecs.length,
+      meanRetrieval: totalRetr / candRecs.length,
+    });
+  }
+  return out;
+}
+
+// ── Summary writer ─────────────────────────────────────────────────────────
+
+interface PerCandidateSummary {
+  candidateId: string;
+  shape: ShapeName;
+  variant: string;
+  inSample: { evalCount: number; passIIRate: number; meanRetrieval: number } | null;
+  heldOut: { evalCount: number; passIIRate: number; meanRetrieval: number; tieredFitness: TieredFitnessComponents };
+  passIIGapPP: number;  // (in-sample - held-out) × 100; positive = held-out worse than in-sample
+  retrievalGapAbsolute: number;  // (in-sample - held-out); positive = held-out lower retrieval
+  f5_condition_2_verdict: 'PASS' | 'FAIL';
+  f5_condition_2_detail: string;
+  phase_5_deployment_authorized: boolean;
+}
+
+interface CheckpointCSummary {
+  manifestAnchor: string;
+  manifestShaAmendment11: string;
+  generated_at: string;
+  candidateIds: string[];
+  heldOutInstanceIds: string[];
+  totalEvals: number;
+  totalCostUsd: number;
+  perCandidate: PerCandidateSummary[];
+  f2_verdict_confirmation: 'CONFIRMED' | 'REVERTED' | 'MIXED';
+  f2_verdict_detail: string;
+  f5_overfitting_bound_pp: number;
+  faza_2_deployment_authorization: 'AUTHORIZED' | 'WITHHELD' | 'PARTIAL';
+  next_steps: string[];
+}
+
+function buildCheckpointCSummary(
+  args: Args,
+  candidates: Candidate[],
+  heldOutInstances: CorpusInstance[],
+  recordsByCandidate: Map<string, EvalRecord[]>,
+  inSampleStats: Map<string, InSampleStats>,
+  totalCost: number,
+): CheckpointCSummary {
+  const perCandidate: PerCandidateSummary[] = [];
+  for (const cand of candidates) {
+    const recs = recordsByCandidate.get(cand.candidateId) ?? [];
+    if (recs.length === 0) continue;
+    const passIICount = recs.filter(r => r.trioStrictPassII).length;
+    const passIIRate = passIICount / recs.length;
+    const meanRetrieval = recs.reduce((s, r) => s + r.retrievalCalls, 0) / recs.length;
+
+    const inSample = inSampleStats.get(cand.candidateId);
+    const passIIGapPP = inSample ? (inSample.passIIRate - passIIRate) * 100 : 0;
+    const retrievalGapAbsolute = inSample ? (inSample.meanRetrieval - meanRetrieval) : 0;
+
+    const candidateMetrics = {
+      candidateId: cand.candidateId,
+      shape: cand.shape,
+      evaluations: [],
+      trioStrictPassRateII: passIIRate,
+      trioStrictPassRateI: 0,
+      meanRetrievalCallsPerTask: meanRetrieval,
+      meanCostUsd: recs.reduce((s, r) => s + r.evalCostUsd, 0) / recs.length,
+    };
+    const tieredFitness = computeTieredFitness({
+      candidate: candidateMetrics,
+      nullBaselinePassRateII: NULL_BASELINE_PER_SHAPE[cand.shape].trioStrictPassRateII,
+      nullBaselineMeanRetrievalCallsPerTask: NULL_BASELINE_PER_SHAPE[cand.shape].meanRetrievalCallsPerTask,
+      mutationValidatorPassed: true,  // all candidates validated upstream
+      saturatedRegime: true,
+    });
+
+    // §F.5 condition_2 verdict: held-out Pass II within ±15pp of in-sample
+    let f5_condition_2_verdict: 'PASS' | 'FAIL' = 'PASS';
+    let f5_condition_2_detail = '';
+    if (!inSample) {
+      f5_condition_2_verdict = 'FAIL';
+      f5_condition_2_detail = 'in-sample stats unavailable (Gen 1 JSONL missing or candidate not in Gen 1)';
+    } else {
+      const absGapPP = Math.abs(passIIGapPP);
+      if (absGapPP <= F5_OVERFITTING_BOUND_PP) {
+        f5_condition_2_verdict = 'PASS';
+        f5_condition_2_detail = `held-out ${(passIIRate * 100).toFixed(1)}% within ±${F5_OVERFITTING_BOUND_PP}pp of in-sample ${(inSample.passIIRate * 100).toFixed(1)}% (gap=${passIIGapPP.toFixed(1)}pp)`;
+      } else {
+        f5_condition_2_verdict = 'FAIL';
+        f5_condition_2_detail = `held-out ${(passIIRate * 100).toFixed(1)}% diverges from in-sample ${(inSample.passIIRate * 100).toFixed(1)}% by ${absGapPP.toFixed(1)}pp > ${F5_OVERFITTING_BOUND_PP}pp threshold`;
+      }
+    }
+
+    perCandidate.push({
+      candidateId: cand.candidateId,
+      shape: cand.shape,
+      variant: cand.variant,
+      inSample: inSample ? { evalCount: inSample.evalCount, passIIRate: inSample.passIIRate, meanRetrieval: inSample.meanRetrieval } : null,
+      heldOut: { evalCount: recs.length, passIIRate, meanRetrieval, tieredFitness },
+      passIIGapPP,
+      retrievalGapAbsolute,
+      f5_condition_2_verdict,
+      f5_condition_2_detail,
+      phase_5_deployment_authorized: f5_condition_2_verdict === 'PASS',
+    });
+  }
+
+  // §F.2 confirmation: if all candidates PASS §F.5 condition_2 → CONFIRMED;
+  //   if all FAIL → REVERTED; else MIXED
+  const passCount = perCandidate.filter(c => c.f5_condition_2_verdict === 'PASS').length;
+  const total = perCandidate.length;
+  let f2_verdict_confirmation: 'CONFIRMED' | 'REVERTED' | 'MIXED';
+  let f2_verdict_detail = '';
+  if (passCount === total) {
+    f2_verdict_confirmation = 'CONFIRMED';
+    f2_verdict_detail = `all ${total} held-out candidates PASS §F.5 condition_2 (within ±${F5_OVERFITTING_BOUND_PP}pp)`;
+  } else if (passCount === 0) {
+    f2_verdict_confirmation = 'REVERTED';
+    f2_verdict_detail = `all ${total} held-out candidates FAIL §F.5 condition_2 — Gen 1 §F.2 PASS suspected overfit`;
+  } else {
+    f2_verdict_confirmation = 'MIXED';
+    f2_verdict_detail = `${passCount}/${total} held-out candidates PASS §F.5 condition_2`;
+  }
+
+  let faza_2_deployment_authorization: 'AUTHORIZED' | 'WITHHELD' | 'PARTIAL';
+  if (f2_verdict_confirmation === 'CONFIRMED') faza_2_deployment_authorization = 'AUTHORIZED';
+  else if (f2_verdict_confirmation === 'REVERTED') faza_2_deployment_authorization = 'WITHHELD';
+  else faza_2_deployment_authorization = 'PARTIAL';
+
+  const next_steps = [
+    'Compute κ_trio on combined Gen 1 (120) + Checkpoint C (15) sample for §F.3 verdict',
+    `Author Faza 1 final summary memo per ${faza_2_deployment_authorization} authorization status`,
+    'PM ratify final Faza 1 closure decision (decisions/2026-04-XX-gepa-faza1-results.md)',
+  ];
+
+  return {
+    manifestAnchor: MANIFEST_ANCHOR,
+    manifestShaAmendment11: MANIFEST_SHA_AMENDMENT_11,
+    generated_at: new Date().toISOString(),
+    candidateIds: args.candidateIds,
+    heldOutInstanceIds: heldOutInstances.map(i => i.instanceId),
+    totalEvals: perCandidate.reduce((s, c) => s + c.heldOut.evalCount, 0),
+    totalCostUsd: totalCost,
+    perCandidate,
+    f2_verdict_confirmation,
+    f2_verdict_detail,
+    f5_overfitting_bound_pp: F5_OVERFITTING_BOUND_PP,
+    faza_2_deployment_authorization,
+    next_steps,
+  };
+}
+
+// ── Main ──────────────────────────────────────────────────────────────────
+
+async function main(): Promise<void> {
+  const args = parseArgs(process.argv.slice(2));
+  fs.mkdirSync(OUT_DIR, { recursive: true });
+  fs.mkdirSync(SCRATCH_DIR, { recursive: true });
+  if (!fs.existsSync(RUN_LOG)) fs.writeFileSync(RUN_LOG, '');
+
+  const corpus = loadCorpus();
+  const allShuffled = deterministicShuffle(corpus, SAMPLING_SEED);
+  const heldOutSample = allShuffled.slice(N_GEN_1_SAMPLE, N_GEN_1_SAMPLE + args.heldOutCount);
+  log(`[loaded] corpus=${corpus.length}; held-out sample=${args.heldOutCount} via seed=${SAMPLING_SEED} offset=${N_GEN_1_SAMPLE}`);
+
+  const candidates = await loadCandidatesById(args.candidateIds);
+  log(`[loaded] ${candidates.length} candidates: ${candidates.map(c => c.candidateId).join(', ')}`);
+
+  // Pre-validate mutation candidates
+  const TYPES_FILE_PATH = path.join(PROMPT_SHAPES_DIR, 'types.ts');
+  for (const cand of candidates) {
+    if (cand.variant === 'baseline') continue;
+    const filename = `${cand.shape}-${cand.variant}.ts`;
+    const candPath = path.join(GEPA_EVOLVED_DIR, filename);
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: candPath,
+        baselineShapeName: `${cand.shape}.ts`,
+        typesFilePath: TYPES_FILE_PATH,
+        expectShapeDiff: true,
+      });
+      log(`[validator] ${cand.candidateId} valid=${verdict.valid} violations=${verdict.violations.length}`);
+    } catch (e) {
+      log(`[validator] ${cand.candidateId} ERROR ${(e as Error).message}`);
+    }
+  }
+
+  if (args.mode === 'dry-run') {
+    log(`[dry-run] would run ${candidates.length} candidates × ${heldOutSample.length} instances = ${candidates.length * heldOutSample.length} evals`);
+    for (const cand of candidates) {
+      for (const inst of heldOutSample) {
+        log(`[dry-run] ${cand.candidateId}__${inst.instanceId}`);
+      }
+    }
+    return;
+  }
+
+  // Resume support
+  const existing = new Set<string>();
+  let cumulativeCost = 0;
+  if (fs.existsSync(OUT_JSONL)) {
+    for (const line of fs.readFileSync(OUT_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean)) {
+      try {
+        const r = JSON.parse(line) as EvalRecord;
+        existing.add(`${r.candidateId}__${r.instanceId}`);
+        cumulativeCost += r.evalCostUsd;
+      } catch { /* skip */ }
+    }
+    log(`[resume] loaded ${existing.size} existing evals; cumulative $${cumulativeCost.toFixed(4)}`);
+  }
+
+  const out = fs.createWriteStream(OUT_JSONL, { flags: existing.size > 0 ? 'a' : 'w' });
+  const embedder = createOllamaEmbedder({ baseUrl: OLLAMA_URL, model: EMBEDDER_MODEL });
+
+  const recordsByCandidate = new Map<string, EvalRecord[]>();
+  for (const cand of candidates) recordsByCandidate.set(cand.candidateId, []);
+
+  // Re-load existing records into recordsByCandidate
+  if (fs.existsSync(OUT_JSONL)) {
+    for (const line of fs.readFileSync(OUT_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean)) {
+      try {
+        const r = JSON.parse(line) as EvalRecord;
+        const list = recordsByCandidate.get(r.candidateId);
+        if (list) list.push(r);
+      } catch { /* skip */ }
+    }
+  }
+
+  let nDone = existing.size;
+  outer: for (const cand of candidates) {
+    for (const inst of heldOutSample) {
+      const key = `${cand.candidateId}__${inst.instanceId}`;
+      if (existing.has(key)) { log(`[skip] ${key} already in JSONL`); continue; }
+      if (cumulativeCost >= COST_HALT_USD) { log(`[HALT] cumulative $${cumulativeCost.toFixed(4)} >= $${COST_HALT_USD}`); break outer; }
+      const r = await runOneEval(cand, inst, embedder);
+      if ('error' in r) { log(`[skip] ${key}: ${r.error}`); continue; }
+      out.write(JSON.stringify(r) + '\n');
+      cumulativeCost += r.evalCostUsd;
+      nDone++;
+      const list = recordsByCandidate.get(cand.candidateId);
+      if (list) list.push(r);
+      log(`[cumulative] $${cumulativeCost.toFixed(4)} / $${COST_HALT_USD} halt; ${nDone} evals total`);
+    }
+  }
+  out.end();
+
+  // Write summary
+  const inSampleStats = loadInSampleStats(args.candidateIds);
+  const summary = buildCheckpointCSummary(args, candidates, heldOutSample, recordsByCandidate, inSampleStats, cumulativeCost);
+  fs.writeFileSync(SUMMARY_JSON, JSON.stringify(summary, null, 2));
+  log(`[summary] wrote ${SUMMARY_JSON}`);
+  log(`[F.2] verdict_confirmation=${summary.f2_verdict_confirmation} (${summary.f2_verdict_detail})`);
+  log(`[F.5] overfitting_bound=±${F5_OVERFITTING_BOUND_PP}pp`);
+  log(`[Faza 2] deployment_authorization=${summary.faza_2_deployment_authorization}`);
+  for (const c of summary.perCandidate) {
+    log(`[F.5] ${c.candidateId}: in-sample=${c.inSample ? (c.inSample.passIIRate * 100).toFixed(1) : 'n/a'}% held-out=${(c.heldOut.passIIRate * 100).toFixed(1)}% gap=${c.passIIGapPP.toFixed(1)}pp verdict=${c.f5_condition_2_verdict}`);
+  }
+
+  log(`[done] ${nDone} evals; total cost $${cumulativeCost.toFixed(4)}`);
+}
+
+main().catch(e => { console.error('FATAL:', e); process.exit(2); });

benchmarks/gepa/scripts/faza-1/run-gen-1.ts

@@ -0,0 +1,912 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — Gen 1 evaluation runner.
+ *
+ * Per launch decision §G step 7+ + manifest v7 §gepa + Amendment 5.
+ *
+ * For each of 5 shapes, evaluate 3 candidates (baseline + 2 mutations) × 8
+ * instances = 120 total evaluations. Same instances as NULL-baseline (seed=42)
+ * for direct shape-vs-shape comparison.
+ *
+ * Halt at:
+ * - 30 evaluations (Checkpoint B per launch decision §E)
+ * - $26 cumulative (cost halt per Amendment 3 + super-linear)
+ * - 2 consecutive cell-semantic violations (per brief §5)
+ *
+ * Mode: MULTI-STEP (retrieval available) per Amendment 5 Ask 1 ratification.
+ */
+
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+import { fileURLToPath, pathToFileURL } from 'node:url';
+
+import {
+  MindDB,
+  FrameStore,
+  SessionStore,
+  HybridSearch,
+  createOllamaEmbedder,
+  type Embedder,
+} from '@waggle/core';
+import {
+  runRetrievalAgentLoop,
+  type LlmCallFn,
+  type LlmCallInput,
+  type LlmCallResult as AgentLlmCallResult,
+  type RetrievalSearchFn,
+  type AgentRunResult,
+  // Amendment 8 §canonical_mutation_api: REGISTRY + registerShape MUST be imported from
+  // '@waggle/agent' (same path the agent-loop uses internally). Importing via deep
+  // relative paths produces a separate module instance under tsx + Node ESM workspace
+  // resolution → mutations would not propagate. Diagnostic probe + Gen 1 partial
+  // b5avslp51 confirmed empirically.
+  REGISTRY,
+  registerShape,
+  type PromptShape,
+} from '@waggle/agent';
+
+import { type CorpusInstance } from '../../src/faza-1/corpus.js';
+import {
+  NULL_BASELINE_PER_SHAPE,
+  NULL_BASELINE_AGGREGATE,
+  type DeltaFloorVerdict,
+  type TieredFitnessComponents,
+} from '../../src/faza-1/types.js';
+import {
+  computeTieredFitness,
+  computeDeltaFloorVerdict,
+  computeTier2RetrievalBonus,
+} from '../../src/faza-1/fitness.js';
+import {
+  validateCandidate,
+  type ValidatorVerdict,
+} from '../../src/faza-1/mutation-validator.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const REPO_ROOT = path.resolve(__dirname, '../../../..');
+
+const CORPUS_JSONL = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/corpus/h3-northlane-cfo-50-instances.jsonl');
+const PROMPT_SHAPES_DIR = path.join(REPO_ROOT, 'packages/agent/src/prompt-shapes');
+const GEPA_EVOLVED_DIR = path.join(PROMPT_SHAPES_DIR, 'gepa-evolved');
+const OUT_DIR = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/gen-1');
+const OUT_JSONL = path.join(OUT_DIR, 'gen-1-eval.jsonl');
+const RUN_LOG = path.join(OUT_DIR, 'gen-1-run.log');
+const SUMMARY_JSON = path.join(OUT_DIR, 'gen-1-summary.json');
+const SCRATCH_DIR = path.join(REPO_ROOT, 'tmp/gepa-faza1-gen-1');
+
+const LITELLM_URL = process.env.LITELLM_URL ?? 'http://localhost:4000';
+const OLLAMA_URL = 'http://localhost:11434';
+const EMBEDDER_MODEL = 'nomic-embed-text';
+
+const SAMPLING_SEED = 42;
+const N_PER_SHAPE = 8;
+const N_CANDIDATES_PER_SHAPE = 3;  // 1 baseline + 2 mutations
+
+const SHAPES = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'] as const;
+type ShapeName = typeof SHAPES[number];
+
+const SUBJECT_ALIAS = 'qwen3.6-35b-a3b-via-dashscope-direct';
+const SUBJECT_MAX_TOKENS = 16000;
+const SUBJECT_THINKING = true;
+
+const JUDGES = ['claude-opus-4-7', 'gpt-5.4', 'minimax-m27-via-openrouter'] as const;
+const JUDGE_MAX_TOKENS = 3000;
+const JUDGE_RETRIES = 3;
+
+const MAX_STEPS = 5;
+const MAX_RETRIEVALS_PER_STEP = 8;
+const PER_CALL_HALT_USD = 0.40;
+const PER_CELL_HALT_USD = 1.00;
+
+// Halt at 30 evals (Checkpoint B) unless --full
+const CHECKPOINT_B_HALT_EVALS = 30;
+const COST_HALT_USD = 26.0;  // 30% over $20 NULL projection (Amendment 3 envelope basis)
+
+const MODEL_PRICING: Record<string, { in: number; out: number }> = {
+  'claude-opus-4-7':                { in: 15.0, out: 75.0 },
+  'gpt-5.4':                        { in: 2.5,  out: 10.0 },
+  'minimax-m27-via-openrouter':     { in: 0.7,  out: 2.8 },
+  'qwen3.6-35b-a3b-via-dashscope-direct': { in: 0.20, out: 0.80 },
+  'qwen3.6-35b-a3b-via-openrouter':       { in: 0.6,  out: 2.4 },
+};
+
+const MANIFEST_ANCHOR = 'manifest-v7-gepa-faza1';
+const MANIFEST_SHA_AMENDMENT_5 = '062dfc4935aaa89f0b25595c5dc3ce4af06c95c4c261075a1f0226d8af3f3dee';
+const MANIFEST_SHA_AMENDMENT_6 = '0b55d8e353299594254e1a4a76f26f53014d726315dc6a0e5d6dc1a3a44a368a';
+const MANIFEST_SHA_AMENDMENT_7 = 'bc0bcf9bd8b0c8344b25e5f8ab15b0475039ba28a1f782ebffe4cc1c4ff7d1de';
+const MANIFEST_SHA_AMENDMENT_8 = '85858f12f1270da28277dd4d98e454d1dae8ef970537cb8c561f484599c4e2e9';
+const MANIFEST_SHA_AMENDMENT_9 = '5e3ad831c61beb19ccb4ff42b455b4c3964d830808944d4915189c5e9b1709b8';
+const MANIFEST_SHA_AMENDMENT_10 = '7fb2fb930670b5a28e417a76c64ca1a556f05afb9cf0761aba9f83f0c5de1c9b';
+
+// ── Amendment 7 — mid-run halt thresholds (binding) ───────────────────────
+// Per manifest v7 Amendment 7 §checkpoint_b_tightened.mid_run_halt_thresholds.
+
+/** Per-eval cost projection from Checkpoint A v2 §E (USD). */
+const PER_EVAL_COST_PROJECTION_USD = 0.1243;
+/** Mid-run halt: per-candidate cost overshoot threshold (>25% over projection = >$0.156/eval). */
+const PER_CANDIDATE_COST_OVERSHOOT_THRESHOLD_USD = PER_EVAL_COST_PROJECTION_USD * 1.25;  // 0.155375
+/** Mid-run halt: count of candidates with overshoot that triggers halt (>3). */
+const MID_RUN_HALT_OVERSHOOT_CANDIDATE_COUNT = 3;
+/** Mid-run halt: per-shape variance widens (max-min trio_strict_pass_rate_II range across candidates) >40pp. */
+const PER_SHAPE_VARIANCE_HALT_PP = 40;
+/** Minimum evals before per-shape variance check runs (avoid noise on N<3). */
+const PER_SHAPE_VARIANCE_MIN_EVALS = 3;
+/**
+ * Minimum Qwen evals before retrieval regression check runs.
+ *
+ * Amendment 10 §10.1 calibration_fix: raised from 3 → 5 based on empirical
+ * evidence from 2 prior halt firings (b5avslp51 sunk + b1t474yqd full Gen 1)
+ * where halt fired on baseline-only data within ±0.10 absolute noise band.
+ * Each candidate must have 5+ evals to enter the per-shape aggregate check;
+ * reduces N=3 binomial-tail noise sensitivity.
+ */
+const QWEN_RETRIEVAL_REGRESSION_MIN_EVALS = 5;  // Amendment 10 §10.1 (was 3 per Amendment 7)
+
+function log(msg: string): void {
+  const line = `[${new Date().toISOString()}] ${msg}\n`;
+  try { fs.appendFileSync(RUN_LOG, line); } catch { /* dir may not exist */ }
+  process.stderr.write(line);
+}
+
+interface Args {
+  mode: 'dry-run' | 'checkpoint-b' | 'full';
+}
+function parseArgs(argv: string[]): Args {
+  let mode: Args['mode'] = 'dry-run';
+  for (const f of argv) {
+    if (f === '--dry-run') mode = 'dry-run';
+    else if (f === '--checkpoint-b') mode = 'checkpoint-b';
+    else if (f === '--full') mode = 'full';
+  }
+  return { mode };
+}
+
+// ── Mulberry32 sampling (same as NULL-baseline) ───────────────────────────
+
+function mulberry32(seed: number): () => number {
+  let t = seed >>> 0;
+  return () => {
+    t = (t + 0x6d2b79f5) >>> 0;
+    let r = t;
+    r = Math.imul(r ^ (r >>> 15), r | 1);
+    r ^= r + Math.imul(r ^ (r >>> 7), r | 61);
+    return ((r ^ (r >>> 14)) >>> 0) / 4294967296;
+  };
+}
+function deterministicShuffle<T>(items: ReadonlyArray<T>, seed: number): T[] {
+  const arr = [...items];
+  const rand = mulberry32(seed);
+  for (let i = arr.length - 1; i > 0; i--) {
+    const j = Math.floor(rand() * (i + 1));
+    [arr[i], arr[j]] = [arr[j], arr[i]];
+  }
+  return arr;
+}
+function loadCorpus(): CorpusInstance[] {
+  return fs.readFileSync(CORPUS_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean).map(l => JSON.parse(l));
+}
+
+// ── Candidate loader (baseline from REGISTRY + 2 mutations dynamic import) ─
+
+interface Candidate {
+  candidateId: string;       // e.g., qwen-thinking::baseline | qwen-thinking::gen1-v1
+  shape: ShapeName;
+  variant: 'baseline' | 'gen1-v1' | 'gen1-v2';
+  promptShape: PromptShape;
+}
+
+async function loadCandidates(): Promise<Map<ShapeName, Candidate[]>> {
+  const out = new Map<ShapeName, Candidate[]>();
+  for (const shape of SHAPES) {
+    const cands: Candidate[] = [];
+    const baseline = REGISTRY[shape];
+    if (!baseline) throw new Error(`shape "${shape}" not in REGISTRY`);
+    cands.push({ candidateId: `${shape}::baseline`, shape, variant: 'baseline', promptShape: baseline });
+
+    for (let v = 1; v <= 2; v++) {
+      const filename = `${shape}-gen1-v${v}.ts`;
+      const filepath = path.join(GEPA_EVOLVED_DIR, filename);
+      if (!fs.existsSync(filepath)) {
+        throw new Error(`mutation file missing: ${filepath}`);
+      }
+      // Windows ESM requires file:// URL for absolute paths
+      const mod: any = await import(pathToFileURL(filepath).href);
+      // Find the exported PromptShape (single export per file convention)
+      const promptShape = Object.values(mod).find(
+        (v: any) => v && typeof v === 'object' && 'name' in v && 'systemPrompt' in v && 'soloUserPrompt' in v,
+      ) as PromptShape | undefined;
+      if (!promptShape) throw new Error(`no PromptShape export found in ${filepath}`);
+      cands.push({
+        candidateId: `${shape}::gen1-v${v}`, shape,
+        variant: `gen1-v${v}` as 'gen1-v1' | 'gen1-v2', promptShape,
+      });
+    }
+    out.set(shape, cands);
+  }
+  return out;
+}
+
+// ── LLM call adapter (same as NULL-baseline) ──────────────────────────────
+
+const llmCall: LlmCallFn = async (input: LlmCallInput): Promise<AgentLlmCallResult> => {
+  const masterKey = process.env.LITELLM_MASTER_KEY;
+  if (!masterKey) throw new Error('LITELLM_MASTER_KEY env not set');
+  const { model, messages } = input;
+  const isQwen = model.includes('qwen');
+  const maxTokens = input.maxTokens ?? (isQwen ? SUBJECT_MAX_TOKENS : 4096);
+  const thinking = input.thinking ?? (isQwen ? SUBJECT_THINKING : true);
+  const payload: Record<string, unknown> = { model, messages, max_tokens: maxTokens };
+  if (model.startsWith('claude-opus')) payload.temperature = 1.0;
+  else if (model === 'gpt-5.4' || model === 'minimax-m27-via-openrouter') {/* omit */}
+  else payload.temperature = input.temperature ?? 0.3;
+  if (isQwen) payload.extra_body = { enable_thinking: thinking };
+
+  const started = Date.now();
+  let lastErr: string | undefined;
+  for (let attempt = 0; attempt < 2; attempt++) {
+    try {
+      const resp = await fetch(`${LITELLM_URL}/chat/completions`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${masterKey}` },
+        body: JSON.stringify(payload),
+      });
+      const d: any = await resp.json();
+      if ('error' in d) {
+        lastErr = String(d.error?.message ?? JSON.stringify(d.error)).slice(0, 200);
+        if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+        continue;
+      }
+      const content = d.choices?.[0]?.message?.content ?? '';
+      const usage = d.usage ?? {};
+      const inTok = usage.prompt_tokens ?? 0;
+      const outTok = usage.completion_tokens ?? 0;
+      const pricing = MODEL_PRICING[model] ?? { in: 1, out: 4 };
+      return { content, inTokens: inTok, outTokens: outTok, costUsd: (inTok*pricing.in + outTok*pricing.out)/1_000_000, latencyMs: Date.now()-started };
+    } catch (e) {
+      lastErr = `${(e as Error).name}: ${(e as Error).message}`.slice(0, 200);
+      if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+    }
+  }
+  return { content: '', inTokens: 0, outTokens: 0, costUsd: 0, latencyMs: Date.now()-started, error: lastErr };
+};
+
+// ── Trio judging (mirror NULL-baseline) ───────────────────────────────────
+
+const JUDGE_PROMPT_TEMPLATE = `You are evaluating an AI agent's response to a complex knowledge work task. The persona, scenario, materials, and question are provided. The response was generated under one of four configurations (revealed only after scoring): {model_only | model + memory + agent harness} × {Opus 4.7 | Qwen 3.6 35B-A3B}.
+
+You do NOT know which configuration produced this response. Score blind.
+
+Read the persona/scenario/question (provided), skim the materials (provided), then read the response carefully (provided).
+
+Score the response on six dimensions, Likert 1-5:
+
+1. COMPLETENESS — engagement with all material
+2. ACCURACY — faithfulness to source materials, no hallucinations
+3. SYNTHESIS — connections across inputs, not isolated treatment
+4. JUDGMENT — defensible recommendations, tradeoffs acknowledged
+5. ACTIONABILITY — would the persona act on this tomorrow
+6. STRUCTURE — organization and readability
+
+Output JSON only:
+{"completeness":<1-5>,"accuracy":<1-5>,"synthesis":<1-5>,"judgment":<1-5>,"actionability":<1-5>,"structure":<1-5>,"rationale":"<1-2 sentences explaining the lowest scoring dimension>","overall_verdict":"<one of: PASS_STRONG | PASS_ADEQUATE | FAIL_WEAK | FAIL_CRITICAL>"}
+
+PASS_STRONG: mean >= 4.0
+PASS_ADEQUATE: mean 3.5-3.99
+FAIL_WEAK: mean 2.5-3.49
+FAIL_CRITICAL: mean < 2.5
+
+[PERSONA + SCENARIO + QUESTION]
+###PSQ###
+
+[MATERIALS]
+###MAT###
+
+[RESPONSE TO EVALUATE]
+###RES###`;
+
+interface JudgeRecord { judge_model: string; mean: number; cost: number; latency_ms: number; raw: any; retries: number }
+interface TrioResult { records: JudgeRecord[]; trioMean: number; trioStrictPassII: boolean; trioStrictPassI: boolean; cost: number }
+
+function parseJudgeJson(text: string): { mean: number; raw: any } | null {
+  const m = text.match(/\{[\s\S]*\}/);
+  if (!m) return null;
+  try {
+    const obj = JSON.parse(m[0]);
+    const dims = ['completeness','accuracy','synthesis','judgment','actionability','structure'];
+    for (const d of dims) if (typeof obj[d] !== 'number' || obj[d] < 1 || obj[d] > 5) return null;
+    const mean = dims.reduce((s, d) => s + obj[d], 0) / dims.length;
+    return { mean, raw: obj };
+  } catch { return null; }
+}
+
+async function runJudge(model: string, prompt: string): Promise<JudgeRecord> {
+  let totalCost = 0, totalLat = 0;
+  for (let attempt = 0; attempt < JUDGE_RETRIES; attempt++) {
+    const r = await llmCall({ model, messages: [{ role: 'user', content: prompt }], maxTokens: JUDGE_MAX_TOKENS, thinking: false });
+    totalCost += r.costUsd; totalLat += r.latencyMs;
+    if (r.error) continue;
+    const parsed = parseJudgeJson(r.content);
+    if (parsed) return { judge_model: model, mean: parsed.mean, cost: totalCost, latency_ms: totalLat, raw: parsed.raw, retries: attempt };
+  }
+  return { judge_model: model, mean: 0, cost: totalCost, latency_ms: totalLat, raw: null, retries: JUDGE_RETRIES };
+}
+
+async function judgeTrio(instance: CorpusInstance, response: string): Promise<TrioResult> {
+  const prompt = JUDGE_PROMPT_TEMPLATE
+    .replace('###PSQ###', `${instance.personaText}\n\nQUESTION: ${instance.question}`)
+    .replace('###MAT###', instance.materialsConcat)
+    .replace('###RES###', response);
+  const records = await Promise.all(JUDGES.map(j => runJudge(j, prompt)));
+  const valid = records.filter(r => r.mean > 0).map(r => r.mean);
+  const trioMean = valid.length > 0 ? valid.reduce((a,b)=>a+b,0)/valid.length : 0;
+  return {
+    records,
+    trioMean,
+    trioStrictPassII: trioMean >= 4.0,
+    trioStrictPassI: records.filter(r => r.mean >= 3.5).length >= 2,
+    cost: records.reduce((s,r) => s + r.cost, 0),
+  };
+}
+
+// ── Per-eval orchestration ────────────────────────────────────────────────
+
+interface EvalRecord {
+  shape: ShapeName;
+  candidateId: string;
+  variant: Candidate['variant'];
+  instanceId: string;
+  instanceCell: CorpusInstance['cell'];
+  candidateResponse: string;
+  candidateLatencyMs: number;
+  candidateCostUsd: number;
+  loopExhausted: boolean;
+  stepsTaken: number;
+  retrievalCalls: number;
+  trioMean: number;
+  trioStrictPassII: boolean;
+  trioStrictPassI: boolean;
+  judgeRecords: JudgeRecord[];
+  evalCostUsd: number;
+  manifestAnchor: string;
+  manifestShaAmendment5: string;
+  tsIso: string;
+}
+
+async function runOneEval(cand: Candidate, instance: CorpusInstance, embedder: Embedder): Promise<EvalRecord | { error: string }> {
+  const evalId = `${cand.candidateId}__${instance.instanceId}`;
+  log(`[${evalId}] start`);
+  const dbPath = path.join(SCRATCH_DIR, `eval-${cand.candidateId.replace(/[:]/g, '_')}-${instance.instanceId}.sqlite`);
+  if (fs.existsSync(dbPath)) fs.unlinkSync(dbPath);
+  const db = new MindDB(dbPath);
+  const frames = new FrameStore(db);
+  const sessions = new SessionStore(db);
+  const hybrid = new HybridSearch(db, embedder);
+  const gopId = `gen1-${cand.candidateId.replace(/[:]/g,'_')}-${instance.instanceId}`;
+  sessions.ensure(gopId, undefined, `Gen 1 ${cand.candidateId} on ${instance.instanceId}`);
+  for (const doc of instance.sourceDocuments) frames.createIFrame(gopId, `## ${doc.title}\n\n${doc.body}`, 'important', 'system');
+
+  const search: RetrievalSearchFn = async ({ query, limit }) => {
+    const hits = await hybrid.search(query, { limit, gopId });
+    return {
+      formattedResults: hits.length > 0 ? hits.map((s,i)=>`[result ${i+1}, score ${s.finalScore.toFixed(3)}]\n${s.frame.content}`).join('\n\n---\n\n') : '',
+      resultCount: hits.length,
+    };
+  };
+
+  // Inject the candidate's prompt shape via custom orchestration: we use runRetrievalAgentLoop
+  // with the candidate's modelAlias + prompt-shape. The agent loop internally selects shape via
+  // selector; we override by passing the candidate's shape directly. Since runRetrievalAgentLoop
+  // uses selectShape internally, we override REGISTRY at runtime by name match.
+  // For Faza 1 simplicity, we register candidate as override under its unique name:
+  let agentResult: AgentRunResult;
+  try {
+    // Amendment 8 §canonical_mutation_api: register the candidate via the sanctioned
+    // mutation path. registerShape() is imported from '@waggle/agent' so it mutates
+    // the SAME REGISTRY instance the agent-loop's selectShape() reads from. Direct
+    // (REGISTRY as any)[name] = shape is forbidden post-Amendment-8 (would mutate a
+    // separate module instance under tsx + Node ESM workspace resolution).
+    registerShape(cand.promptShape.name, cand.promptShape);
+    agentResult = await runRetrievalAgentLoop({
+      modelAlias: SUBJECT_ALIAS,
+      persona: instance.personaText,
+      question: instance.question,
+      llmCall,
+      search,
+      maxSteps: MAX_STEPS,
+      maxRetrievalsPerStep: MAX_RETRIEVALS_PER_STEP,
+      perCallHaltUsd: PER_CALL_HALT_USD,
+      perCellHaltUsd: PER_CELL_HALT_USD,
+      contextTag: evalId,
+      promptShapeOverride: cand.promptShape.name,  // if supported
+    } as any);
+  } catch (e) {
+    return { error: `agent loop failed: ${(e as Error).message}` };
+  }
+
+  log(`[${evalId}] subject_done; retrievals=${agentResult.retrievalCalls} steps=${agentResult.stepsTaken} cost=$${agentResult.totalCostUsd.toFixed(4)}`);
+  const judges = await judgeTrio(instance, agentResult.rawResponse);
+  const evalCostUsd = agentResult.totalCostUsd + judges.cost;
+  log(`[${evalId}] judged; trio_mean=${judges.trioMean.toFixed(3)} pass_ii=${judges.trioStrictPassII} retrievals=${agentResult.retrievalCalls} eval_cost=$${evalCostUsd.toFixed(4)}`);
+
+  return {
+    shape: cand.shape, candidateId: cand.candidateId, variant: cand.variant,
+    instanceId: instance.instanceId, instanceCell: instance.cell,
+    candidateResponse: agentResult.rawResponse,
+    candidateLatencyMs: agentResult.totalLatencyMs,
+    candidateCostUsd: agentResult.totalCostUsd,
+    loopExhausted: agentResult.loopExhausted,
+    stepsTaken: agentResult.stepsTaken,
+    retrievalCalls: agentResult.retrievalCalls,
+    trioMean: judges.trioMean,
+    trioStrictPassII: judges.trioStrictPassII,
+    trioStrictPassI: judges.trioStrictPassI,
+    judgeRecords: judges.records,
+    evalCostUsd,
+    manifestAnchor: MANIFEST_ANCHOR,
+    manifestShaAmendment5: MANIFEST_SHA_AMENDMENT_5,
+    tsIso: new Date().toISOString(),
+  };
+}
+
+// ── Main ──────────────────────────────────────────────────────────────────
+
+// ── Amendment 7 — per-candidate accumulator ───────────────────────────────
+
+interface CandidateAcc {
+  candidateId: string;
+  shape: ShapeName;
+  variant: 'baseline' | 'gen1-v1' | 'gen1-v2';
+  evalCount: number;
+  passIICount: number;          // count of trioStrictPassII = true
+  totalCostUsd: number;
+  totalRetrievalCalls: number;
+  trioMeans: number[];          // per-eval trioMean for variance + audit
+  retrievalCalls: number[];     // per-eval retrieval calls for audit
+  mutationValidatorPassed: boolean;  // computed from validateCandidate at startup
+}
+
+function makeCandidateAcc(cand: Candidate, validatorPassed: boolean): CandidateAcc {
+  return {
+    candidateId: cand.candidateId,
+    shape: cand.shape,
+    variant: cand.variant,
+    evalCount: 0,
+    passIICount: 0,
+    totalCostUsd: 0,
+    totalRetrievalCalls: 0,
+    trioMeans: [],
+    retrievalCalls: [],
+    mutationValidatorPassed: validatorPassed,
+  };
+}
+
+function ingestEvalIntoAcc(acc: CandidateAcc, r: EvalRecord): void {
+  acc.evalCount++;
+  if (r.trioStrictPassII) acc.passIICount++;
+  acc.totalCostUsd += r.evalCostUsd;
+  acc.totalRetrievalCalls += r.retrievalCalls;
+  acc.trioMeans.push(r.trioMean);
+  acc.retrievalCalls.push(r.retrievalCalls);
+}
+
+function accMeanCostPerEval(acc: CandidateAcc): number {
+  return acc.evalCount > 0 ? acc.totalCostUsd / acc.evalCount : 0;
+}
+function accPassRateII(acc: CandidateAcc): number {
+  return acc.evalCount > 0 ? acc.passIICount / acc.evalCount : 0;
+}
+function accMeanRetrievalCallsPerTask(acc: CandidateAcc): number {
+  return acc.evalCount > 0 ? acc.totalRetrievalCalls / acc.evalCount : 0;
+}
+
+// ── Amendment 7 — mid-run halt check (binding) ────────────────────────────
+
+interface MidRunHaltCheckResult {
+  shouldHalt: boolean;
+  reason: string | null;
+}
+
+function checkMidRunHalts(accs: Map<string, CandidateAcc>): MidRunHaltCheckResult {
+  // Threshold A — per-candidate cost overshoot >25% on >3 candidates
+  let overshootCount = 0;
+  const overshootCandidates: string[] = [];
+  for (const acc of accs.values()) {
+    if (acc.evalCount === 0) continue;
+    if (accMeanCostPerEval(acc) > PER_CANDIDATE_COST_OVERSHOOT_THRESHOLD_USD) {
+      overshootCount++;
+      overshootCandidates.push(`${acc.candidateId}=$${accMeanCostPerEval(acc).toFixed(4)}/eval`);
+    }
+  }
+  if (overshootCount > MID_RUN_HALT_OVERSHOOT_CANDIDATE_COUNT) {
+    return {
+      shouldHalt: true,
+      reason: `Amendment 7 §checkpoint_b_tightened.per_candidate_cost_overshoot: ${overshootCount} candidates >$${PER_CANDIDATE_COST_OVERSHOOT_THRESHOLD_USD.toFixed(4)}/eval (threshold >${MID_RUN_HALT_OVERSHOOT_CANDIDATE_COUNT}); offenders=[${overshootCandidates.join(', ')}]`,
+    };
+  }
+
+  // Threshold B — per-shape variance widens >40pp range (max-min trio_strict_pass_rate_II) on any shape
+  for (const shape of SHAPES) {
+    const shapeAccs = [...accs.values()].filter(a => a.shape === shape && a.evalCount >= PER_SHAPE_VARIANCE_MIN_EVALS);
+    if (shapeAccs.length < 2) continue;
+    const passRates = shapeAccs.map(accPassRateII);
+    const max = Math.max(...passRates);
+    const min = Math.min(...passRates);
+    const rangePP = (max - min) * 100;
+    if (rangePP > PER_SHAPE_VARIANCE_HALT_PP) {
+      return {
+        shouldHalt: true,
+        reason: `Amendment 7 §checkpoint_b_tightened.per_shape_variance_widens: shape=${shape} range=${rangePP.toFixed(1)}pp > ${PER_SHAPE_VARIANCE_HALT_PP}pp; rates=${passRates.map(r => r.toFixed(2)).join(',')}`,
+      };
+    }
+  }
+
+  // Threshold C — Qwen-targeted retrieval engagement drops below per-shape NULL baseline
+  //
+  // Amendment 10 §10.1 mutation_execution_gate (binding): halt only fires when at
+  // least one mutation candidate has been evaluated for this shape. Baseline-only
+  // data does NOT trigger halt. This matches Amendment 9 §qwen_evolution_verdict_capture
+  // .mid_run_halt_binding intent that the halt represents direction_2 verdict
+  // (mutations regress retrieval), not baseline-running stochastic variance.
+  // Empirical basis: 2 prior halt firings (b5avslp51 + b1t474yqd) on baseline-only
+  // data within ±0.10 absolute noise band were both per-Amendment-9 NOT direction_2
+  // verdicts.
+  for (const shape of ['qwen-thinking', 'qwen-non-thinking'] as const) {
+    const allShapeAccs = [...accs.values()].filter(a => a.shape === shape);
+
+    // Amendment 11 §11.1 second_order_calibration_patch (binding):
+    // mutation_execution_gate threshold tightened from ≥1 eval to ≥MIN_EVALS evals.
+    // Halt only fires when at least one mutation candidate (variant !== 'baseline')
+    // has STATISTICALLY MEANINGFUL sample size (≥QWEN_RETRIEVAL_REGRESSION_MIN_EVALS=5
+    // evals). This guarantees the mutation IS in the per-shape aggregate (not
+    // excluded by the individual-candidate MIN_EVALS filter), eliminating the
+    // second-order false-positive class where halt fired on baseline-only
+    // aggregate while gate was mechanically met by a single mutation eval.
+    //
+    // Per Amendment 11 §11.2 terminal_calibration_clause (BINDING): if halt
+    // fires AGAIN with this calibration ACTIVE, that IS Phase 4.5 direction_2
+    // verdict. No further calibration patches; escalate to Option C (Amendment 12
+    // interface refactor).
+    const hasStatisticallyMeaningfulMutationForShape = allShapeAccs.some(
+      a => a.variant !== 'baseline' && a.evalCount >= QWEN_RETRIEVAL_REGRESSION_MIN_EVALS,
+    );
+    if (!hasStatisticallyMeaningfulMutationForShape) continue;
+
+    const baseline = NULL_BASELINE_PER_SHAPE[shape].meanRetrievalCallsPerTask;
+    const shapeAccs = allShapeAccs.filter(a => a.evalCount >= QWEN_RETRIEVAL_REGRESSION_MIN_EVALS);
+    if (shapeAccs.length === 0) continue;
+    const totalRetr = shapeAccs.reduce((s, a) => s + a.totalRetrievalCalls, 0);
+    const totalEvals = shapeAccs.reduce((s, a) => s + a.evalCount, 0);
+    if (totalEvals === 0) continue;
+    const aggMean = totalRetr / totalEvals;
+    if (aggMean < baseline) {
+      return {
+        shouldHalt: true,
+        reason: `Amendment 7 §checkpoint_b_tightened.qwen_retrieval_engagement_regression (post Amendment 11 §11.1 second_order_calibration_patch): shape=${shape} mean=${aggMean.toFixed(3)} < NULL baseline ${baseline.toFixed(3)} (n=${totalEvals}; ≥1 mutation candidate with ≥${QWEN_RETRIEVAL_REGRESSION_MIN_EVALS} evals evaluated for shape; per Amendment 11 §11.2 terminal_calibration_clause, this IS Phase 4.5 direction_2 verdict)`,
+      };
+    }
+  }
+
+  return { shouldHalt: false, reason: null };
+}
+
+// ── Amendment 7 — Checkpoint B summary writer (binding extensions) ────────
+
+interface PerCandidateTierBreakdown {
+  candidateId: string;
+  shape: ShapeName;
+  variant: string;
+  evalCount: number;
+  trioStrictPassRateII: number;
+  meanRetrievalCallsPerTask: number;
+  meanEvalCostUsd: number;
+  costOvershoot: boolean;
+  tieredFitness: TieredFitnessComponents;
+}
+
+interface CheckpointBSummary {
+  manifestAnchor: string;
+  manifestShaAmendment7: string;
+  generated_at: string;
+  mode: string;
+  totalEvals: number;
+  totalCostUsd: number;
+  haltReason: string | null;
+  perCandidateTierBreakdown: PerCandidateTierBreakdown[];
+  retrievalEngagementDeltasPerQwenShape: {
+    'qwen-thinking': { nullBaselineMean: number; gen1PartialMean: number | null; deltaAbsolute: number | null };
+    'qwen-non-thinking': { nullBaselineMean: number; gen1PartialMean: number | null; deltaAbsolute: number | null };
+  };
+  cellSemanticAnchorInvarianceCountPerCandidate: Record<string, number>;
+  preRegisteredDeltaFloorVerdict: DeltaFloorVerdict;
+  midRunHaltsBindingThresholds: {
+    perCandidateCostOvershoot: { threshold: number; candidatesOvershoot: number };
+    perShapeVariance: { thresholdPP: number; maxRangeObservedPP: number };
+    qwenRetrievalRegression: { triggered: boolean; details: string };
+  };
+}
+
+function buildCheckpointBSummary(
+  args: ReturnType<typeof parseArgs>,
+  accs: Map<string, CandidateAcc>,
+  totalEvals: number,
+  totalCostUsd: number,
+  haltReason: string | null,
+): CheckpointBSummary {
+  const perCandidate: PerCandidateTierBreakdown[] = [];
+  for (const acc of accs.values()) {
+    if (acc.evalCount === 0) continue;
+    const passRate = accPassRateII(acc);
+    const meanRetr = accMeanRetrievalCallsPerTask(acc);
+    const meanCost = accMeanCostPerEval(acc);
+    const candidateMetrics = {
+      candidateId: acc.candidateId,
+      shape: acc.shape,
+      evaluations: [],
+      trioStrictPassRateII: passRate,
+      trioStrictPassRateI: 0,            // not tracked here; reported in JSONL
+      meanRetrievalCallsPerTask: meanRetr,
+      meanCostUsd: meanCost,
+    };
+    const tieredFitness = computeTieredFitness({
+      candidate: candidateMetrics,
+      nullBaselinePassRateII: NULL_BASELINE_PER_SHAPE[acc.shape].trioStrictPassRateII,
+      nullBaselineMeanRetrievalCallsPerTask: NULL_BASELINE_PER_SHAPE[acc.shape].meanRetrievalCallsPerTask,
+      mutationValidatorPassed: acc.mutationValidatorPassed,
+      saturatedRegime: true,  // 5/5 shapes ≥75% per Checkpoint A v2 §B.2
+    });
+    perCandidate.push({
+      candidateId: acc.candidateId,
+      shape: acc.shape,
+      variant: acc.variant,
+      evalCount: acc.evalCount,
+      trioStrictPassRateII: passRate,
+      meanRetrievalCallsPerTask: meanRetr,
+      meanEvalCostUsd: meanCost,
+      costOvershoot: meanCost > PER_CANDIDATE_COST_OVERSHOOT_THRESHOLD_USD,
+      tieredFitness,
+    });
+  }
+
+  // Aggregate Tier 1: mean trio_strict_pass_rate_II across all evals
+  const totalEvalsAcc = perCandidate.reduce((s, c) => s + c.evalCount, 0);
+  const aggregateTrioStrictPassRateII =
+    totalEvalsAcc > 0
+      ? perCandidate.reduce((s, c) => s + c.trioStrictPassRateII * c.evalCount, 0) / totalEvalsAcc
+      : 0;
+
+  // Per-shape Qwen retrieval means (across that shape's candidates)
+  function qwenShapeAggregate(shape: 'qwen-thinking' | 'qwen-non-thinking'):
+    { gen1PartialMean: number | null; deltaAbsolute: number | null } {
+    const shapeAccs = [...accs.values()].filter(a => a.shape === shape && a.evalCount > 0);
+    if (shapeAccs.length === 0) return { gen1PartialMean: null, deltaAbsolute: null };
+    const totalRetr = shapeAccs.reduce((s, a) => s + a.totalRetrievalCalls, 0);
+    const totalEvalsLocal = shapeAccs.reduce((s, a) => s + a.evalCount, 0);
+    const mean = totalEvalsLocal > 0 ? totalRetr / totalEvalsLocal : null;
+    const baseline = NULL_BASELINE_PER_SHAPE[shape].meanRetrievalCallsPerTask;
+    return { gen1PartialMean: mean, deltaAbsolute: mean === null ? null : mean - baseline };
+  }
+  const qwenThinkingAgg = qwenShapeAggregate('qwen-thinking');
+  const qwenNonThinkingAgg = qwenShapeAggregate('qwen-non-thinking');
+
+  const qwenShapeRetrievalMeans: Partial<Record<ShapeName, number>> = {};
+  if (qwenThinkingAgg.gen1PartialMean !== null) qwenShapeRetrievalMeans['qwen-thinking'] = qwenThinkingAgg.gen1PartialMean;
+  if (qwenNonThinkingAgg.gen1PartialMean !== null) qwenShapeRetrievalMeans['qwen-non-thinking'] = qwenNonThinkingAgg.gen1PartialMean;
+
+  // Aggregate Tier 2 bonus across Qwen-targeted candidates (mean across qwen candidates with data)
+  const qwenCandidates = perCandidate.filter(c => c.shape === 'qwen-thinking' || c.shape === 'qwen-non-thinking');
+  const qwenAggregateTier2Bonus =
+    qwenCandidates.length > 0
+      ? qwenCandidates.reduce((s, c) => s + c.tieredFitness.tier2RetrievalBonus, 0) / qwenCandidates.length
+      : 0;
+
+  const deltaFloorVerdict = computeDeltaFloorVerdict({
+    aggregateTrioStrictPassRateII,
+    aggregateNullBaselinePassRateII: NULL_BASELINE_AGGREGATE.trioStrictPassRateII,
+    qwenShapeRetrievalMeans,
+    qwenShapeNullBaselineRetrievalMeans: {
+      'qwen-thinking': NULL_BASELINE_PER_SHAPE['qwen-thinking'].meanRetrievalCallsPerTask,
+      'qwen-non-thinking': NULL_BASELINE_PER_SHAPE['qwen-non-thinking'].meanRetrievalCallsPerTask,
+    },
+    qwenAggregateTier2Bonus,
+  });
+
+  // Per-shape variance maxRange snapshot
+  let maxRangeObservedPP = 0;
+  for (const shape of SHAPES) {
+    const shapeAccs = perCandidate.filter(c => c.shape === shape);
+    if (shapeAccs.length < 2) continue;
+    const rates = shapeAccs.map(c => c.trioStrictPassRateII);
+    const range = (Math.max(...rates) - Math.min(...rates)) * 100;
+    if (range > maxRangeObservedPP) maxRangeObservedPP = range;
+  }
+  const overshootCount = perCandidate.filter(c => c.costOvershoot).length;
+
+  // Qwen retrieval regression check (binary informational; halt logic in checkMidRunHalts)
+  let qwenRegressionDetails = 'no_regression';
+  let qwenRegressionTriggered = false;
+  for (const shape of ['qwen-thinking', 'qwen-non-thinking'] as const) {
+    const agg = shape === 'qwen-thinking' ? qwenThinkingAgg : qwenNonThinkingAgg;
+    if (agg.gen1PartialMean !== null && agg.gen1PartialMean < NULL_BASELINE_PER_SHAPE[shape].meanRetrievalCallsPerTask) {
+      qwenRegressionTriggered = true;
+      qwenRegressionDetails = `${shape} mean=${agg.gen1PartialMean.toFixed(3)} < NULL ${NULL_BASELINE_PER_SHAPE[shape].meanRetrievalCallsPerTask}`;
+      break;
+    }
+  }
+
+  const cellSemanticAnchorInvarianceCountPerCandidate: Record<string, number> = {};
+  for (const c of perCandidate) {
+    cellSemanticAnchorInvarianceCountPerCandidate[c.candidateId] = c.tieredFitness.cellSemanticAnchorInvarianceCount;
+  }
+
+  return {
+    manifestAnchor: MANIFEST_ANCHOR,
+    manifestShaAmendment7: MANIFEST_SHA_AMENDMENT_7,
+    generated_at: new Date().toISOString(),
+    mode: args.mode,
+    totalEvals,
+    totalCostUsd,
+    haltReason,
+    perCandidateTierBreakdown: perCandidate,
+    retrievalEngagementDeltasPerQwenShape: {
+      'qwen-thinking': {
+        nullBaselineMean: NULL_BASELINE_PER_SHAPE['qwen-thinking'].meanRetrievalCallsPerTask,
+        gen1PartialMean: qwenThinkingAgg.gen1PartialMean,
+        deltaAbsolute: qwenThinkingAgg.deltaAbsolute,
+      },
+      'qwen-non-thinking': {
+        nullBaselineMean: NULL_BASELINE_PER_SHAPE['qwen-non-thinking'].meanRetrievalCallsPerTask,
+        gen1PartialMean: qwenNonThinkingAgg.gen1PartialMean,
+        deltaAbsolute: qwenNonThinkingAgg.deltaAbsolute,
+      },
+    },
+    cellSemanticAnchorInvarianceCountPerCandidate,
+    preRegisteredDeltaFloorVerdict: deltaFloorVerdict,
+    midRunHaltsBindingThresholds: {
+      perCandidateCostOvershoot: {
+        threshold: PER_CANDIDATE_COST_OVERSHOOT_THRESHOLD_USD,
+        candidatesOvershoot: overshootCount,
+      },
+      perShapeVariance: {
+        thresholdPP: PER_SHAPE_VARIANCE_HALT_PP,
+        maxRangeObservedPP,
+      },
+      qwenRetrievalRegression: {
+        triggered: qwenRegressionTriggered,
+        details: qwenRegressionDetails,
+      },
+    },
+  };
+}
+
+// ── Main ──────────────────────────────────────────────────────────────────
+
+async function main(): Promise<void> {
+  const args = parseArgs(process.argv.slice(2));
+  fs.mkdirSync(OUT_DIR, { recursive: true });
+  fs.mkdirSync(SCRATCH_DIR, { recursive: true });
+  if (!fs.existsSync(RUN_LOG)) fs.writeFileSync(RUN_LOG, '');
+
+  const corpus = loadCorpus();
+  const sample = deterministicShuffle(corpus, SAMPLING_SEED).slice(0, N_PER_SHAPE);
+  log(`[loaded] corpus=${corpus.length}; sample=${N_PER_SHAPE} via seed=${SAMPLING_SEED}`);
+
+  const candidates = await loadCandidates();
+  log(`[loaded] ${SHAPES.length} shapes × ${N_CANDIDATES_PER_SHAPE} candidates each`);
+
+  // Amendment 7 — pre-validate all candidates against cell-semantic anchors (Tier 3 input)
+  const candidateValidatorVerdicts = new Map<string, ValidatorVerdict | null>();
+  const TYPES_FILE_PATH = path.join(PROMPT_SHAPES_DIR, 'types.ts');
+  for (const shape of SHAPES) {
+    for (const cand of candidates.get(shape)!) {
+      if (cand.variant === 'baseline') {
+        // Baselines pass by definition (they ARE the pinned shape file)
+        candidateValidatorVerdicts.set(cand.candidateId, null); // null = baseline (Tier 3 = 0.10 by anchor invariance)
+        continue;
+      }
+      const filename = `${shape}-${cand.variant}.ts`;
+      const candPath = path.join(GEPA_EVOLVED_DIR, filename);
+      try {
+        const verdict = validateCandidate({
+          candidateShapeFilePath: candPath,
+          baselineShapeName: `${shape}.ts`,
+          typesFilePath: TYPES_FILE_PATH,
+          expectShapeDiff: true,
+        });
+        candidateValidatorVerdicts.set(cand.candidateId, verdict);
+        log(`[validator] ${cand.candidateId} valid=${verdict.valid} violations=${verdict.violations.length}`);
+      } catch (e) {
+        log(`[validator] ${cand.candidateId} ERROR ${(e as Error).message}`);
+        candidateValidatorVerdicts.set(cand.candidateId, null);
+      }
+    }
+  }
+
+  if (args.mode === 'dry-run') {
+    log(`[dry-run] would run 5×3×8 = 120 evals (or halt at 30 = Checkpoint B)`);
+    for (const shape of SHAPES) {
+      for (const cand of candidates.get(shape)!) {
+        log(`[dry-run] candidate=${cand.candidateId} variant=${cand.variant} shape.name=${cand.promptShape.name}`);
+      }
+    }
+    return;
+  }
+
+  // Resume support
+  const existing = new Set<string>();
+  let cumulativeCost = 0;
+
+  // Amendment 7 — per-candidate accumulator (rebuilt from JSONL on resume)
+  const accs = new Map<string, CandidateAcc>();
+  for (const shape of SHAPES) {
+    for (const cand of candidates.get(shape)!) {
+      const verdict = candidateValidatorVerdicts.get(cand.candidateId);
+      // Baselines: validatorPassed = true (anchor invariant by definition).
+      // Mutations: validatorPassed = verdict.valid (or false if validator threw).
+      const validatorPassed = cand.variant === 'baseline' ? true : verdict?.valid ?? false;
+      accs.set(cand.candidateId, makeCandidateAcc(cand, validatorPassed));
+    }
+  }
+
+  if (fs.existsSync(OUT_JSONL)) {
+    for (const line of fs.readFileSync(OUT_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean)) {
+      try {
+        const r = JSON.parse(line) as EvalRecord;
+        existing.add(`${r.candidateId}__${r.instanceId}`);
+        cumulativeCost += r.evalCostUsd;
+        const acc = accs.get(r.candidateId);
+        if (acc) ingestEvalIntoAcc(acc, r);
+      } catch { /* skip */ }
+    }
+    log(`[resume] loaded ${existing.size} existing evals; cumulative $${cumulativeCost.toFixed(4)}`);
+  }
+
+  const out = fs.createWriteStream(OUT_JSONL, { flags: existing.size > 0 ? 'a' : 'w' });
+  const embedder = createOllamaEmbedder({ baseUrl: OLLAMA_URL, model: EMBEDDER_MODEL });
+
+  const haltAt = args.mode === 'checkpoint-b' ? CHECKPOINT_B_HALT_EVALS : 120;
+  log(`[mode=${args.mode}] target eval count: ${haltAt}`);
+
+  let nDone = existing.size;
+  let amendment7HaltReason: string | null = null;
+
+  outer: for (const shape of SHAPES) {
+    for (const cand of candidates.get(shape)!) {
+      for (const inst of sample) {
+        const key = `${cand.candidateId}__${inst.instanceId}`;
+        if (existing.has(key)) { log(`[skip] ${key} already in JSONL`); continue; }
+        if (nDone >= haltAt) { log(`[HALT] reached ${haltAt} evals (Checkpoint B)`); break outer; }
+        if (cumulativeCost >= COST_HALT_USD) { log(`[HALT] cumulative $${cumulativeCost.toFixed(4)} >= $${COST_HALT_USD}`); break outer; }
+        const r = await runOneEval(cand, inst, embedder);
+        if ('error' in r) { log(`[skip] ${key}: ${r.error}`); continue; }
+        out.write(JSON.stringify(r) + '\n');
+        cumulativeCost += r.evalCostUsd;
+        nDone++;
+        // Amendment 7 — update accumulator + check mid-run halts
+        const acc = accs.get(cand.candidateId);
+        if (acc) ingestEvalIntoAcc(acc, r);
+        const haltCheck = checkMidRunHalts(accs);
+        if (haltCheck.shouldHalt) {
+          amendment7HaltReason = haltCheck.reason;
+          log(`[HALT-A7] ${haltCheck.reason}`);
+          break outer;
+        }
+        log(`[cumulative] $${cumulativeCost.toFixed(4)} / $${COST_HALT_USD} halt; ${nDone} evals total`);
+      }
+    }
+  }
+  out.end();
+
+  // Amendment 7 — write Checkpoint B summary (binding extension per §checkpoint_b_tightened.report_extensions)
+  const summary = buildCheckpointBSummary(args, accs, nDone, cumulativeCost, amendment7HaltReason);
+  fs.writeFileSync(SUMMARY_JSON, JSON.stringify(summary, null, 2));
+  log(`[summary] wrote ${SUMMARY_JSON}`);
+  log(`[delta-floor] verdict=${summary.preRegisteredDeltaFloorVerdict.overallVerdict}`);
+  log(`[delta-floor]   threshold_1_aggregate_tier_1=${summary.preRegisteredDeltaFloorVerdict.threshold1AggregateTier1} (value=${summary.preRegisteredDeltaFloorVerdict.threshold1ValuePP.toFixed(2)}pp)`);
+  log(`[delta-floor]   threshold_2_qwen_retrieval_absolute=${summary.preRegisteredDeltaFloorVerdict.threshold2QwenRetrievalAbsolute} (max_delta=${summary.preRegisteredDeltaFloorVerdict.threshold2MaxDeltaAbsolute.toFixed(3)})`);
+  log(`[delta-floor]   threshold_3_compound_tier_1_plus_tier_2=${summary.preRegisteredDeltaFloorVerdict.threshold3CompoundTier1PlusTier2} (tier1=${summary.preRegisteredDeltaFloorVerdict.threshold3Tier1ValuePP.toFixed(2)}pp tier2_agg=${summary.preRegisteredDeltaFloorVerdict.threshold3Tier2Aggregate.toFixed(3)})`);
+
+  log(`[done] ${nDone} evals; total cost $${cumulativeCost.toFixed(4)}; halt_reason=${amendment7HaltReason ?? 'none (Checkpoint B reached or completed)'}`);
+}
+
+main().catch(e => { console.error('FATAL:', e); process.exit(2); });

benchmarks/gepa/scripts/faza-1/run-mutation-oracle.ts

@@ -0,0 +1,438 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — mutation oracle runner.
+ *
+ * Per launch decision §G step 7 + manifest v7 §gepa.mutation_oracle +
+ * §mutation_oracle_design + Amendment 2 §4 (forked Qwen vs non-Qwen templates).
+ *
+ * For each of 5 shapes, generate 2 mutation candidates via Opus 4.7. Each
+ * candidate is validated via mutation-validator.ts (cell-semantic preservation).
+ * Output: 10 candidate files at packages/agent/src/prompt-shapes/gepa-evolved/<shape>-gen1-v<N>.ts
+ *
+ * Approach:
+ * - Use JSON-mode response_format (Amendment 4 lesson) for reliable parsing
+ * - Opus outputs JSON with the 5 method body strings + new evidence_link
+ * - Runner assembles TS file from fixed template (preserves cell semantics by construction)
+ * - Validator confirms types.ts + MULTI_STEP_ACTION_CONTRACT SHAs unchanged
+ *
+ * Cost projection: 10 calls × ~$0.15 = ~$1.50
+ *
+ * Failure handling per brief §5: 2 consecutive invalid mutations from oracle
+ * → halt-and-PM.
+ */
+
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+import { fileURLToPath } from 'node:url';
+
+import { validateCandidate, type ValidatorVerdict } from '../../src/faza-1/mutation-validator.js';
+import { classifyShape, type TemplateClass } from '../../src/faza-1/mutation-oracle-fork.js';
+import { type ShapeName, QWEN_TARGETED_SHAPES } from '../../src/faza-1/types.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const REPO_ROOT = path.resolve(__dirname, '../../../..');
+
+const PROMPT_SHAPES_DIR = path.join(REPO_ROOT, 'packages/agent/src/prompt-shapes');
+const TYPES_FILE = path.join(PROMPT_SHAPES_DIR, 'types.ts');
+const GEPA_EVOLVED_DIR = path.join(PROMPT_SHAPES_DIR, 'gepa-evolved');
+const ORACLE_LOG = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/gen-1/mutation-oracle-run.log');
+const OUT_MANIFEST = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/gen-1/mutation-oracle-manifest.json');
+
+const LITELLM_URL = process.env.LITELLM_URL ?? 'http://localhost:4000';
+const ORACLE_MODEL = 'claude-opus-4-7';
+const ORACLE_MAX_TOKENS = 8000;
+const N_MUTATIONS_PER_SHAPE = 2;
+
+const SHAPES: ShapeName[] = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'];
+
+const PHASE_4_3_FAILURE_MODES = `Top T2 categories from Phase 4.3 verdict (decisions/2026-04-28-phase-4-3-rescore-delta-report.md):
+1. unsupported-specifics (10 of 26 T2 hits): hallucinated specifics or overreach beyond materials
+2. missed / didn't-consider / shallow (9 of 26 T2 hits): incomplete coverage of source documents
+3. conflation / weak-synthesis (5 of 26 T2 hits): risks blended together rather than separated
+4. wrong-entity / off-topic (sparse): minor framing errors
+
+Phase 4.5 retrieval-engagement signal: Qwen retrieves 1.33×/task vs Opus 2.33×/task on byte-identical tool surface. H3 corpus NULL-baseline replicated this (mean retrievals = 1.05).`;
+
+// ── Logging ────────────────────────────────────────────────────────────────
+
+function log(msg: string): void {
+  const line = `[${new Date().toISOString()}] ${msg}\n`;
+  try { fs.appendFileSync(ORACLE_LOG, line); } catch { /* dir may not exist yet */ }
+  process.stderr.write(line);
+}
+
+// ── Baseline shape inspection (extract metadata for prompt) ────────────────
+
+interface BaselineShapeMetadata {
+  description: string;
+  modelClass: string;
+  defaultThinking: boolean;
+  defaultMaxTokens: number;
+  shapeFileContent: string;  // full file text
+}
+
+function loadBaselineShape(shapeName: ShapeName): BaselineShapeMetadata {
+  const shapeFile = path.join(PROMPT_SHAPES_DIR, `${shapeName}.ts`);
+  const content = fs.readFileSync(shapeFile, 'utf-8');
+
+  const description = content.match(/description: '([^']+)'/)?.[1] ?? '';
+  const modelClass = content.match(/modelClass: '([^']+)'/)?.[1] ?? shapeName;
+  const defaultThinking = content.match(/defaultThinking: (true|false|undefined)/)?.[1] === 'true';
+  const defaultMaxTokens = Number(content.match(/defaultMaxTokens: (\d+)/)?.[1] ?? 4096);
+
+  return { description, modelClass, defaultThinking, defaultMaxTokens, shapeFileContent: content };
+}
+
+// ── Oracle prompt (JSON-mode) ──────────────────────────────────────────────
+
+function buildOraclePrompt(shapeName: ShapeName, baseline: BaselineShapeMetadata, mutationIdx: number): string {
+  const cls: TemplateClass = classifyShape(shapeName);
+  const isQwen = QWEN_TARGETED_SHAPES.has(shapeName);
+
+  const qwenGuidance = `For Qwen-targeted shape mutation:
+- Emphasize multi-turn retrieval over single-shot retrieval. Phrase like "Continue retrieving until you have evidence from at least 2 distinct queries before finalizing."
+- Add anti-premature-finalization scaffolding. Phrase like "Before finalizing, ask: what gap in evidence remains? Issue another retrieval if any gap exists."
+- Encourage iterative refinement of retrieval queries based on prior turn results.
+- Goal: push mean retrieval_calls per task from current 1.0 baseline toward >= 1.5 (escape Amendment 2 penalty zone) and ideally >= 2.0 (Opus parity proxy).`;
+
+  const nonQwenGuidance = `For non-Qwen shape mutation:
+- Standard mutation guidance per brief §3.3 — evolve reasoning scaffold, planning step structure, chain-of-thought triggers.
+- Restructure implicit reasoning prompts (e.g., "think step by step" variants, planning bullets).
+- Refine where the model is prompted to articulate reasoning before producing output.
+- Improve multi-step task decomposition explicitness.`;
+
+  return `You are an expert prompt engineer. Generate ONE mutated variant of the prompt-shape below, evolving reasoning scaffold + retrieval-engagement guidance while preserving cell semantics.
+
+## Target shape
+- Name: ${shapeName}
+- Class: ${cls}
+- Mutation variant index: ${mutationIdx} (you are generating mutation #${mutationIdx} of 2 for this shape)
+
+## Baseline metadata (LOCKED — do NOT change these)
+- description: ${baseline.description}
+- modelClass: ${baseline.modelClass}
+- defaultThinking: ${baseline.defaultThinking}
+- defaultMaxTokens: ${baseline.defaultMaxTokens}
+
+## Phase 4.3 + Phase 4.5 failure modes to address
+${PHASE_4_3_FAILURE_MODES}
+
+## Mutation guidance
+${isQwen ? qwenGuidance : nonQwenGuidance}
+
+## Cell semantic boundaries (LOCKED — violation = REJECTED candidate)
+You may NOT modify:
+- The MULTI_STEP_ACTION_CONTRACT constant (lives in types.ts; bytes are SHA-pinned)
+- The JSON action contract format ({"action": "retrieve" | "finalize", ...})
+- Task framing (persona/question/materials section labels)
+- Imports block
+- Locked metadata fields above
+
+You MAY modify:
+- The 5 method bodies (string-building only): systemPromptSolo, systemPromptMultiStep, soloUserPrompt, multiStepKickoffUserPrompt, retrievalInjectionUserPrompt
+- The evidence_link metadata (you MUST update to point to GEPA Gen 1 results: "benchmarks/results/gepa-faza1/gen-1/mutation-oracle-run.log + Phase 4.5 + Amendment 2 §3 retrieval-engagement bonus")
+
+## Baseline shape file (your input)
+
+\`\`\`typescript
+${baseline.shapeFileContent}
+\`\`\`
+
+## Your output: JSON object
+
+Output a single JSON object with these fields. Each method body field should be a TypeScript expression that evaluates to a string (the prompt text). Use the same approach as the baseline (e.g., array.join('\\n')). Variable references like \${persona}, \${question}, \${maxSteps}, \${maxRetrievalsPerStep}, \${input.persona}, etc. must be preserved verbatim where the baseline used them.
+
+\`\`\`json
+{
+  "evidenceLink": "<updated evidence_link string referencing Gen 1 results + Phase 4.5/Amendment 2>",
+  "systemPromptSolo": "<TypeScript expression returning system prompt for !isMultiStep — typically a join of strings or template literal; reference {persona}>",
+  "systemPromptMultiStep": "<TypeScript expression returning system prompt for isMultiStep — must reference MULTI_STEP_ACTION_CONTRACT verbatim, {persona}, {question}, {maxSteps}, {maxRetrievalsPerStep}>",
+  "soloUserPrompt": "<TypeScript expression returning user prompt with {input.persona}, {input.materials}, {input.question}>",
+  "multiStepKickoffUserPrompt": "<TypeScript expression returning kickoff user message; baseline uses 'Begin. Output your first action JSON now.' — your variant should request engagement scaffolding for Qwen shapes>",
+  "retrievalInjectionUserPrompt": "<TypeScript expression returning retrieval-injection user message with {input.query}, {input.resultCount}, {input.results}>"
+}
+\`\`\`
+
+CRITICAL: each field's value must be a STRING containing valid TypeScript code that, when wrapped in \`return (\${value})\`, would compile + return a string. The simplest valid pattern is template literals or .join('\\n') over an array of strings.
+
+Output ONLY the JSON object. No prose. No code fences.`;
+}
+
+// ── LLM call (JSON-mode, no temperature per Amendment 4 lesson) ────────────
+
+interface OracleCallResult {
+  content: string;
+  inTokens: number;
+  outTokens: number;
+  costUsd: number;
+  latencyMs: number;
+  error?: string;
+}
+
+async function callOpusOracle(prompt: string): Promise<OracleCallResult> {
+  const masterKey = process.env.LITELLM_MASTER_KEY;
+  if (!masterKey) throw new Error('LITELLM_MASTER_KEY env not set');
+
+  const payload = {
+    model: ORACLE_MODEL,
+    messages: [{ role: 'user', content: prompt }],
+    max_tokens: ORACLE_MAX_TOKENS,
+    response_format: { type: 'json_object' },
+    // temperature omitted per Amendment 4 (Anthropic deprecates with JSON mode)
+  };
+
+  const started = Date.now();
+  let lastErr: string | undefined;
+  for (let attempt = 0; attempt < 2; attempt++) {
+    try {
+      const resp = await fetch(`${LITELLM_URL}/chat/completions`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${masterKey}` },
+        body: JSON.stringify(payload),
+      });
+      const d: any = await resp.json();
+      if ('error' in d) {
+        lastErr = String(d.error?.message ?? JSON.stringify(d.error)).slice(0, 200);
+        if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+        continue;
+      }
+      const content = d.choices?.[0]?.message?.content ?? '';
+      const usage = d.usage ?? {};
+      const inTok = usage.prompt_tokens ?? 0;
+      const outTok = usage.completion_tokens ?? 0;
+      const costUsd = (inTok * 15.0 + outTok * 75.0) / 1_000_000;
+      return { content, inTokens: inTok, outTokens: outTok, costUsd, latencyMs: Date.now() - started };
+    } catch (e) {
+      lastErr = `${(e as Error).name}: ${(e as Error).message}`.slice(0, 200);
+      if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+    }
+  }
+  return { content: '', inTokens: 0, outTokens: 0, costUsd: 0, latencyMs: Date.now() - started, error: lastErr };
+}
+
+// ── JSON parsing + TS file assembly ────────────────────────────────────────
+
+interface MutationFields {
+  evidenceLink: string;
+  systemPromptSolo: string;
+  systemPromptMultiStep: string;
+  soloUserPrompt: string;
+  multiStepKickoffUserPrompt: string;
+  retrievalInjectionUserPrompt: string;
+}
+
+function parseOracleOutput(content: string): MutationFields | { error: string } {
+  let s = content.trim();
+  if (s.startsWith('```')) s = s.replace(/^```[a-z]*\n?/, '').replace(/```\s*$/, '');
+  const firstBrace = s.indexOf('{');
+  const lastBrace = s.lastIndexOf('}');
+  if (firstBrace < 0 || lastBrace < 0) {
+    return { error: `no JSON object found in output (length=${content.length})` };
+  }
+  try {
+    const obj = JSON.parse(s.slice(firstBrace, lastBrace + 1));
+    const required = ['evidenceLink', 'systemPromptSolo', 'systemPromptMultiStep', 'soloUserPrompt', 'multiStepKickoffUserPrompt', 'retrievalInjectionUserPrompt'];
+    for (const k of required) {
+      if (typeof obj[k] !== 'string' || obj[k].length === 0) {
+        return { error: `field "${k}" missing or empty` };
+      }
+    }
+    return obj as MutationFields;
+  } catch (e) {
+    return { error: `JSON parse failed: ${(e as Error).message}` };
+  }
+}
+
+function buildShapeFile(shapeName: ShapeName, baseline: BaselineShapeMetadata, fields: MutationFields, mutationIdx: number): string {
+  const exportName = `${shapeName.replace(/-([a-z])/g, (_, c) => c.toUpperCase())}Gen1V${mutationIdx}Shape`;
+  const tsName = `${shapeName}-gen1-v${mutationIdx}`;
+  return `/**
+ * GEPA Faza 1 — Gen 1 mutation #${mutationIdx} of ${shapeName}.
+ *
+ * Generated by Opus 4.7 mutation oracle per Amendment 2 §4 forked template
+ * (${classifyShape(shapeName)} branch). Cell-semantic boundaries preserved
+ * via mutation-validator.ts SHA pins.
+ *
+ * Evidence: ${fields.evidenceLink}
+ *
+ * Baseline anchor: packages/agent/src/prompt-shapes/${shapeName}.ts (manifest v7
+ * §gepa.mutation_validator.baseline_shape_shas[${shapeName}.ts]).
+ */
+
+import {
+  type PromptShape,
+  type SystemPromptInput,
+  type SoloUserPromptInput,
+  type MultiStepKickoffInput,
+  type RetrievalInjectionInput,
+  MULTI_STEP_ACTION_CONTRACT,
+} from '../types.js';
+
+export const ${exportName}: PromptShape = {
+  name: '${tsName}',
+  metadata: {
+    description: '${baseline.description.replace(/'/g, "\\'")}',
+    modelClass: '${baseline.modelClass}',
+    evidence_link: ${JSON.stringify(fields.evidenceLink)},
+    defaultThinking: ${baseline.defaultThinking},
+    defaultMaxTokens: ${baseline.defaultMaxTokens},
+  },
+
+  systemPrompt(input: SystemPromptInput): string {
+    const { persona, question, isMultiStep, maxSteps = 5, maxRetrievalsPerStep = 8 } = input;
+    if (!isMultiStep) {
+      return ${fields.systemPromptSolo};
+    }
+    return ${fields.systemPromptMultiStep};
+  },
+
+  soloUserPrompt(input: SoloUserPromptInput): string {
+    return ${fields.soloUserPrompt};
+  },
+
+  multiStepKickoffUserPrompt(_input: MultiStepKickoffInput): string {
+    return ${fields.multiStepKickoffUserPrompt};
+  },
+
+  retrievalInjectionUserPrompt(input: RetrievalInjectionInput): string {
+    return ${fields.retrievalInjectionUserPrompt};
+  },
+};
+`;
+}
+
+// ── Validate via runtime sanity (compile check) + structural check ─────────
+
+function validateAssembledFile(filepath: string, baselineShapeName: ShapeName): { valid: boolean; reason?: string } {
+  // Check file parses as TypeScript by attempting a require-style import
+  // For Faza 1 simplicity, just check that the file:
+  // 1. Imports from types.js (mutation-validator's import preservation check)
+  // 2. Contains the required locked metadata fields
+  // 3. Exports a single PromptShape
+  // 4. Doesn't break the cell-semantic boundary (types.ts/MULTI_STEP_ACTION_CONTRACT SHAs unchanged)
+  // The mutation-validator.ts does the SHA checks; we use it via validateCandidate.
+
+  const verdict: ValidatorVerdict = validateCandidate({
+    candidateShapeFilePath: filepath,
+    baselineShapeName: `${baselineShapeName}.ts` as any,
+    typesFilePath: TYPES_FILE,
+    expectShapeDiff: true,
+  });
+
+  if (!verdict.valid) {
+    return { valid: false, reason: `validator violations: ${verdict.violations.map(v => `${v.category}: ${v.detail}`).join('; ')}` };
+  }
+  return { valid: true };
+}
+
+// ── Main: generate 10 mutations ────────────────────────────────────────────
+
+interface MutationManifestEntry {
+  shape: ShapeName;
+  mutationIdx: number;
+  filename: string;
+  costUsd: number;
+  latencyMs: number;
+  tsName: string;
+  validatorVerdict: 'valid' | 'invalid_after_retry';
+  error?: string;
+}
+
+async function main(): Promise<void> {
+  fs.mkdirSync(GEPA_EVOLVED_DIR, { recursive: true });
+  fs.mkdirSync(path.dirname(ORACLE_LOG), { recursive: true });
+  if (!fs.existsSync(ORACLE_LOG)) fs.writeFileSync(ORACLE_LOG, '');
+
+  log(`[start] mutation oracle for ${SHAPES.length} shapes × ${N_MUTATIONS_PER_SHAPE} mutations`);
+
+  const manifest: MutationManifestEntry[] = [];
+  let cumulativeCost = 0;
+  let consecutiveInvalidGlobal = 0;
+
+  for (const shapeName of SHAPES) {
+    const baseline = loadBaselineShape(shapeName);
+    log(`[${shapeName}] baseline loaded; modelClass=${baseline.modelClass} description="${baseline.description.slice(0, 60)}..."`);
+
+    for (let mutIdx = 1; mutIdx <= N_MUTATIONS_PER_SHAPE; mutIdx++) {
+      const tsName = `${shapeName}-gen1-v${mutIdx}`;
+      const outFile = path.join(GEPA_EVOLVED_DIR, `${tsName}.ts`);
+
+      if (fs.existsSync(outFile)) {
+        log(`[${tsName}] already exists; skipping`);
+        manifest.push({ shape: shapeName, mutationIdx: mutIdx, filename: outFile, costUsd: 0, latencyMs: 0, tsName, validatorVerdict: 'valid' });
+        continue;
+      }
+
+      // Try once; if invalid, retry once with structural feedback. After 2 fails → mark invalid_after_retry.
+      let valid = false;
+      let totalCost = 0;
+      let totalLatency = 0;
+      let errMsg: string | undefined;
+
+      for (let attempt = 0; attempt < 2 && !valid; attempt++) {
+        const prompt = buildOraclePrompt(shapeName, baseline, mutIdx);
+        log(`[${tsName}] oracle call attempt ${attempt + 1}; prompt_len=${prompt.length}c`);
+        const llm = await callOpusOracle(prompt);
+        totalCost += llm.costUsd;
+        totalLatency += llm.latencyMs;
+
+        if (llm.error) {
+          errMsg = `LLM error: ${llm.error}`;
+          log(`[${tsName}] ${errMsg}`);
+          continue;
+        }
+        const parsed = parseOracleOutput(llm.content);
+        if ('error' in parsed) {
+          errMsg = `parse error: ${parsed.error}`;
+          log(`[${tsName}] ${errMsg}; first 200c: ${llm.content.slice(0, 200)}`);
+          continue;
+        }
+
+        const tsContent = buildShapeFile(shapeName, baseline, parsed, mutIdx);
+        fs.writeFileSync(outFile, tsContent, 'utf-8');
+
+        const v = validateAssembledFile(outFile, shapeName);
+        if (v.valid) {
+          valid = true;
+          log(`[${tsName}] OK; cost=$${llm.costUsd.toFixed(4)}; latency=${llm.latencyMs}ms; file=${path.basename(outFile)}`);
+        } else {
+          errMsg = `validator failed: ${v.reason}`;
+          log(`[${tsName}] ${errMsg}`);
+          fs.unlinkSync(outFile);
+        }
+      }
+
+      cumulativeCost += totalCost;
+      manifest.push({
+        shape: shapeName, mutationIdx: mutIdx, filename: outFile,
+        costUsd: totalCost, latencyMs: totalLatency, tsName,
+        validatorVerdict: valid ? 'valid' : 'invalid_after_retry',
+        error: valid ? undefined : errMsg,
+      });
+
+      if (valid) {
+        consecutiveInvalidGlobal = 0;
+      } else {
+        consecutiveInvalidGlobal++;
+        if (consecutiveInvalidGlobal >= 2) {
+          log(`[HALT] 2 consecutive invalid mutations from oracle (per brief §5) — stopping`);
+          break;
+        }
+      }
+    }
+    if (consecutiveInvalidGlobal >= 2) break;
+  }
+
+  fs.writeFileSync(OUT_MANIFEST, JSON.stringify({
+    totalMutationsAttempted: manifest.length,
+    totalValid: manifest.filter(m => m.validatorVerdict === 'valid').length,
+    totalInvalid: manifest.filter(m => m.validatorVerdict !== 'valid').length,
+    cumulativeCostUsd: +cumulativeCost.toFixed(6),
+    entries: manifest,
+    completedAtIso: new Date().toISOString(),
+  }, null, 2));
+  log(`[done] ${manifest.length} mutations attempted; ${manifest.filter(m => m.validatorVerdict === 'valid').length} valid; cumulative $${cumulativeCost.toFixed(4)}`);
+}
+
+main().catch(e => { console.error('FATAL:', e); process.exit(2); });

benchmarks/gepa/scripts/faza-1/run-null-baseline.ts

@@ -0,0 +1,662 @@
+#!/usr/bin/env tsx
+/**
+ * GEPA Faza 1 — NULL-baseline runner.
+ *
+ * Per launch decision §G step 6 + §F + §A.5/A.7/A.10.
+ *
+ * Per Amendment 4 + binding texture-audit verdict, the corpus is 50/50 (PM
+ * ratified). NULL-baseline measures each of 5 baseline prompt-shapes against
+ * 8 stratified instances from the corpus to establish per-shape trio_strict_pass
+ * baseline rate.
+ *
+ * --------------------------------------------------------------------------
+ * MULTI-STEP MODE (vs solo) — design rationale
+ * --------------------------------------------------------------------------
+ *
+ * Brief §2 says "Cell scope Faza 1: H3 only" and pilot Cell C labels H3 as
+ * "Qwen solo". HOWEVER, Amendment 2 §3 retrieval_engagement_bonus only makes
+ * sense if a retrieval tool is present (in solo mode, retrieval_calls always
+ * = 0 → bonus pinned at -0.05 → fitness function cannot discriminate
+ * candidates). Amendment 2 §6 Phase 5 forward-record acceptance criteria
+ * (engagement parity ≥ Opus + score parity narrowed by ≥0.30 H4 trio_mean
+ * delta) explicitly invoke retrieval-mode metrics.
+ *
+ * Resolution (BINDING for this runner): NULL-baseline runs in MULTI-STEP
+ * mode with retrieval tool available. The "H3 cell" in Faza 1 GEPA context
+ * means "Qwen-targeted evaluation with retrieval available", not pilot Cell
+ * C strict "Qwen solo". This reconciles brief §2 with Amendment 2 + Phase 5
+ * forward record, mirrors Phase 4.5 empirical setup (Cells B/D had
+ * retrieval), and makes Amendment 2 fitness function meaningful.
+ *
+ * Documented in Checkpoint A halt-and-PM report for PM ratification or pivot.
+ *
+ * --------------------------------------------------------------------------
+ * Cost projection (PM ratified ~$20):
+ * --------------------------------------------------------------------------
+ *
+ * 5 shapes × 8 instances × ($0.50/eval avg) ≈ $20 expected
+ *  - Subject: Qwen 3.6 35B-A3B (DashScope direct, ~$0.001/call × 2-3 calls)
+ *  - Trio judges: Opus 4.7 + GPT-5.4 + MiniMax M2.7 × ($0.05/call avg) = $0.15/eval
+ *  - Per pilot 2026-04-26 cost average = $0.47/cell
+ *
+ * Halt threshold (per launch decision §D + Amendment 3): if cumulative > $26
+ * (30% over $20 expected), halt-and-PM per super-linear sub-rule.
+ *
+ * --------------------------------------------------------------------------
+ * Sampling design:
+ * --------------------------------------------------------------------------
+ *
+ * Same 8 instances across all 5 shapes (controlled comparison; trio_mean delta
+ * is purely shape-attributable). Deterministic Mulberry32 with seed=42, then
+ * take first 8 of shuffled corpus. Held-out 5 = next 5 (instances 9-13)
+ * after the 8 — kept for Faza 1 §F.4 held-out validation.
+ *
+ * --------------------------------------------------------------------------
+ * Usage:
+ * --------------------------------------------------------------------------
+ *
+ *   npx tsx benchmarks/gepa/scripts/faza-1/run-null-baseline.ts --dry-run
+ *     # No LLM call. Validates sampling + shape resolution + substrate setup.
+ *
+ *   npx tsx benchmarks/gepa/scripts/faza-1/run-null-baseline.ts --probe
+ *     # Single (shape=qwen-thinking, instance=0). ~$0.50. Validates round-trip.
+ *
+ *   npx tsx benchmarks/gepa/scripts/faza-1/run-null-baseline.ts --all
+ *     # Full 5×8 = 40 evaluations. ~$20 expected, $26 halt.
+ */
+
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+import { fileURLToPath } from 'node:url';
+
+import {
+  MindDB,
+  FrameStore,
+  SessionStore,
+  HybridSearch,
+  createOllamaEmbedder,
+  type Embedder,
+} from '@waggle/core';
+import {
+  runRetrievalAgentLoop,
+  type LlmCallFn,
+  type LlmCallInput,
+  type LlmCallResult as AgentLlmCallResult,
+  type RetrievalSearchFn,
+  type AgentRunResult,
+} from '@waggle/agent';
+
+import { REGISTRY, selectShape } from '../../../../packages/agent/src/prompt-shapes/selector.js';
+import { type PromptShape } from '../../../../packages/agent/src/prompt-shapes/types.js';
+
+import { type CorpusInstance } from '../../src/faza-1/corpus.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const REPO_ROOT = path.resolve(__dirname, '../../../..');
+
+const CORPUS_JSONL = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/corpus/h3-northlane-cfo-50-instances.jsonl');
+const OUT_DIR = path.join(REPO_ROOT, 'benchmarks/results/gepa-faza1/null-baseline');
+const OUT_JSONL = path.join(OUT_DIR, 'null-baseline-eval.jsonl');
+const RUN_LOG = path.join(OUT_DIR, 'null-baseline-run.log');
+const SUMMARY_JSON = path.join(OUT_DIR, 'null-baseline-summary.json');
+const SCRATCH_DIR = path.join(REPO_ROOT, 'tmp/gepa-faza1-null-baseline');
+
+const LITELLM_URL = process.env.LITELLM_URL ?? 'http://localhost:4000';
+const OLLAMA_URL = 'http://localhost:11434';
+const EMBEDDER_MODEL = 'nomic-embed-text';
+
+const SAMPLING_SEED = 42;
+const N_PER_SHAPE = 8;
+
+// Shapes in fixed evaluation order (matches manifest v7 §gepa.shape_scope.targets)
+const SHAPES = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'] as const;
+type ShapeName = typeof SHAPES[number];
+
+// Subject inheritance per manifest v7 §subject (= pilot 2026-04-26 runner SHA 8a6251e2)
+const SUBJECT_ALIAS = 'qwen3.6-35b-a3b-via-dashscope-direct';
+const SUBJECT_MAX_TOKENS = 16000;
+const SUBJECT_THINKING = true;
+
+// Judges inheritance per manifest v7 §judges (= pilot runner line 626)
+const JUDGES = ['claude-opus-4-7', 'gpt-5.4', 'minimax-m27-via-openrouter'] as const;
+const JUDGE_MAX_TOKENS = 3000;
+const JUDGE_RETRIES = 3;
+
+// Multi-step orchestration
+const MAX_STEPS = 5;
+const MAX_RETRIEVALS_PER_STEP = 8;
+const PER_CALL_HALT_USD = 0.40;
+const PER_CELL_HALT_USD = 1.00;
+
+// Cost halt per launch decision §D (super-linear sub-rule per A.7)
+const COST_HALT_USD = 26.0;  // 30% over $20 expected
+
+// Pricing (per pilot runner line 128-133)
+const MODEL_PRICING: Record<string, { in: number; out: number }> = {
+  'claude-opus-4-7':                { in: 15.0, out: 75.0 },
+  'gpt-5.4':                        { in: 2.5,  out: 10.0 },
+  'minimax-m27-via-openrouter':     { in: 0.7,  out: 2.8 },
+  'qwen3.6-35b-a3b-via-dashscope-direct': { in: 0.20, out: 0.80 },
+  'qwen3.6-35b-a3b-via-openrouter':       { in: 0.6, out: 2.4 },
+};
+
+const MANIFEST_ANCHOR = 'manifest-v7-gepa-faza1';
+const MANIFEST_SHA_AMENDMENT_4 = '1f7a6d6fa01403f6c8d6855893adbfa5e82898a81b7583cfa55628e5eba60196';
+
+// ── Logging ────────────────────────────────────────────────────────────────
+
+function log(msg: string): void {
+  const line = `[${new Date().toISOString()}] ${msg}\n`;
+  try { fs.appendFileSync(RUN_LOG, line); } catch { /* dir not yet created */ }
+  process.stderr.write(line);
+}
+
+// ── CLI ────────────────────────────────────────────────────────────────────
+
+interface Args {
+  mode: 'dry-run' | 'probe' | 'all';
+  probeShape?: ShapeName;
+  probeInstanceIdx?: number;
+}
+
+function parseArgs(argv: string[]): Args {
+  let mode: Args['mode'] = 'dry-run';
+  let probeShape: ShapeName | undefined;
+  let probeInstanceIdx: number | undefined;
+  for (let i = 0; i < argv.length; i++) {
+    const flag = argv[i];
+    const next = argv[i + 1];
+    switch (flag) {
+      case '--dry-run': mode = 'dry-run'; break;
+      case '--probe':   mode = 'probe'; break;
+      case '--all':     mode = 'all'; break;
+      case '--probe-shape': probeShape = next as ShapeName; i++; break;
+      case '--probe-instance': probeInstanceIdx = Number(next); i++; break;
+    }
+  }
+  return { mode, probeShape, probeInstanceIdx };
+}
+
+// ── Deterministic sampling (Mulberry32, seed=42) ───────────────────────────
+
+function mulberry32(seed: number): () => number {
+  let t = seed >>> 0;
+  return () => {
+    t = (t + 0x6d2b79f5) >>> 0;
+    let r = t;
+    r = Math.imul(r ^ (r >>> 15), r | 1);
+    r ^= r + Math.imul(r ^ (r >>> 7), r | 61);
+    return ((r ^ (r >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+function deterministicShuffle<T>(items: ReadonlyArray<T>, seed: number): T[] {
+  const arr = [...items];
+  const rand = mulberry32(seed);
+  for (let i = arr.length - 1; i > 0; i--) {
+    const j = Math.floor(rand() * (i + 1));
+    [arr[i], arr[j]] = [arr[j], arr[i]];
+  }
+  return arr;
+}
+
+function loadCorpus(): CorpusInstance[] {
+  const text = fs.readFileSync(CORPUS_JSONL, 'utf-8');
+  return text.trim().split(/\n+/).filter(Boolean).map(l => JSON.parse(l) as CorpusInstance);
+}
+
+/**
+ * Sample N=8 instances deterministically. Same set across all shapes (controlled
+ * comparison). Held-out 5 are the next 5 after the sample (instances 9-13).
+ */
+function sampleInstances(corpus: CorpusInstance[], n: number, seed: number): CorpusInstance[] {
+  const shuffled = deterministicShuffle(corpus, seed);
+  return shuffled.slice(0, n);
+}
+
+// ── LiteLLM call adapter ──────────────────────────────────────────────────
+
+const llmCall: LlmCallFn = async (input: LlmCallInput): Promise<AgentLlmCallResult> => {
+  const masterKey = process.env.LITELLM_MASTER_KEY;
+  if (!masterKey) throw new Error('LITELLM_MASTER_KEY env not set');
+
+  const { model, messages } = input;
+  const isQwen = model.includes('qwen');
+  const maxTokens = input.maxTokens ?? (isQwen ? SUBJECT_MAX_TOKENS : 4096);
+  const thinking = input.thinking ?? (isQwen ? SUBJECT_THINKING : true);
+
+  const payload: Record<string, unknown> = { model, messages, max_tokens: maxTokens };
+
+  if (model.startsWith('claude-opus')) {
+    payload.temperature = 1.0;
+  } else if (model === 'gpt-5.4' || model === 'minimax-m27-via-openrouter') {
+    // omit temperature — reasoning-model defaults
+  } else {
+    payload.temperature = input.temperature ?? 0.3;
+  }
+
+  if (isQwen) {
+    payload.extra_body = { enable_thinking: thinking };
+  }
+
+  const started = Date.now();
+  let lastErr: string | undefined;
+  for (let attempt = 0; attempt < 2; attempt++) {
+    try {
+      const resp = await fetch(`${LITELLM_URL}/chat/completions`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${masterKey}` },
+        body: JSON.stringify(payload),
+      });
+      const d: any = await resp.json();
+      if ('error' in d) {
+        lastErr = String(d.error?.message ?? JSON.stringify(d.error)).slice(0, 200);
+        if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+        continue;
+      }
+      const content = d.choices?.[0]?.message?.content ?? '';
+      const usage = d.usage ?? {};
+      const inTok = usage.prompt_tokens ?? 0;
+      const outTok = usage.completion_tokens ?? 0;
+      const pricing = MODEL_PRICING[model] ?? { in: 1, out: 4 };
+      const costUsd = (inTok * pricing.in + outTok * pricing.out) / 1_000_000;
+      return { content, inTokens: inTok, outTokens: outTok, costUsd, latencyMs: Date.now() - started };
+    } catch (e) {
+      lastErr = `${(e as Error).name}: ${(e as Error).message}`.slice(0, 200);
+      if (attempt < 1) await new Promise(r => setTimeout(r, 1500));
+      continue;
+    }
+  }
+  return { content: '', inTokens: 0, outTokens: 0, costUsd: 0, latencyMs: Date.now() - started, error: lastErr ?? 'unknown error' };
+};
+
+// ── Trio judging (mirrors pilot 2026-04-26 runner line 545+) ───────────────
+
+const JUDGE_PROMPT_TEMPLATE = `You are evaluating an AI agent's response to a complex knowledge work task. The persona, scenario, materials, and question are provided. The response was generated under one of four configurations (revealed only after scoring): {model_only | model + memory + agent harness} × {Opus 4.7 | Qwen 3.6 35B-A3B}.
+
+You do NOT know which configuration produced this response. Score blind.
+
+Read the persona/scenario/question (provided), skim the materials (provided), then read the response carefully (provided).
+
+Score the response on six dimensions, Likert 1-5:
+
+1. COMPLETENESS — engagement with all material
+2. ACCURACY — faithfulness to source materials, no hallucinations
+3. SYNTHESIS — connections across inputs, not isolated treatment
+4. JUDGMENT — defensible recommendations, tradeoffs acknowledged
+5. ACTIONABILITY — would the persona act on this tomorrow
+6. STRUCTURE — organization and readability
+
+Output JSON only:
+{
+  "completeness": <1-5>,
+  "accuracy": <1-5>,
+  "synthesis": <1-5>,
+  "judgment": <1-5>,
+  "actionability": <1-5>,
+  "structure": <1-5>,
+  "rationale": "<1-2 sentences explaining the lowest scoring dimension>",
+  "overall_verdict": "<one of: PASS_STRONG | PASS_ADEQUATE | FAIL_WEAK | FAIL_CRITICAL>"
+}
+
+PASS_STRONG: mean >= 4.0
+PASS_ADEQUATE: mean 3.5-3.99
+FAIL_WEAK: mean 2.5-3.49
+FAIL_CRITICAL: mean < 2.5
+
+[PERSONA + SCENARIO + QUESTION]
+###PERSONA_SCENARIO_QUESTION###
+
+[MATERIALS]
+###MATERIALS###
+
+[RESPONSE TO EVALUATE]
+###RESPONSE###`;
+
+interface JudgeVerdict {
+  completeness: number;
+  accuracy: number;
+  synthesis: number;
+  judgment: number;
+  actionability: number;
+  structure: number;
+  rationale: string;
+  overall_verdict: string;
+  mean: number;
+}
+
+interface JudgeRecord extends JudgeVerdict {
+  judge_model: string;
+  judge_cost_usd: number;
+  judge_latency_ms: number;
+  judge_retries: number;
+}
+
+const ZERO_VERDICT: JudgeVerdict = {
+  completeness: 0, accuracy: 0, synthesis: 0, judgment: 0, actionability: 0, structure: 0,
+  rationale: '__JUDGE_FAILED__', overall_verdict: 'FAIL_CRITICAL', mean: 0,
+};
+
+function parseJudgeJson(text: string): JudgeVerdict | null {
+  const m = text.match(/\{[\s\S]*\}/);
+  if (!m) return null;
+  try {
+    const obj = JSON.parse(m[0]);
+    const dims = ['completeness', 'accuracy', 'synthesis', 'judgment', 'actionability', 'structure'] as const;
+    for (const d of dims) {
+      if (typeof obj[d] !== 'number' || obj[d] < 1 || obj[d] > 5) return null;
+    }
+    const mean = dims.reduce((s, d) => s + obj[d], 0) / dims.length;
+    return {
+      completeness: obj.completeness, accuracy: obj.accuracy, synthesis: obj.synthesis,
+      judgment: obj.judgment, actionability: obj.actionability, structure: obj.structure,
+      rationale: typeof obj.rationale === 'string' ? obj.rationale : '',
+      overall_verdict: typeof obj.overall_verdict === 'string' ? obj.overall_verdict : '',
+      mean,
+    };
+  } catch { return null; }
+}
+
+function buildJudgePrompt(instance: CorpusInstance, response: string): string {
+  return JUDGE_PROMPT_TEMPLATE
+    .replace('###PERSONA_SCENARIO_QUESTION###', `${instance.personaText}\n\nQUESTION: ${instance.question}`)
+    .replace('###MATERIALS###', instance.materialsConcat)
+    .replace('###RESPONSE###', response);
+}
+
+async function runJudge(judgeModel: string, prompt: string): Promise<JudgeRecord> {
+  let lastErr = '';
+  let totalCost = 0, totalLatency = 0;
+  for (let attempt = 0; attempt < JUDGE_RETRIES; attempt++) {
+    const r = await llmCall({ model: judgeModel, messages: [{ role: 'user', content: prompt }], maxTokens: JUDGE_MAX_TOKENS, thinking: false });
+    totalCost += r.costUsd; totalLatency += r.latencyMs;
+    if (r.error) { lastErr = `attempt ${attempt + 1}: ${r.error}`; continue; }
+    const parsed = parseJudgeJson(r.content);
+    if (parsed) {
+      return { ...parsed, judge_model: judgeModel, judge_cost_usd: totalCost, judge_latency_ms: totalLatency, judge_retries: attempt };
+    }
+    lastErr = `attempt ${attempt + 1}: malformed JSON: ${r.content.slice(0, 100)}`;
+  }
+  log(`[judge ${judgeModel}] FAILED after ${JUDGE_RETRIES}: ${lastErr}`);
+  return { ...ZERO_VERDICT, judge_model: judgeModel, judge_cost_usd: totalCost, judge_latency_ms: totalLatency, judge_retries: JUDGE_RETRIES, rationale: `__JUDGE_FAILED__: ${lastErr}` };
+}
+
+interface TrioResult {
+  records: JudgeRecord[];
+  trioMean: number;
+  trioStrictPassII: boolean;  // op (ii) — trio_mean >= 4.0
+  trioStrictPassI: boolean;   // op (i) — >=2 of 3 judges with mean >= 3.5
+  judgeCostTotal: number;
+}
+
+async function judgeTrio(instance: CorpusInstance, response: string): Promise<TrioResult> {
+  const prompt = buildJudgePrompt(instance, response);
+  const records = await Promise.all(JUDGES.map(j => runJudge(j, prompt)));
+  const validMeans = records.filter(r => r.mean > 0).map(r => r.mean);
+  const trioMean = validMeans.length > 0 ? validMeans.reduce((s, m) => s + m, 0) / validMeans.length : 0;
+  const trioStrictPassII = trioMean >= 4.0;
+  const trioStrictPassI = records.filter(r => r.mean >= 3.5).length >= 2;
+  const judgeCostTotal = records.reduce((s, r) => s + r.judge_cost_usd, 0);
+  return { records, trioMean, trioStrictPassII, trioStrictPassI, judgeCostTotal };
+}
+
+// ── Per-eval orchestration: run shape × instance via multi-step ───────────
+
+interface EvalRecord {
+  shape: ShapeName;
+  instanceId: string;
+  instanceCell: CorpusInstance['cell'];
+  candidateResponse: string;
+  candidateLatencyMs: number;
+  candidateTokensIn: number;
+  candidateTokensOut: number;
+  candidateCostUsd: number;
+  loopExhausted: boolean;
+  stepsTaken: number;
+  retrievalCalls: number;
+  judges: { records: JudgeRecord[]; trioMean: number; trioStrictPassII: boolean; trioStrictPassI: boolean; judgeCostTotal: number };
+  evalCostUsd: number;
+  manifestAnchor: string;
+  manifestShaAmendment4: string;
+  tsIso: string;
+}
+
+async function runOneEval(shape: PromptShape, instance: CorpusInstance, embedder: Embedder): Promise<EvalRecord | { error: string }> {
+  const evalId = `${shape.name}__${instance.instanceId}`;
+  log(`[${evalId}] start`);
+
+  // Per-eval SQLite + HybridSearch substrate (per pilot runner pattern)
+  const dbPath = path.join(SCRATCH_DIR, `eval-${shape.name}-${instance.instanceId}.sqlite`);
+  if (fs.existsSync(dbPath)) fs.unlinkSync(dbPath);
+  const db = new MindDB(dbPath);
+  const frames = new FrameStore(db);
+  const sessions = new SessionStore(db);
+  const hybrid = new HybridSearch(db, embedder);
+
+  const gopId = `gepa-faza1-null-${shape.name}-${instance.instanceId}`;
+  sessions.ensure(gopId, undefined, `NULL-baseline ${shape.name} on ${instance.instanceId}`);
+
+  // Ingest source documents
+  for (const doc of instance.sourceDocuments) {
+    const content = `## ${doc.title}\n\n${doc.body}`;
+    frames.createIFrame(gopId, content, 'important', 'system');
+  }
+  log(`[${evalId}] ingested ${instance.sourceDocuments.length} frames`);
+
+  // Retrieval adapter
+  const searchAdapter: RetrievalSearchFn = async ({ query, limit }) => {
+    const hits = await hybrid.search(query, { limit, gopId });
+    const formatted = hits.length > 0
+      ? hits.map((sr, i) => `[result ${i + 1}, score ${sr.finalScore.toFixed(3)}]\n${sr.frame.content}`).join('\n\n---\n\n')
+      : '';
+    return { formattedResults: formatted, resultCount: hits.length };
+  };
+
+  // Run multi-step retrieval agent loop.
+  // CRITICAL: pass promptShapeOverride to actually apply the per-shape prompt.
+  // Without this, runRetrievalAgentLoop calls selectShape(modelAlias) which always
+  // resolves to 'qwen-thinking' for Qwen subject (per config), making the runner's
+  // shape parameter unused. This bug was discovered post-Checkpoint-A and corrected
+  // per PM Option A ratification + manifest v7 Amendment 6.
+  let agentResult: AgentRunResult;
+  try {
+    agentResult = await runRetrievalAgentLoop({
+      modelAlias: SUBJECT_ALIAS,
+      persona: instance.personaText,
+      question: instance.question,
+      llmCall,
+      search: searchAdapter,
+      maxSteps: MAX_STEPS,
+      maxRetrievalsPerStep: MAX_RETRIEVALS_PER_STEP,
+      perCallHaltUsd: PER_CALL_HALT_USD,
+      perCellHaltUsd: PER_CELL_HALT_USD,
+      contextTag: evalId,
+      promptShapeOverride: shape.name,  // bug fix per Amendment 6
+    } as any);
+  } catch (e) {
+    const msg = `agent loop failed: ${(e as Error).message}`;
+    log(`[${evalId}] ${msg}`);
+    return { error: msg };
+  }
+
+  if (agentResult.errors.length > 0) {
+    log(`[${evalId}] agent errors: ${agentResult.errors.join('; ').slice(0, 200)}`);
+  }
+
+  log(`[${evalId}] subject_done; retrievals=${agentResult.retrievalCalls} steps=${agentResult.stepsTaken} cost=$${agentResult.totalCostUsd.toFixed(4)} loop_exhausted=${agentResult.loopExhausted}`);
+
+  // Judge response
+  const judges = await judgeTrio(instance, agentResult.rawResponse);
+  const evalCostUsd = agentResult.totalCostUsd + judges.judgeCostTotal;
+  log(`[${evalId}] judged; trio_mean=${judges.trioMean.toFixed(3)} pass_ii=${judges.trioStrictPassII} pass_i=${judges.trioStrictPassI} eval_cost=$${evalCostUsd.toFixed(4)}`);
+
+  return {
+    shape: shape.name as ShapeName,
+    instanceId: instance.instanceId,
+    instanceCell: instance.cell,
+    candidateResponse: agentResult.rawResponse,
+    candidateLatencyMs: agentResult.totalLatencyMs,
+    candidateTokensIn: agentResult.totalTokensIn,
+    candidateTokensOut: agentResult.totalTokensOut,
+    candidateCostUsd: agentResult.totalCostUsd,
+    loopExhausted: agentResult.loopExhausted,
+    stepsTaken: agentResult.stepsTaken,
+    retrievalCalls: agentResult.retrievalCalls,
+    judges,
+    evalCostUsd,
+    manifestAnchor: MANIFEST_ANCHOR,
+    manifestShaAmendment4: MANIFEST_SHA_AMENDMENT_4,
+    tsIso: new Date().toISOString(),
+  };
+}
+
+// ── Aggregate per-shape metrics + κ across batch ──────────────────────────
+
+interface ShapeAggregate {
+  shape: ShapeName;
+  nEvals: number;
+  trioStrictPassRateII: number;       // op (ii) primary
+  trioStrictPassRateI: number;        // op (i) supplementary
+  meanRetrievalCallsPerTask: number;
+  meanCandidateCostUsd: number;
+  meanJudgeCostUsd: number;
+  meanEvalCostUsd: number;
+  totalEvalCostUsd: number;
+  loopExhaustedRate: number;
+  meanStepsTaken: number;
+}
+
+function aggregatePerShape(records: EvalRecord[]): ShapeAggregate[] {
+  const byShape = new Map<ShapeName, EvalRecord[]>();
+  for (const r of records) {
+    if (!byShape.has(r.shape)) byShape.set(r.shape, []);
+    byShape.get(r.shape)!.push(r);
+  }
+  const out: ShapeAggregate[] = [];
+  for (const shape of SHAPES) {
+    const rs = byShape.get(shape) ?? [];
+    if (rs.length === 0) continue;
+    const passII = rs.filter(r => r.judges.trioStrictPassII).length;
+    const passI = rs.filter(r => r.judges.trioStrictPassI).length;
+    const meanRetr = rs.reduce((s, r) => s + r.retrievalCalls, 0) / rs.length;
+    const meanCandCost = rs.reduce((s, r) => s + r.candidateCostUsd, 0) / rs.length;
+    const meanJudgeCost = rs.reduce((s, r) => s + r.judges.judgeCostTotal, 0) / rs.length;
+    const meanEvalCost = rs.reduce((s, r) => s + r.evalCostUsd, 0) / rs.length;
+    const totalEvalCost = rs.reduce((s, r) => s + r.evalCostUsd, 0);
+    const exhaustedRate = rs.filter(r => r.loopExhausted).length / rs.length;
+    const meanSteps = rs.reduce((s, r) => s + r.stepsTaken, 0) / rs.length;
+    out.push({
+      shape, nEvals: rs.length,
+      trioStrictPassRateII: passII / rs.length,
+      trioStrictPassRateI: passI / rs.length,
+      meanRetrievalCallsPerTask: meanRetr,
+      meanCandidateCostUsd: meanCandCost,
+      meanJudgeCostUsd: meanJudgeCost,
+      meanEvalCostUsd: meanEvalCost,
+      totalEvalCostUsd: totalEvalCost,
+      loopExhaustedRate: exhaustedRate,
+      meanStepsTaken: meanSteps,
+    });
+  }
+  return out;
+}
+
+// ── Main ───────────────────────────────────────────────────────────────────
+
+async function main(): Promise<void> {
+  const args = parseArgs(process.argv.slice(2));
+  fs.mkdirSync(OUT_DIR, { recursive: true });
+  fs.mkdirSync(SCRATCH_DIR, { recursive: true });
+  if (!fs.existsSync(RUN_LOG)) fs.writeFileSync(RUN_LOG, '');
+
+  const corpus = loadCorpus();
+  log(`[loaded] corpus = ${corpus.length} instances from ${CORPUS_JSONL}`);
+
+  const sample = sampleInstances(corpus, N_PER_SHAPE, SAMPLING_SEED);
+  log(`[sampled] N=${N_PER_SHAPE} via seed=${SAMPLING_SEED}: ${sample.map(i => i.instanceId).join(', ')}`);
+
+  if (args.mode === 'dry-run') {
+    log(`[dry-run] would run ${SHAPES.length} shapes × ${N_PER_SHAPE} instances = ${SHAPES.length * N_PER_SHAPE} evals`);
+    for (const shapeName of SHAPES) {
+      const shape = REGISTRY[shapeName];
+      const sysPrompt = shape.systemPrompt({ persona: sample[0].personaText, question: sample[0].question, isMultiStep: true });
+      log(`[dry-run] shape=${shapeName} systemPrompt(multi-step)=${sysPrompt.length}c`);
+    }
+    log(`[dry-run] OK; no LLM call; cost: $0.00`);
+    return;
+  }
+
+  // Resume support: skip evals already in JSONL
+  const existing = new Set<string>();
+  let cumulativeCost = 0;
+  let allRecords: EvalRecord[] = [];
+  if (fs.existsSync(OUT_JSONL)) {
+    for (const line of fs.readFileSync(OUT_JSONL, 'utf-8').trim().split(/\n+/).filter(Boolean)) {
+      try {
+        const r = JSON.parse(line) as EvalRecord;
+        const key = `${r.shape}__${r.instanceId}`;
+        existing.add(key);
+        cumulativeCost += r.evalCostUsd;
+        allRecords.push(r);
+      } catch { /* skip */ }
+    }
+    log(`[resume] loaded ${existing.size} existing evals; cumulative cost $${cumulativeCost.toFixed(4)}`);
+  }
+
+  const out = fs.createWriteStream(OUT_JSONL, { flags: existing.size > 0 ? 'a' : 'w' });
+
+  const embedder = createOllamaEmbedder({ baseUrl: OLLAMA_URL, model: EMBEDDER_MODEL });
+
+  const targetShapes = args.mode === 'probe' ? [args.probeShape ?? 'qwen-thinking'] : SHAPES;
+  const targetInstances = args.mode === 'probe'
+    ? [sample[args.probeInstanceIdx ?? 0]]
+    : sample;
+
+  for (const shapeName of targetShapes) {
+    const shape = REGISTRY[shapeName];
+    if (!shape) { log(`[error] shape "${shapeName}" not in REGISTRY`); continue; }
+    for (const instance of targetInstances) {
+      const key = `${shapeName}__${instance.instanceId}`;
+      if (existing.has(key)) {
+        log(`[skip] ${key} already in JSONL`);
+        continue;
+      }
+      if (cumulativeCost >= COST_HALT_USD) {
+        log(`[HALT] cumulative $${cumulativeCost.toFixed(4)} >= $${COST_HALT_USD} cost halt — stopping`);
+        break;
+      }
+      const result = await runOneEval(shape, instance, embedder);
+      if ('error' in result) {
+        log(`[skip] ${key} due to: ${result.error}`);
+        continue;
+      }
+      out.write(JSON.stringify(result) + '\n');
+      cumulativeCost += result.evalCostUsd;
+      allRecords.push(result);
+      log(`[cumulative] $${cumulativeCost.toFixed(4)} / $${COST_HALT_USD} halt; ${allRecords.length} evals total`);
+    }
+  }
+  out.end();
+
+  // Aggregate + summary
+  const aggregates = aggregatePerShape(allRecords);
+  const summary = {
+    manifestAnchor: MANIFEST_ANCHOR,
+    manifestShaAmendment4: MANIFEST_SHA_AMENDMENT_4,
+    samplingSeed: SAMPLING_SEED,
+    nPerShape: N_PER_SHAPE,
+    sampledInstanceIds: sample.map(i => i.instanceId),
+    totalEvals: allRecords.length,
+    totalCostUsd: +cumulativeCost.toFixed(6),
+    perShape: aggregates,
+    completedAtIso: new Date().toISOString(),
+  };
+  fs.writeFileSync(SUMMARY_JSON, JSON.stringify(summary, null, 2), 'utf-8');
+  log(`[done] ${allRecords.length} evals; total cost $${cumulativeCost.toFixed(4)}; summary written`);
+}
+
+main().catch(e => {
+  console.error('FATAL:', e);
+  process.exit(2);
+});

benchmarks/gepa/src/faza-1/acceptance.ts

@@ -0,0 +1,154 @@
+/**
+ * GEPA Faza 1 — acceptance validator.
+ *
+ * Per launch decision §F (4 must-hold conditions) + §F.5 (Amendment 2 §5
+ * false-positive evolution guard).
+ *
+ * Per-candidate verdict logic:
+ *
+ * §F condition 1 (third update — Amendment 2 §5):
+ *   "Best GEPA candidate per shape beats NULL-baseline by ≥+5pp on
+ *    trio_strict_pass rate (trio_mean ≥ 4.0). For Qwen-targeted shapes,
+ *    additionally: best candidate must have mean retrieval_calls per task
+ *    ≥ 1.7 (engagement gap closed by ≥50% relative to Qwen baseline 1.33)."
+ *
+ * §F.5 (NEW per Amendment 2 — false-positive guard):
+ *   "If best Qwen-shape candidate achieves +5pp trio_strict delta WITHOUT
+ *    closing retrieval engagement gap (mean retrieval_calls < 1.5), this
+ *    signals false-positive evolution. Result: candidate REJECTED, shape
+ *    marked FAIL even if other criteria pass."
+ *
+ * Conditions §F.2/§F.3/§F.4 are evaluated at the run-aggregate level (across
+ * shapes / κ stability across all evaluations / mutation validator log) and
+ * are NOT checked per-candidate here. See selection.ts + run-orchestrator
+ * for those.
+ */
+
+import {
+  type AcceptanceInputs,
+  type AcceptanceVerdict,
+  QWEN_TARGETED_SHAPES,
+} from './types.js';
+
+/** §F condition 1 trio_strict delta threshold (percentage points). */
+export const TRIO_STRICT_DELTA_THRESHOLD_PP = 5;
+
+/**
+ * §F condition 1 Qwen-only retrieval engagement floor (mean retrieval_calls
+ * per task). 1.7 = 50% gap closure between Qwen baseline 1.33 and Opus
+ * parity 2.33 per Amendment 2 §5.
+ */
+export const QWEN_RETRIEVAL_ENGAGEMENT_FLOOR = 1.7;
+
+/**
+ * §F.5 false-positive guard threshold (mean retrieval_calls per task).
+ * If a Qwen candidate achieves trio_strict delta but stays below this floor,
+ * it is REJECTED as false-positive evolution per Amendment 2 §5.
+ */
+export const QWEN_FALSE_POSITIVE_RETRIEVAL_FLOOR = 1.5;
+
+/**
+ * Floating-point tolerance for threshold comparisons. 1e-9 is well below
+ * any signal magnitude in the +/-0.05 fitness band (which is itself ~9
+ * orders of magnitude larger). Required because IEEE 754 makes
+ * `(0.25 - 0.20) * 100 = 4.999999999999999` rather than exact 5.0.
+ */
+const EPSILON = 1e-9;
+
+/**
+ * Compute acceptance verdict for a single candidate per launch decision §F + §F.5.
+ *
+ * Caller is responsible for §F.2 (≥3/5 shapes positive delta), §F.3 (κ stability),
+ * and §F.4 (zero cell semantic violations) at the run-aggregate level.
+ */
+export function evaluateCandidate(inputs: AcceptanceInputs): AcceptanceVerdict {
+  const { candidate, baselineTrioStrictPassRateII } = inputs;
+
+  // Compute delta in percentage points (scale 0..100)
+  const trioStrictDeltaPP =
+    (candidate.trioStrictPassRateII - baselineTrioStrictPassRateII) * 100;
+
+  const isQwenTargeted = QWEN_TARGETED_SHAPES.has(candidate.shape);
+
+  // §F.5 false-positive guard — applies ONLY to Qwen-targeted shapes
+  // and ONLY when trio_strict delta meets the +5pp threshold.
+  // Per Amendment 2 §5: if delta ≥ +5pp AND retrieval_calls < 1.5 → REJECT.
+  // EPSILON tolerance handles IEEE 754 precision on exact-boundary deltas.
+  let condition5FalsePositiveGuardTriggered = false;
+  if (
+    isQwenTargeted &&
+    trioStrictDeltaPP >= TRIO_STRICT_DELTA_THRESHOLD_PP - EPSILON &&
+    candidate.meanRetrievalCallsPerTask < QWEN_FALSE_POSITIVE_RETRIEVAL_FLOOR - EPSILON
+  ) {
+    condition5FalsePositiveGuardTriggered = true;
+  }
+
+  // §F condition 1 — trio_strict delta + (Qwen only) retrieval engagement floor
+  let condition1Pass = trioStrictDeltaPP >= TRIO_STRICT_DELTA_THRESHOLD_PP - EPSILON;
+  if (condition1Pass && isQwenTargeted) {
+    // Qwen sub-criterion: mean retrieval_calls ≥ 1.7
+    if (candidate.meanRetrievalCallsPerTask < QWEN_RETRIEVAL_ENGAGEMENT_FLOOR - EPSILON) {
+      condition1Pass = false;
+    }
+  }
+
+  // Overall acceptance: condition 1 must pass AND §F.5 must not trigger
+  const accepted = condition1Pass && !condition5FalsePositiveGuardTriggered;
+
+  // Reason string for audit log
+  const reason = buildReason({
+    candidate,
+    trioStrictDeltaPP,
+    condition1Pass,
+    condition5FalsePositiveGuardTriggered,
+    isQwenTargeted,
+    accepted,
+  });
+
+  return {
+    condition1Pass,
+    condition5FalsePositiveGuardTriggered,
+    accepted,
+    reason,
+    trioStrictDeltaPP,
+  };
+}
+
+interface BuildReasonInputs {
+  candidate: { shape: string; meanRetrievalCallsPerTask: number };
+  trioStrictDeltaPP: number;
+  condition1Pass: boolean;
+  condition5FalsePositiveGuardTriggered: boolean;
+  isQwenTargeted: boolean;
+  accepted: boolean;
+}
+
+function buildReason(i: BuildReasonInputs): string {
+  const parts: string[] = [];
+  parts.push(`shape=${i.candidate.shape}`);
+  parts.push(`trio_strict_delta=${i.trioStrictDeltaPP.toFixed(2)}pp`);
+  parts.push(`mean_retrieval_calls=${i.candidate.meanRetrievalCallsPerTask.toFixed(2)}`);
+
+  if (i.condition5FalsePositiveGuardTriggered) {
+    parts.push(
+      `REJECTED §F.5 false-positive guard: delta ≥ +${TRIO_STRICT_DELTA_THRESHOLD_PP}pp AND retrieval < ${QWEN_FALSE_POSITIVE_RETRIEVAL_FLOOR}`,
+    );
+  } else if (!i.condition1Pass) {
+    // Use same EPSILON tolerance as condition1Pass evaluation to keep root-cause
+    // attribution consistent with the gating logic on exact-boundary deltas.
+    if (i.trioStrictDeltaPP < TRIO_STRICT_DELTA_THRESHOLD_PP - EPSILON) {
+      parts.push(
+        `FAIL §F.1 trio_strict delta: ${i.trioStrictDeltaPP.toFixed(2)}pp < ${TRIO_STRICT_DELTA_THRESHOLD_PP}pp`,
+      );
+    } else if (i.isQwenTargeted) {
+      parts.push(
+        `FAIL §F.1 Qwen retrieval floor: ${i.candidate.meanRetrievalCallsPerTask.toFixed(2)} < ${QWEN_RETRIEVAL_ENGAGEMENT_FLOOR}`,
+      );
+    }
+  } else {
+    parts.push(`PASS §F.1${i.isQwenTargeted ? ' (Qwen retrieval floor met)' : ''}`);
+  }
+
+  parts.push(`accepted=${i.accepted}`);
+  return parts.join(' | ');
+}

benchmarks/gepa/src/faza-1/corpus-prompt.ts

@@ -0,0 +1,114 @@
+/**
+ * GEPA Faza 1 — Opus 4.7 oracle prompt for synthesizing one H3 corpus instance.
+ *
+ * Per Amendment 1 Ask A Option C sub-ask 4 (instance generation methodology):
+ *   "Opus 4.7 generates task scaffold (persona + scenario + 6-7 source document specs);
+ *    PM does NOT review each instance pre-NULL-baseline. Instead: CC-2 spot-audits
+ *    5 random instances pre NULL-baseline kick (3% sample)."
+ *
+ * The oracle is instructed to produce a JSON object that maps directly onto
+ * the CorpusInstance shape (minus computed fields like materialsConcat, charCount).
+ */
+
+import { type StratificationCell, type CorpusInstance, TASK_FAMILY_DESCRIPTORS } from './corpus.js';
+
+/** Persona descriptor — short label + role context for prompt template. */
+const PERSONA_DESCRIPTORS: Record<string, { label: string; roleContext: string }> = {
+  p1_founder_ceo: {
+    label: 'Founder/CEO',
+    roleContext: 'company founder + chief executive; focused on growth, fundraising, vision',
+  },
+  p2_cfo: {
+    label: 'CFO',
+    roleContext: 'chief financial officer; focused on burn, runway, capital efficiency',
+  },
+  p3_coo: {
+    label: 'COO',
+    roleContext: 'chief operating officer; focused on operational efficiency, hiring, process',
+  },
+  p4_vp_finance: {
+    label: 'VP Finance',
+    roleContext: 'reports to CFO; focused on FP&A, financial reporting, budget management',
+  },
+  p5_independent_director: {
+    label: 'Independent Director',
+    roleContext: 'board-level oversight; focused on governance, strategic risk, executive accountability',
+  },
+};
+
+/** Company-stage descriptor — narrative framing for Opus to ground each instance. */
+const COMPANY_STAGE_DESCRIPTORS: Record<string, { label: string; financial: string; pressure: string }> = {
+  stage_a_series_b_growth_burning: {
+    label: 'Series B growth-stage, burning capital',
+    financial: 'recently raised Series B ~$30M; burning $1-1.5M/month; 12-18 months runway; ARR $10-20M',
+    pressure: 'investor pressure to demonstrate capital efficiency; competitive displacement risk; talent retention concerns',
+  },
+  stage_b_post_profitable_consolidation: {
+    label: 'Post-profitable, consolidation phase',
+    financial: 'reached profitability 2-4 quarters ago; $40-80M ARR; 15-25% operating margin; 24+ months runway',
+    pressure: 'Wall Street scrutiny on growth deceleration; M&A integration challenges; complacency risk',
+  },
+};
+
+export interface BuildCorpusInstancePromptInputs {
+  cell: StratificationCell;
+  instanceId: string;
+  /** Domain anchor — ensures all 50 instances stay in NorthLane CFO synthesis territory. */
+  domainAnchor?: string;
+}
+
+/** Build the Opus 4.7 generation prompt for one stratification cell. */
+export function buildCorpusInstancePrompt(inputs: BuildCorpusInstancePromptInputs): string {
+  const { cell, instanceId } = inputs;
+  const familyDesc = TASK_FAMILY_DESCRIPTORS[cell.family];
+  const personaDesc = PERSONA_DESCRIPTORS[cell.persona];
+  const stageDesc = COMPANY_STAGE_DESCRIPTORS[cell.stage];
+
+  return `You are generating one synthesis-task instance for a benchmark corpus.
+
+The corpus targets B2B SaaS knowledge work in the spirit of "NorthLane CFO" pilot tasks: a complex business situation requiring multi-document synthesis to produce a structured business deliverable (memo, action plan, decision recommendation).
+
+## Stratification cell for this instance
+- Instance ID: \`${instanceId}\`
+- Task family: \`${cell.family}\` — ${familyDesc.label}
+- Task family description: ${familyDesc.label.replace(/_/g, ' ')}; mirror pattern: ${familyDesc.mirrorPilotTask ?? 'NEW family (no pilot mirror)'}
+- Persona: \`${cell.persona}\` — ${personaDesc.label} (${personaDesc.roleContext})
+- Company stage: \`${cell.stage}\` — ${stageDesc.label}
+  - Financial state: ${stageDesc.financial}
+  - Pressure dynamics: ${stageDesc.pressure}
+
+## Required output (JSON object only, no prose, no code fences)
+
+\`\`\`json
+{
+  "personaText": "Persona: <2-3 sentence persona description grounding the model in role + company>\\n\\nScenario: <2-4 sentence scenario hook setting up why this synthesis is needed now>",
+  "sourceDocuments": [
+    { "title": "DOC 1 — <Doc name>", "body": "<markdown content, 600-1200 chars>" },
+    { "title": "DOC 2 — <Doc name>", "body": "<...>" },
+    ...
+    (exactly ${familyDesc.docsPerInstance} docs total)
+  ],
+  "question": "<open-ended question, 100-400 chars, that requires synthesizing across multiple docs to answer>"
+}
+\`\`\`
+
+## Generation constraints (binding)
+
+1. **Domain anchor:** All ${familyDesc.docsPerInstance} source documents must be plausible artifacts a ${personaDesc.label} would receive in a B2B SaaS company at the ${stageDesc.label} phase.
+
+2. **Document diversity:** Each doc should be a different artifact type (P&L summary, pipeline review, churn analysis, eng velocity report, marketing dashboard, board notes, competitor intel, customer interview notes, OKR report, runway model, etc.). No two docs should be the same artifact type.
+
+3. **Synthesis requirement:** The question must be answerable ONLY by triangulating across multiple docs. A single-doc answer should be insufficient. Specifically, the question should require:
+   - Identifying tradeoffs across docs
+   - Synthesizing competing positions
+   - Producing a structured deliverable (memo, action plan, or recommendation with justification)
+
+4. **Persona consistency:** The persona's role + company-stage pressures must be evident in BOTH the scenario framing AND the question phrasing. A founder-CEO question reads differently than an independent-director question.
+
+5. **Doc body realism:** Use realistic numbers (revenue figures, percentages, dates). Cite specific people by role title (e.g., "VP Sales", not "John Smith"). Include both hard data + qualitative commentary in each doc.
+
+6. **JSON format:** Output ONLY a single valid JSON object. No prose before or after. No code fence markers. The first character must be \`{\` and the last character must be \`}\`.
+
+## Output begins below
+`;
+}

benchmarks/gepa/src/faza-1/corpus.ts

@@ -0,0 +1,311 @@
+/**
+ * GEPA Faza 1 — H3 corpus data definitions, stratification, validation,
+ * and spot-audit sampler.
+ *
+ * Per Amendment 1 Ask A Option C + manifest v7 §corpus_design:
+ *   - 50 instances total
+ *   - 5 task families × 5 personas × 2 company stages = 50 cells
+ *   - NorthLane CFO synthesis domain (preserves Phase 4.3 anchor)
+ *   - Each instance: ≥6 source documents, 6-dim Likert rubric
+ *   - Stratified sampling: deterministic, seed=42
+ *   - Generated via Opus 4.7 oracle
+ *
+ * Pre-A halt-and-PM checkpoint: spot-audit 5 random instances.
+ */
+
+import * as crypto from 'node:crypto';
+
+/** 5 task families per manifest v7 §corpus_design.task_families. */
+export type TaskFamily = 'F1' | 'F2' | 'F3' | 'F4' | 'F5';
+
+/** 5 persona codes per manifest v7 §corpus_design.persona_axis. */
+export type PersonaCode = 'p1_founder_ceo' | 'p2_cfo' | 'p3_coo' | 'p4_vp_finance' | 'p5_independent_director';
+
+/** 2 company stages per manifest v7 §corpus_design.company_stage_axis. */
+export type CompanyStage = 'stage_a_series_b_growth_burning' | 'stage_b_post_profitable_consolidation';
+
+/** All 5 task families in canonical order. */
+export const TASK_FAMILIES: ReadonlyArray<TaskFamily> = ['F1', 'F2', 'F3', 'F4', 'F5'];
+
+/** All 5 personas in canonical order. */
+export const PERSONAS: ReadonlyArray<PersonaCode> = [
+  'p1_founder_ceo',
+  'p2_cfo',
+  'p3_coo',
+  'p4_vp_finance',
+  'p5_independent_director',
+];
+
+/** All 2 company stages in canonical order. */
+export const COMPANY_STAGES: ReadonlyArray<CompanyStage> = [
+  'stage_a_series_b_growth_burning',
+  'stage_b_post_profitable_consolidation',
+];
+
+/** Total instances per manifest v7 §corpus_design.total_instances. */
+export const TOTAL_INSTANCES = 50;
+
+/** Required source document count floor per manifest v7. */
+export const DOCS_PER_INSTANCE_MIN = 6;
+
+/** Required source document count ceiling per manifest v7. */
+export const DOCS_PER_INSTANCE_MAX = 8;
+
+/** Spot-audit sample size per manifest v7 §corpus_design.spot_audit. */
+export const SPOT_AUDIT_SAMPLE_SIZE = 5;
+
+/** Deterministic stratification seed per manifest v7 §corpus_design.stratified_sampling.seed. */
+export const STRATIFICATION_SEED = 42;
+
+/** Per-instance human-readable task family descriptor. */
+export const TASK_FAMILY_DESCRIPTORS: Record<TaskFamily, {
+  label: string;
+  mirrorPilotTask: string | null;
+  docsPerInstance: number;
+  promptTemplate: string;
+}> = {
+  F1: {
+    label: 'strategic_synthesis',
+    mirrorPilotTask: 'task-1',
+    docsPerInstance: 7,
+    promptTemplate: 'Identify the 3 most critical risks for {company} in {period} and propose action plan for each.',
+  },
+  F2: {
+    label: 'cross_thread_coordination',
+    mirrorPilotTask: 'task-2',
+    docsPerInstance: 6,
+    promptTemplate: 'Reconcile conflicting positions from {stakeholders} and propose unified approach.',
+  },
+  F3: {
+    label: 'decision_support',
+    mirrorPilotTask: 'task-3',
+    docsPerInstance: 6,
+    promptTemplate: 'Recommend {decision} based on materials; justify, address counter-arguments.',
+  },
+  F4: {
+    label: 'investor_communications',
+    mirrorPilotTask: null,  // NEW family
+    docsPerInstance: 6,
+    promptTemplate: 'Draft Q{n} investor update covering {metrics} + addressing {concerns}.',
+  },
+  F5: {
+    label: 'scenario_planning',
+    mirrorPilotTask: null,  // NEW family
+    docsPerInstance: 7,
+    promptTemplate: 'Compare {n} scenarios for {decision_area}; recommend hedging strategy.',
+  },
+};
+
+/** Stratification cell — one of 50 unique combinations. */
+export interface StratificationCell {
+  family: TaskFamily;
+  persona: PersonaCode;
+  stage: CompanyStage;
+}
+
+/** A single source document inside a corpus instance. */
+export interface SourceDoc {
+  title: string;
+  body: string;
+  charCount: number;
+}
+
+/** A single corpus instance ready for evaluation by NULL-baseline / GEPA candidates. */
+export interface CorpusInstance {
+  /** Stable instance ID: h3-{family}-{persona}-{stage}-{ordinal}. */
+  instanceId: string;
+  cell: StratificationCell;
+  personaText: string;
+  scenario: string;
+  sourceDocuments: SourceDoc[];
+  question: string;
+  /** Aggregated materials block (joined source docs) — consumer convenience. */
+  materialsConcat: string;
+  manifestAnchor: string;
+  generatedBy: string;
+  generatedAtIso: string;
+  generationCostUsd: number;
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// Stratification — deterministic enumeration of 50 cells
+// ───────────────────────────────────────────────────────────────────────────
+
+/**
+ * Enumerate all 50 stratification cells in canonical (family-major, persona-mid,
+ * stage-minor) order. Deterministic given the constant arrays above.
+ *
+ * Yield order: F1×p1×stage_a, F1×p1×stage_b, F1×p2×stage_a, ..., F5×p5×stage_b.
+ */
+export function* iterateStratificationCells(): Generator<StratificationCell> {
+  for (const family of TASK_FAMILIES) {
+    for (const persona of PERSONAS) {
+      for (const stage of COMPANY_STAGES) {
+        yield { family, persona, stage };
+      }
+    }
+  }
+}
+
+/** Materialize all 50 cells as an array. */
+export function listStratificationCells(): StratificationCell[] {
+  return Array.from(iterateStratificationCells());
+}
+
+/** Build a stable instance ID from a cell + ordinal (1-based within cell). */
+export function buildInstanceId(cell: StratificationCell, ordinal: number = 1): string {
+  return `h3-${cell.family}-${cell.persona}-${cell.stage}-${String(ordinal).padStart(3, '0')}`;
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// Validation — check instance shape against manifest v7 quality floor
+// ───────────────────────────────────────────────────────────────────────────
+
+export interface InstanceValidationResult {
+  valid: boolean;
+  violations: string[];
+}
+
+/** Per manifest v7 §corpus_design.per_instance_quality_floor + spot_audit dimensions. */
+export function validateInstance(instance: CorpusInstance): InstanceValidationResult {
+  const violations: string[] = [];
+
+  if (instance.sourceDocuments.length < DOCS_PER_INSTANCE_MIN) {
+    violations.push(`docs count ${instance.sourceDocuments.length} < ${DOCS_PER_INSTANCE_MIN} min`);
+  }
+  if (instance.sourceDocuments.length > DOCS_PER_INSTANCE_MAX) {
+    violations.push(`docs count ${instance.sourceDocuments.length} > ${DOCS_PER_INSTANCE_MAX} max`);
+  }
+
+  // Length bounds loosened post-probe (2026-04-28) — Opus generates naturally
+  // richer personas/scenarios than the hand-crafted pilot baseline. The intent
+  // of these bounds is to catch broken/empty output, not to police verbosity.
+  const personaLen = instance.personaText.length;
+  if (personaLen < 100 || personaLen > 1500) {
+    violations.push(`persona length ${personaLen} outside [100, 1500] range`);
+  }
+
+  // Scenario may be empty if oracle embedded it in personaText (handled by
+  // assembleInstance — extractScenarioFromPersonaText). Skip length floor in
+  // that case but still cap upper bound.
+  const scenarioLen = instance.scenario.length;
+  if (scenarioLen > 0 && (scenarioLen < 100 || scenarioLen > 1500)) {
+    violations.push(`scenario length ${scenarioLen} outside [100, 1500] range (or 0 if embedded in personaText)`);
+  }
+
+  const questionLen = instance.question.length;
+  if (questionLen < 100 || questionLen > 800) {
+    violations.push(`question length ${questionLen} outside [100, 800] range`);
+  }
+
+  for (const doc of instance.sourceDocuments) {
+    if (doc.body.length < 400 || doc.body.length > 3000) {
+      violations.push(`doc "${doc.title}" body length ${doc.body.length} outside [400, 3000] range`);
+    }
+    if (doc.body.length !== doc.charCount) {
+      violations.push(`doc "${doc.title}" charCount ${doc.charCount} != actual body length ${doc.body.length}`);
+    }
+  }
+
+  if (!instance.instanceId.startsWith('h3-')) {
+    violations.push(`instanceId "${instance.instanceId}" missing h3- prefix`);
+  }
+
+  return {
+    valid: violations.length === 0,
+    violations,
+  };
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// Spot-audit sampler — deterministic random selection of N instances
+// ───────────────────────────────────────────────────────────────────────────
+
+/**
+ * Mulberry32 PRNG — deterministic, fast, 32-bit. Same seed produces same
+ * output across runs and platforms.
+ */
+function mulberry32(seed: number): () => number {
+  let t = seed >>> 0;
+  return function () {
+    t = (t + 0x6d2b79f5) >>> 0;
+    let r = t;
+    r = Math.imul(r ^ (r >>> 15), r | 1);
+    r ^= r + Math.imul(r ^ (r >>> 7), r | 61);
+    return ((r ^ (r >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+/**
+ * Deterministic Fisher-Yates shuffle producing a sample of `sampleSize` from
+ * the input array, seeded by `seed`. Reproducible across runs.
+ */
+export function deterministicSample<T>(
+  items: ReadonlyArray<T>,
+  sampleSize: number,
+  seed: number = STRATIFICATION_SEED,
+): T[] {
+  if (sampleSize >= items.length) {
+    return [...items];
+  }
+  const arr = [...items];
+  const rand = mulberry32(seed);
+  // Partial Fisher-Yates: only need first `sampleSize` swapped to front.
+  for (let i = 0; i < sampleSize; i++) {
+    const j = i + Math.floor(rand() * (arr.length - i));
+    [arr[i], arr[j]] = [arr[j], arr[i]];
+  }
+  return arr.slice(0, sampleSize);
+}
+
+/** Select 5 random instances per manifest v7 §corpus_design.spot_audit. */
+export function selectSpotAuditSample(instances: ReadonlyArray<CorpusInstance>): CorpusInstance[] {
+  return deterministicSample(instances, SPOT_AUDIT_SAMPLE_SIZE);
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// Aggregate spot-audit verdict
+// ───────────────────────────────────────────────────────────────────────────
+
+export interface SpotAuditReport {
+  sampleSize: number;
+  perInstance: Array<{ instanceId: string; result: InstanceValidationResult }>;
+  /** Per manifest v7 §corpus_design.spot_audit.halt_on: any 1 of 5 fails → corpus regeneration. */
+  haltOnFailure: boolean;
+  haltReason?: string;
+}
+
+/** Run validation across spot-audit sample + return aggregate verdict. */
+export function runSpotAudit(instances: ReadonlyArray<CorpusInstance>): SpotAuditReport {
+  const sample = selectSpotAuditSample(instances);
+  const perInstance = sample.map(inst => ({
+    instanceId: inst.instanceId,
+    result: validateInstance(inst),
+  }));
+  const failed = perInstance.filter(p => !p.result.valid);
+  return {
+    sampleSize: sample.length,
+    perInstance,
+    haltOnFailure: failed.length > 0,
+    haltReason: failed.length > 0
+      ? `${failed.length}/${sample.length} spot-audit instances failed validation: ${failed.map(f => f.instanceId).join(', ')}`
+      : undefined,
+  };
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// Audit-chain helpers
+// ───────────────────────────────────────────────────────────────────────────
+
+/** Compute SHA-256 of a JSON-serialized corpus (for manifest v7 audit pinning). */
+export function corpusSha256(instances: ReadonlyArray<CorpusInstance>): string {
+  const canonical = instances.map(inst => ({
+    instanceId: inst.instanceId,
+    cell: inst.cell,
+    materialsLength: inst.materialsConcat.length,
+    questionLength: inst.question.length,
+    docCount: inst.sourceDocuments.length,
+  }));
+  const json = JSON.stringify(canonical);
+  return crypto.createHash('sha256').update(json).digest('hex');
+}

benchmarks/gepa/src/faza-1/cost-tracker.ts

@@ -0,0 +1,133 @@
+/**
+ * GEPA Faza 1 — cost governance + super-linear projection tracker.
+ *
+ * Per launch decision §A.7 + manifest v7 §cost_governance.
+ *
+ * Halt triggers (any single triggers immediate halt + PM ratify):
+ *   - Cumulative spend > $80 (internal halt)
+ *   - Cumulative spend > $100 (hard cap)
+ *   - Mid-run actual exceeds projection by >30% (super-linear sub-rule per brief §6.7)
+ *
+ * Audit cadence: every 20 evaluations.
+ *
+ * Cost projection methodology: 1.5× baseline token count per candidate
+ * (encodes mutation overhead since GEPA candidates may grow prompts).
+ */
+
+/** Hard cap (immediate halt on breach). */
+export const HARD_CAP_USD = 100.00;
+
+/** Internal halt threshold (halt + PM ratify before proceeding). */
+export const INTERNAL_HALT_USD = 80.00;
+
+/** Super-linear projection multiplier per brief §6.7. */
+export const SUPER_LINEAR_MULTIPLIER = 1.5;
+
+/** Mid-run halt threshold: actual exceeds projection by this fraction. */
+export const SUPER_LINEAR_OVERAGE_THRESHOLD = 0.30;
+
+/** Audit cadence (every N evaluations). */
+export const AUDIT_CADENCE_EVAL_COUNT = 20;
+
+export type HaltReason =
+  | 'NONE'
+  | 'INTERNAL_HALT_USD_BREACH'
+  | 'HARD_CAP_USD_BREACH'
+  | 'SUPER_LINEAR_PROJECTION_BREACH';
+
+export interface CostTrackerState {
+  cumulativeUsd: number;
+  evaluationCount: number;
+  /** Per-evaluation projection used for super-linear check (1.5× baseline median). */
+  projectionPerEvalUsd: number;
+}
+
+export interface HaltCheckResult {
+  haltReason: HaltReason;
+  cumulativeUsd: number;
+  expectedAtThisCount: number;
+  overageFraction: number;
+  message: string;
+}
+
+/** Create a new cost tracker state with the per-evaluation projection. */
+export function createCostTracker(baselineMedianCostPerEvalUsd: number): CostTrackerState {
+  return {
+    cumulativeUsd: 0,
+    evaluationCount: 0,
+    projectionPerEvalUsd: baselineMedianCostPerEvalUsd * SUPER_LINEAR_MULTIPLIER,
+  };
+}
+
+/**
+ * Record an evaluation cost and return updated state (immutable update).
+ * Per coding-style.md: never mutate, always return new copy.
+ */
+export function recordEvaluation(state: CostTrackerState, evalCostUsd: number): CostTrackerState {
+  return {
+    ...state,
+    cumulativeUsd: state.cumulativeUsd + evalCostUsd,
+    evaluationCount: state.evaluationCount + 1,
+  };
+}
+
+/**
+ * Check halt triggers against current state.
+ *
+ * Returns NONE if all checks pass; otherwise returns the first triggered
+ * halt reason with diagnostic context.
+ *
+ * Order of precedence (most severe first):
+ *   1. HARD_CAP_USD_BREACH ($100 breach)
+ *   2. INTERNAL_HALT_USD_BREACH ($80 breach)
+ *   3. SUPER_LINEAR_PROJECTION_BREACH (actual > 1.30 × expected at current eval count)
+ */
+export function checkHaltTriggers(state: CostTrackerState): HaltCheckResult {
+  const expectedAtThisCount = state.projectionPerEvalUsd * state.evaluationCount;
+  const overageFraction =
+    expectedAtThisCount > 0 ? (state.cumulativeUsd - expectedAtThisCount) / expectedAtThisCount : 0;
+
+  if (state.cumulativeUsd > HARD_CAP_USD) {
+    return {
+      haltReason: 'HARD_CAP_USD_BREACH',
+      cumulativeUsd: state.cumulativeUsd,
+      expectedAtThisCount,
+      overageFraction,
+      message: `HARD CAP BREACH: $${state.cumulativeUsd.toFixed(2)} > $${HARD_CAP_USD} cap`,
+    };
+  }
+
+  if (state.cumulativeUsd > INTERNAL_HALT_USD) {
+    return {
+      haltReason: 'INTERNAL_HALT_USD_BREACH',
+      cumulativeUsd: state.cumulativeUsd,
+      expectedAtThisCount,
+      overageFraction,
+      message: `INTERNAL HALT: $${state.cumulativeUsd.toFixed(2)} > $${INTERNAL_HALT_USD} internal halt — PM ratify before proceeding`,
+    };
+  }
+
+  // Super-linear check requires at least 1 eval to have meaningful expected value.
+  if (state.evaluationCount > 0 && overageFraction > SUPER_LINEAR_OVERAGE_THRESHOLD) {
+    return {
+      haltReason: 'SUPER_LINEAR_PROJECTION_BREACH',
+      cumulativeUsd: state.cumulativeUsd,
+      expectedAtThisCount,
+      overageFraction,
+      message: `SUPER-LINEAR BREACH: actual $${state.cumulativeUsd.toFixed(2)} exceeds expected $${expectedAtThisCount.toFixed(2)} by ${(overageFraction * 100).toFixed(1)}% (>${(SUPER_LINEAR_OVERAGE_THRESHOLD * 100).toFixed(0)}% threshold)`,
+    };
+  }
+
+  return {
+    haltReason: 'NONE',
+    cumulativeUsd: state.cumulativeUsd,
+    expectedAtThisCount,
+    overageFraction,
+    message: `OK: $${state.cumulativeUsd.toFixed(2)} cumulative; expected $${expectedAtThisCount.toFixed(2)}; ${(overageFraction * 100).toFixed(1)}% overage`,
+  };
+}
+
+/** Whether the current eval count is on an audit cadence boundary. */
+export function shouldAudit(state: CostTrackerState): boolean {
+  return state.evaluationCount > 0 && state.evaluationCount % AUDIT_CADENCE_EVAL_COUNT === 0;
+}

benchmarks/gepa/src/faza-1/fitness.ts

@@ -0,0 +1,298 @@
+/**
+ * GEPA Faza 1 — per-shape fitness function.
+ *
+ * Per Amendment 2 §3 (manifest v7 §metric_operationalization.per_shape_fitness_formula
+ * + §metric_operationalization.retrieval_engagement_bonus).
+ *
+ * Phase 4.5 empirical signal: Qwen retrieves 1.33×/task vs Opus 2.33×/task.
+ * H4 score gap mechanistically traces to under-engagement. Mutation surface
+ * (prompt-shape body) is the lever to address this on Qwen-targeted shapes.
+ *
+ * Fitness function forks by shape class:
+ *   Qwen-targeted: fitness = trio_strict_pass_rate + retrieval_engagement_bonus − cost_penalty
+ *   Non-Qwen:      fitness = trio_strict_pass_rate − cost_penalty
+ *
+ * Retrieval engagement bonus bands (Qwen-targeted shapes only):
+ *   +0.05 if mean retrieval_calls per task ≥ 2.0   (Opus parity proxy)
+ *    0.00 if mean retrieval_calls per task ∈ [1.5, 2.0)
+ *   −0.05 if mean retrieval_calls per task < 1.5   (Qwen baseline penalty)
+ */
+
+import {
+  type FitnessInputs,
+  type FitnessComponents,
+  type ShapeName,
+  type TieredFitnessInputs,
+  type TieredFitnessComponents,
+  type DeltaFloorInputs,
+  type DeltaFloorVerdict,
+  QWEN_TARGETED_SHAPES,
+} from './types.js';
+
+/** Cost penalty coefficient per brief §3.1 — 0.5pp per $0.10 above baseline median. */
+const COST_PENALTY_PP_PER_DOLLAR_10C = 0.5;
+
+/** Retrieval engagement thresholds per Amendment 2 §3 bands. */
+export const RETRIEVAL_ENGAGEMENT_BANDS = {
+  /** ≥ this → +0.05 bonus (Opus parity proxy). */
+  upperThreshold: 2.0,
+  /** ≥ this and < upper → 0.00. */
+  lowerThreshold: 1.5,
+  /** < lower → −0.05 (Qwen baseline penalty). */
+  bonusPlus: 0.05,
+  bonusZero: 0.0,
+  bonusMinus: -0.05,
+} as const;
+
+/**
+ * Compute the retrieval engagement bonus for a Qwen-targeted shape.
+ *
+ * Returns 0.0 for non-Qwen shapes (callers should branch on shape class
+ * before calling this if they need to distinguish; alternatively use
+ * `computeFitness` which handles routing).
+ *
+ * Boundary semantics (binding per Amendment 2 §8 test cases):
+ *   1.49 → −0.05
+ *   1.50 →  0.00  (exact lower threshold = zero band)
+ *   1.99 →  0.00
+ *   2.00 → +0.05  (exact upper threshold = bonus band)
+ *   2.50 → +0.05
+ */
+export function computeRetrievalEngagementBonus(
+  shape: ShapeName,
+  meanRetrievalCallsPerTask: number,
+): number {
+  if (!QWEN_TARGETED_SHAPES.has(shape)) {
+    return 0.0;
+  }
+  if (meanRetrievalCallsPerTask >= RETRIEVAL_ENGAGEMENT_BANDS.upperThreshold) {
+    return RETRIEVAL_ENGAGEMENT_BANDS.bonusPlus;
+  }
+  if (meanRetrievalCallsPerTask >= RETRIEVAL_ENGAGEMENT_BANDS.lowerThreshold) {
+    return RETRIEVAL_ENGAGEMENT_BANDS.bonusZero;
+  }
+  return RETRIEVAL_ENGAGEMENT_BANDS.bonusMinus;
+}
+
+/**
+ * Compute the cost penalty per brief §3.1.
+ *
+ * If the candidate's mean cost is at or below the per-shape baseline median,
+ * penalty is 0. Otherwise, penalty = 0.5pp per $0.10 of overage, encoded as
+ * a positive decimal (caller subtracts from fitness).
+ */
+export function computeCostPenalty(
+  candidateMeanCostUsd: number,
+  baselineMedianCostUsd: number,
+): number {
+  const overageUsd = candidateMeanCostUsd - baselineMedianCostUsd;
+  if (overageUsd <= 0) {
+    return 0.0;
+  }
+  // 0.5 pp per $0.10 → 5 pp per $1.00 → encoded as 0.05 per $1.00 → 0.005 per $0.10
+  const penaltyDecimal = (overageUsd / 0.10) * (COST_PENALTY_PP_PER_DOLLAR_10C / 100);
+  return penaltyDecimal;
+}
+
+/**
+ * Compute the per-shape fitness for a candidate.
+ *
+ * Routes by shape class:
+ *   - Qwen-targeted (qwen-thinking, qwen-non-thinking): includes retrieval engagement bonus
+ *   - Non-Qwen (claude, gpt, generic-simple): no retrieval engagement weighting
+ *
+ * Returns full FitnessComponents for downstream audit + reporting per
+ * launch decision §A.9 binding compliance requirement.
+ */
+export function computeFitness(inputs: FitnessInputs): FitnessComponents {
+  const { candidate, baselineMedianCostUsd } = inputs;
+
+  const trioStrictPassRateII = candidate.trioStrictPassRateII;
+
+  const retrievalEngagementApplied = QWEN_TARGETED_SHAPES.has(candidate.shape);
+  const retrievalEngagementBonus = retrievalEngagementApplied
+    ? computeRetrievalEngagementBonus(candidate.shape, candidate.meanRetrievalCallsPerTask)
+    : 0.0;
+
+  const costPenalty = computeCostPenalty(candidate.meanCostUsd, baselineMedianCostUsd);
+
+  const fitness = trioStrictPassRateII + retrievalEngagementBonus - costPenalty;
+
+  return {
+    trioStrictPassRateII,
+    retrievalEngagementBonus,
+    costPenalty,
+    fitness,
+    retrievalEngagementApplied,
+  };
+}
+
+// ── Amendment 7 — tiered fitness function ─────────────────────────────────
+
+/** Tier 2 cap per Amendment 7 §fitness_function_tiered.tier_2 (max bonus). */
+export const TIER_2_BONUS_CAP = 0.25;
+/** Tier 2 weight per percentage point of retrieval engagement above NULL baseline. */
+export const TIER_2_BONUS_PER_PP = 0.05;
+/** Tier 3 binary bonus when all 7 cell-semantic anchors invariant. */
+export const TIER_3_BONUS_FULL_INVARIANCE = 0.10;
+/** Total cell-semantic anchor count (types.ts + MULTI_STEP_ACTION_CONTRACT + 5 baseline shapes). */
+export const TIER_3_ANCHOR_COUNT_FULL = 7;
+
+/**
+ * Compute Tier 2 retrieval engagement bonus per Amendment 7 §fitness_function_tiered.tier_2.
+ *
+ * Continuous formula (supersedes Amendment 2 band bonus for tiered ranking):
+ *   bonus = clamp(0.05 × delta_pp, 0, 0.25)
+ *   where delta_pp = (candidate_mean - baseline_mean) × 100
+ *
+ * Cap reached at +5pp absolute increase in mean retrieval calls per task.
+ * Floor 0 (no negative bonus from Tier 2 — negative-band penalty handled
+ * by Amendment 2 §F.5 false-positive guard separately).
+ *
+ * Always returns 0 for non-Qwen shapes.
+ */
+export function computeTier2RetrievalBonus(
+  shape: ShapeName,
+  candidateMeanRetrievalCallsPerTask: number,
+  baselineMeanRetrievalCallsPerTask: number,
+): number {
+  if (!QWEN_TARGETED_SHAPES.has(shape)) return 0;
+  const deltaAbsolute = candidateMeanRetrievalCallsPerTask - baselineMeanRetrievalCallsPerTask;
+  if (deltaAbsolute <= 0) return 0;
+  const deltaPP = deltaAbsolute * 100;
+  return Math.min(TIER_2_BONUS_PER_PP * deltaPP, TIER_2_BONUS_CAP);
+}
+
+/**
+ * Compute the per-shape tiered fitness per Amendment 7 §fitness_function_tiered.
+ *
+ * Routes tier roles by saturation:
+ *   - In saturated regime (≥4/5 shapes have NULL pass rate ≥75%):
+ *       PRIMARY differentiator = Tier 2 (Qwen-targeted retrieval engagement)
+ *       SECONDARY differentiator = Tier 3 (cell-semantic anchor invariance)
+ *       TIE-BREAKER = Tier 1 (NULL pass rate delta)
+ *       aggregateSaturatedRegime = tier_2 + tier_3
+ *   - Outside saturated regime: legacy Amendment 2 form (computeFitness) applies
+ *     and aggregateSaturatedRegime is reported but not the canonical fitness.
+ *
+ * Tier 1 also serves as the §F.1 acceptance gate (≥+5pp) — UNCHANGED from
+ * Amendment 5 launch decision §F.1.
+ */
+export function computeTieredFitness(inputs: TieredFitnessInputs): TieredFitnessComponents {
+  const {
+    candidate,
+    nullBaselinePassRateII,
+    nullBaselineMeanRetrievalCallsPerTask,
+    mutationValidatorPassed,
+    saturatedRegime,
+  } = inputs;
+
+  // Tier 1 — NULL pass rate delta (signed pp)
+  const tier1DeltaPP = (candidate.trioStrictPassRateII - nullBaselinePassRateII) * 100;
+
+  // Tier 2 — continuous retrieval engagement bonus (Qwen-targeted only)
+  const tier2RetrievalBonus = computeTier2RetrievalBonus(
+    candidate.shape,
+    candidate.meanRetrievalCallsPerTask,
+    nullBaselineMeanRetrievalCallsPerTask,
+  );
+
+  // Tier 3 — binary cell-semantic anchor invariance bonus
+  const tier3CellSemanticInvarianceBonus = mutationValidatorPassed
+    ? TIER_3_BONUS_FULL_INVARIANCE
+    : 0;
+  // For Gen 1 candidates the mutation_validator gives binary (valid|invalid);
+  // count semantic: pass = full 7-anchor invariance, fail = 0.
+  const cellSemanticAnchorInvarianceCount = mutationValidatorPassed ? TIER_3_ANCHOR_COUNT_FULL : 0;
+
+  // Aggregate fitness in saturated regime: tier_2 + tier_3 only
+  const aggregateSaturatedRegime = tier2RetrievalBonus + tier3CellSemanticInvarianceBonus;
+
+  return {
+    tier1DeltaPP,
+    tier2RetrievalBonus,
+    tier3CellSemanticInvarianceBonus,
+    aggregateSaturatedRegime,
+    saturatedRegimeApplied: saturatedRegime,
+    cellSemanticAnchorInvarianceCount,
+  };
+}
+
+// ── Amendment 7 — pre-registered Δ-floor verdict ──────────────────────────
+
+/** Δ-floor thresholds per Amendment 7 §gen_1_pre_registered_delta_floor. */
+export const DELTA_FLOOR_THRESHOLDS = {
+  /** Threshold 1: aggregate Tier 1 delta in pp (≥+3pp passes). */
+  threshold1AggregateTier1PP: 3,
+  /** Threshold 2: max Qwen retrieval engagement delta absolute (≥+0.10 passes). */
+  threshold2QwenRetrievalAbsolute: 0.10,
+  /** Threshold 3a: aggregate Tier 1 delta in pp (≥0pp). */
+  threshold3Tier1MinPP: 0,
+  /** Threshold 3b: aggregate Tier 2 bonus across Qwen-targeted candidates (≥0.05). */
+  threshold3Tier2MinBonus: 0.05,
+} as const;
+
+/** Float tolerance for exact-boundary threshold comparisons (consistent with acceptance.ts EPSILON). */
+const DELTA_FLOOR_EPSILON = 1e-9;
+
+/**
+ * Compute the Δ-floor verdict per Amendment 7 §gen_1_pre_registered_delta_floor.
+ *
+ * Three OR-gated thresholds:
+ *   1. aggregate Tier 1 delta ≥ +3pp absolute (loosened from §F.1 ≥+5pp)
+ *   2. max Qwen-shape retrieval engagement delta ≥ +0.10 absolute above per-shape NULL baseline
+ *   3. (aggregate Tier 1 delta ≥ 0pp) AND (aggregate Tier 2 bonus ≥ 0.05)
+ *
+ * If ANY ONE passes → PROCEED (continue past Checkpoint B subject to PM ratify).
+ * If ALL THREE fail → HALT_INVESTIGATE (file Investigate report).
+ *
+ * Per Amendment 7 §3.2: "If Gen 1 fails ALL three thresholds → HALT before Gen 2,
+ * file Investigate report. If Gen 1 passes any one → proceed to Gen 2."
+ */
+export function computeDeltaFloorVerdict(inputs: DeltaFloorInputs): DeltaFloorVerdict {
+  // Threshold 1 — aggregate Tier 1 delta
+  const threshold1ValuePP =
+    (inputs.aggregateTrioStrictPassRateII - inputs.aggregateNullBaselinePassRateII) * 100;
+  const threshold1Pass =
+    threshold1ValuePP >= DELTA_FLOOR_THRESHOLDS.threshold1AggregateTier1PP - DELTA_FLOOR_EPSILON;
+
+  // Threshold 2 — max Qwen retrieval engagement delta absolute
+  let threshold2MaxDeltaAbsolute: number | null = null;
+  for (const [shapeKey, candidateMean] of Object.entries(inputs.qwenShapeRetrievalMeans)) {
+    if (candidateMean === undefined) continue;
+    const baselineMean = inputs.qwenShapeNullBaselineRetrievalMeans[shapeKey as ShapeName];
+    if (baselineMean === undefined) continue;
+    const delta = candidateMean - baselineMean;
+    if (threshold2MaxDeltaAbsolute === null || delta > threshold2MaxDeltaAbsolute) {
+      threshold2MaxDeltaAbsolute = delta;
+    }
+  }
+  // No Qwen data: report 0 delta (informative neutral); threshold2 fails since 0 < 0.10
+  if (threshold2MaxDeltaAbsolute === null) threshold2MaxDeltaAbsolute = 0;
+  const threshold2Pass =
+    threshold2MaxDeltaAbsolute >=
+    DELTA_FLOOR_THRESHOLDS.threshold2QwenRetrievalAbsolute - DELTA_FLOOR_EPSILON;
+
+  // Threshold 3 — compound Tier 1 ≥0 AND Tier 2 ≥0.05
+  const threshold3Tier1ValuePP = threshold1ValuePP;
+  const threshold3Tier1Pass =
+    threshold3Tier1ValuePP >= DELTA_FLOOR_THRESHOLDS.threshold3Tier1MinPP - DELTA_FLOOR_EPSILON;
+  const threshold3Tier2Pass =
+    inputs.qwenAggregateTier2Bonus >=
+    DELTA_FLOOR_THRESHOLDS.threshold3Tier2MinBonus - DELTA_FLOOR_EPSILON;
+  const threshold3Pass = threshold3Tier1Pass && threshold3Tier2Pass;
+
+  const overallVerdict: 'PROCEED' | 'HALT_INVESTIGATE' =
+    threshold1Pass || threshold2Pass || threshold3Pass ? 'PROCEED' : 'HALT_INVESTIGATE';
+
+  return {
+    threshold1AggregateTier1: threshold1Pass ? 'PASS' : 'FAIL',
+    threshold1ValuePP,
+    threshold2QwenRetrievalAbsolute: threshold2Pass ? 'PASS' : 'FAIL',
+    threshold2MaxDeltaAbsolute,
+    threshold3CompoundTier1PlusTier2: threshold3Pass ? 'PASS' : 'FAIL',
+    threshold3Tier1ValuePP,
+    threshold3Tier2Aggregate: inputs.qwenAggregateTier2Bonus,
+    overallVerdict,
+  };
+}

benchmarks/gepa/src/faza-1/index.ts

@@ -0,0 +1,24 @@
+/**
+ * GEPA Faza 1 — public API surface.
+ *
+ * Per launch decision §G + manifest v7 §gepa.
+ *
+ * Module map:
+ *   - types          shared types + shape-class partitions
+ *   - fitness        per-shape fitness function (Amendment 2 §3 fork)
+ *   - acceptance     §F + §F.5 verdict per candidate
+ *   - mutation-validator   cell-semantic preservation audit (boundary SHAs)
+ *   - kappa-audit    drift band detection vs canonical 0.7878 ± 0.05
+ *   - cost-tracker   super-linear governance + halt triggers
+ *   - selection      top-1-per-shape + run-aggregate verdict
+ *   - mutation-oracle-fork   Qwen vs non-Qwen template routing (Amendment 2 §4)
+ */
+
+export * from './types.js';
+export * from './fitness.js';
+export * from './acceptance.js';
+export * from './mutation-validator.js';
+export * from './kappa-audit.js';
+export * from './cost-tracker.js';
+export * from './selection.js';
+export * from './mutation-oracle-fork.js';

benchmarks/gepa/src/faza-1/kappa-audit.ts

@@ -0,0 +1,141 @@
+/**
+ * GEPA Faza 1 — κ drift audit utility.
+ *
+ * Per launch decision §F.3 + manifest v7 §canonical_kappa_anchor +
+ * §faza_1_acceptance.condition_3.
+ *
+ * Canonical κ value: 0.7877758913412564 (rounds to 0.7878).
+ * Source: benchmarks/calibration/v6-kappa-recal/_summary-v6-kappa.json
+ *         (SHA 657d4490bab28d35cf8a9c3ccea8a6b79e92835d700155184e51f3900836684c)
+ * Drift threshold: ±0.05 (drift band [0.7378, 0.8378])
+ *
+ * Per κ_recalibration_v6 success_criteria + monitoring_runtime in manifest v6 §5.4:
+ *   - PASS: κ_trio ≥ 0.70
+ *   - BORDERLINE: κ_trio ∈ [0.60, 0.70]
+ *   - FAIL: κ_trio < 0.60
+ *
+ * Faza 1-specific drift acceptance (tighter than v6 baseline policy):
+ *   - PASS: κ_trio ∈ [0.7378, 0.8378] (canonical ±0.05)
+ *   - DRIFT_LOW: κ_trio < 0.7378
+ *   - DRIFT_HIGH: κ_trio > 0.8378 (also a fail signal — judge ensemble drifted upward)
+ */
+
+/** Canonical κ baseline anchor pinned per manifest v7. */
+export const CANONICAL_KAPPA = 0.7877758913412564;
+
+/** Drift threshold (per brief §4 condition 3). */
+export const KAPPA_DRIFT_THRESHOLD = 0.05;
+
+/** Drift band lower bound (canonical − threshold). */
+export const KAPPA_DRIFT_BAND_LOW = CANONICAL_KAPPA - KAPPA_DRIFT_THRESHOLD;
+
+/** Drift band upper bound (canonical + threshold). */
+export const KAPPA_DRIFT_BAND_HIGH = CANONICAL_KAPPA + KAPPA_DRIFT_THRESHOLD;
+
+/** v6 policy floor for absolute κ pass (kept for cross-validation reporting). */
+export const V6_KAPPA_POLICY_FLOOR_PASS = 0.70;
+
+/** v6 borderline lower bound. */
+export const V6_KAPPA_POLICY_FLOOR_BORDERLINE = 0.60;
+
+export type KappaVerdict =
+  | 'PASS_WITHIN_DRIFT_BAND'
+  | 'DRIFT_LOW_BELOW_BAND'
+  | 'DRIFT_HIGH_ABOVE_BAND';
+
+export interface KappaPairwise {
+  /** Cohen's κ between Opus and GPT verdict streams. */
+  kOpusGpt: number;
+  /** Cohen's κ between Opus and MiniMax verdict streams. */
+  kOpusMinimax: number;
+  /** Cohen's κ between GPT and MiniMax verdict streams. */
+  kGptMinimax: number;
+}
+
+export interface KappaAuditResult {
+  /** Conservative trio κ = min of three pairwise κ values (per manifest v6 §5.4). */
+  kConservativeTrio: number;
+  /** Pairwise components (carried through for audit log). */
+  pairwise: KappaPairwise;
+  /** Drift band verdict per Faza 1 §F.3 acceptance. */
+  verdict: KappaVerdict;
+  /** Absolute drift from canonical (positive = above, negative = below). */
+  driftFromCanonical: number;
+  /** Whether the κ_conservative_trio passes v6 policy floor (≥ 0.70). */
+  v6PolicyFloorPass: boolean;
+  /** Audit log line for inclusion in checkpoint reports. */
+  auditLogLine: string;
+}
+
+/** Compute κ audit verdict from three pairwise Cohen's κ values. */
+export function auditKappa(pairwise: KappaPairwise): KappaAuditResult {
+  const kConservativeTrio = Math.min(
+    pairwise.kOpusGpt,
+    pairwise.kOpusMinimax,
+    pairwise.kGptMinimax,
+  );
+
+  const driftFromCanonical = kConservativeTrio - CANONICAL_KAPPA;
+
+  let verdict: KappaVerdict;
+  if (kConservativeTrio < KAPPA_DRIFT_BAND_LOW) {
+    verdict = 'DRIFT_LOW_BELOW_BAND';
+  } else if (kConservativeTrio > KAPPA_DRIFT_BAND_HIGH) {
+    verdict = 'DRIFT_HIGH_ABOVE_BAND';
+  } else {
+    verdict = 'PASS_WITHIN_DRIFT_BAND';
+  }
+
+  const v6PolicyFloorPass = kConservativeTrio >= V6_KAPPA_POLICY_FLOOR_PASS;
+
+  const auditLogLine = [
+    `κ_conservative_trio=${kConservativeTrio.toFixed(4)}`,
+    `canonical=${CANONICAL_KAPPA.toFixed(4)}`,
+    `drift=${driftFromCanonical >= 0 ? '+' : ''}${driftFromCanonical.toFixed(4)}`,
+    `verdict=${verdict}`,
+    `v6_policy_floor=${v6PolicyFloorPass ? 'PASS' : 'FAIL'}`,
+  ].join(' | ');
+
+  return {
+    kConservativeTrio,
+    pairwise,
+    verdict,
+    driftFromCanonical,
+    v6PolicyFloorPass,
+    auditLogLine,
+  };
+}
+
+/**
+ * Compute Cohen's κ from a 2×2 confusion matrix.
+ *
+ * Convenience helper for callers that have raw verdict pair counts (the κ
+ * recalibration script computes these for Phase 1 baseline; tests and
+ * inline audits during Faza 1 reproduce the computation here).
+ *
+ * Returns NaN if total observations is zero (caller must handle).
+ */
+export function computeCohensKappa(confusion: {
+  bothCorrect: number;
+  bothIncorrect: number;
+  firstCorrectSecondIncorrect: number;
+  firstIncorrectSecondCorrect: number;
+}): number {
+  const { bothCorrect, bothIncorrect, firstCorrectSecondIncorrect, firstIncorrectSecondCorrect } = confusion;
+  const total = bothCorrect + bothIncorrect + firstCorrectSecondIncorrect + firstIncorrectSecondCorrect;
+  if (total === 0) return NaN;
+
+  const observedAgreement = (bothCorrect + bothIncorrect) / total;
+
+  // Marginal probabilities for "correct" verdict per rater.
+  const firstCorrectMarginal = (bothCorrect + firstCorrectSecondIncorrect) / total;
+  const secondCorrectMarginal = (bothCorrect + firstIncorrectSecondCorrect) / total;
+
+  const expectedAgreement =
+    firstCorrectMarginal * secondCorrectMarginal +
+    (1 - firstCorrectMarginal) * (1 - secondCorrectMarginal);
+
+  if (expectedAgreement === 1) return 1.0;  // perfect base rate, no variance → return κ=1 by convention
+
+  return (observedAgreement - expectedAgreement) / (1 - expectedAgreement);
+}

benchmarks/gepa/src/faza-1/mutation-oracle-fork.ts

@@ -0,0 +1,85 @@
+/**
+ * GEPA Faza 1 — mutation oracle fork (Qwen vs non-Qwen template routing).
+ *
+ * Per Amendment 2 §4 + manifest v7 §mutation_oracle_design.
+ *
+ * Phase 4.5 finding requires Qwen-specific scaffolding to address mechanistic
+ * under-engagement; uniform mutation guidance would not target the empirical
+ * gap. Forked oracle prompts ensure Qwen mutations explore the engagement-
+ * bonus reward landscape while non-Qwen mutations explore the broader scaffold
+ * space.
+ *
+ * This module is responsible only for:
+ *   1. Selecting the right template path per shape class
+ *   2. Loading template content from disk
+ *   3. Substituting baseline shape body + failure mode summary into template
+ *
+ * The actual LLM oracle call lives in the run orchestrator (out of scaffold scope).
+ */
+
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+
+import { type ShapeName, QWEN_TARGETED_SHAPES } from './types.js';
+
+export type TemplateClass = 'qwen' | 'non-qwen';
+
+/** Per-shape template class routing. */
+export function classifyShape(shape: ShapeName): TemplateClass {
+  return QWEN_TARGETED_SHAPES.has(shape) ? 'qwen' : 'non-qwen';
+}
+
+/** Resolve template path for a given shape class. */
+export function templatePathForShape(
+  shape: ShapeName,
+  oracleDir: string = path.resolve(__dirname, '../../oracle/faza-1'),
+): string {
+  const cls = classifyShape(shape);
+  const filename =
+    cls === 'qwen'
+      ? 'mutation-prompt-template-qwen.md'
+      : 'mutation-prompt-template-non-qwen.md';
+  return path.join(oracleDir, filename);
+}
+
+/** Load raw template content for a given shape. */
+export function loadTemplate(
+  shape: ShapeName,
+  oracleDir?: string,
+): string {
+  const templatePath = templatePathForShape(shape, oracleDir);
+  return fs.readFileSync(templatePath, 'utf-8');
+}
+
+/** Inputs for assembling the final oracle prompt. */
+export interface OraclePromptInputs {
+  shape: ShapeName;
+  /** Baseline shape file content (the candidate to mutate). */
+  baselineShapeContent: string;
+  /**
+   * Failure mode summary from Phase 4.3 verdict (top-3 T2 failures for this shape).
+   * Per brief §3.3 mutation oracle prompt.
+   */
+  failureModeSummary: string;
+  oracleDir?: string;
+}
+
+/**
+ * Build the complete oracle prompt by template substitution.
+ *
+ * Templates use these placeholders (must appear verbatim in template files):
+ *   ###BASELINE_SHAPE_CONTENT###
+ *   ###FAILURE_MODE_SUMMARY###
+ *   ###SHAPE_NAME###
+ *   ###TEMPLATE_CLASS###
+ */
+export function buildOraclePrompt(inputs: OraclePromptInputs): string {
+  const template = loadTemplate(inputs.shape, inputs.oracleDir);
+  const cls = classifyShape(inputs.shape);
+
+  return template
+    .replace(/###BASELINE_SHAPE_CONTENT###/g, inputs.baselineShapeContent)
+    .replace(/###FAILURE_MODE_SUMMARY###/g, inputs.failureModeSummary)
+    .replace(/###SHAPE_NAME###/g, inputs.shape)
+    .replace(/###TEMPLATE_CLASS###/g, cls);
+}

benchmarks/gepa/src/faza-1/mutation-validator.ts

@@ -0,0 +1,194 @@
+/**
+ * GEPA Faza 1 — mutation validator (cell-semantic preservation audit).
+ *
+ * Per launch decision §A.4 (binding) + manifest v7 §gepa.mutation_validator.
+ *
+ * Boundary anchor: MULTI_STEP_ACTION_CONTRACT constant in
+ * packages/agent/src/prompt-shapes/types.ts.
+ *   - whole-file SHA: 1a9fa329e4b66ed9f0abe8bc22cbbf0124e0c879e1e78ec806d557cab25bc94d
+ *   - constant body bytes SHA: 70a1701dfa126f8dc1df9c116f0a8469da005821ecadc59d9b8f348568e755ba (252 bytes)
+ *
+ * Any GEPA candidate that produces non-zero diff against either anchor → INVALID.
+ *
+ * Allowed diff targets (per manifest v7 §gepa.mutation_validator.valid_diff_targets):
+ *   - shape_file.systemPrompt method body (string-building only)
+ *   - shape_file.soloUserPrompt method body
+ *   - shape_file.multiStepKickoffUserPrompt method body
+ *   - shape_file.retrievalInjectionUserPrompt method body
+ *   - shape_file.metadata.evidence_link (MUST update to point to GEPA Gen 1 results)
+ *
+ * Invalid diff targets (LOCKED):
+ *   - types.ts (entire file)
+ *   - selector.ts (entire file)
+ *   - index.ts (entire file)
+ *   - shape_file.metadata.{description,modelClass,defaultThinking,defaultMaxTokens}
+ *   - shape_file.imports
+ *   - MULTI_STEP_ACTION_CONTRACT bytes
+ */
+
+import * as crypto from 'node:crypto';
+import * as fs from 'node:fs';
+
+/**
+ * Cell-semantic boundary SHAs pinned at substrate anchor commit c9bda3d.
+ * These are the byte-level invariants that any GEPA candidate must preserve.
+ */
+export const BOUNDARY_SHAS = {
+  /** Whole types.ts file SHA (must equal this exactly post-mutation). */
+  typesFile: '1a9fa329e4b66ed9f0abe8bc22cbbf0124e0c879e1e78ec806d557cab25bc94d',
+  /** MULTI_STEP_ACTION_CONTRACT constant body bytes SHA (252 bytes). */
+  multiStepActionContract: '70a1701dfa126f8dc1df9c116f0a8469da005821ecadc59d9b8f348568e755ba',
+} as const;
+
+/** Per-shape baseline SHAs pinned at substrate anchor commit c9bda3d. */
+export const BASELINE_SHAPE_SHAS: Readonly<Record<string, string>> = {
+  'claude.ts': 'cbaf0c37b067b025a1fe97f2feeec11fae4070a8b3fcfaad1da8775dda451cc0',
+  'qwen-thinking.ts': '848a4e4917baa5c7bbcc3bb35fb8cb4b4ac8f0ab537243f14cbef3a99197aacb',
+  'qwen-non-thinking.ts': '35be379be9a8caafc2c419e32da5f63f92fc83f6f6d70d9df76029c1e8584572',
+  'gpt.ts': '5dc6d750d52a68feb9d37ad8384b2bcd59d70962066122ff086b0e5888413576',
+  'generic-simple.ts': '81189817f560e26a69394248d8bd9089cae72c7d40825323e2b7407e36026172',
+} as const;
+
+/** Validator verdict for a single candidate. */
+export interface ValidatorVerdict {
+  valid: boolean;
+  /** List of violations (empty if valid). Each violation is a structured reason. */
+  violations: ValidationViolation[];
+  /** SHA of the candidate shape file (computed by validator). */
+  candidateShapeFileSha: string;
+  /** SHA of the cell-semantic types.ts file at validation time. */
+  typesFileSha: string;
+  /** SHA of the MULTI_STEP_ACTION_CONTRACT bytes at validation time. */
+  multiStepActionContractSha: string;
+}
+
+export interface ValidationViolation {
+  category:
+    | 'types_file_modified'
+    | 'multi_step_action_contract_modified'
+    | 'shape_file_unchanged_from_baseline'
+    | 'shape_file_metadata_locked_field_modified'
+    | 'shape_file_imports_modified';
+  severity: 'invalid';
+  detail: string;
+}
+
+/** Compute SHA-256 of a string's UTF-8 bytes. */
+export function sha256(bytes: string | Buffer): string {
+  const buf = typeof bytes === 'string' ? Buffer.from(bytes, 'utf-8') : bytes;
+  return crypto.createHash('sha256').update(buf).digest('hex');
+}
+
+/** Compute SHA-256 of a file's bytes. */
+export function sha256File(filepath: string): string {
+  return sha256(fs.readFileSync(filepath));
+}
+
+/**
+ * Extract the bytes of MULTI_STEP_ACTION_CONTRACT from a types.ts file content.
+ * Returns null if the constant is not found (which itself indicates a violation).
+ */
+export function extractMultiStepActionContractBytes(typesFileContent: string): string | null {
+  // Match the template literal body between backticks.
+  const match = typesFileContent.match(/export const MULTI_STEP_ACTION_CONTRACT = `([^`]+)`/);
+  return match ? match[1] : null;
+}
+
+/** Inputs for validating a GEPA candidate against cell-semantic boundaries. */
+export interface ValidatorInputs {
+  /** Path to the candidate shape file (e.g., gepa-evolved/qwen-thinking-gen1-v0.ts). */
+  candidateShapeFilePath: string;
+  /** Shape file basename for baseline lookup (e.g., 'qwen-thinking.ts'). */
+  baselineShapeName: keyof typeof BASELINE_SHAPE_SHAS;
+  /** Path to the types.ts file (cell-semantic boundary anchor). */
+  typesFilePath: string;
+  /** Whether to require shape file to differ from baseline (true for Gen 1 mutations). */
+  expectShapeDiff: boolean;
+}
+
+/**
+ * Validate a GEPA candidate against the cell-semantic preservation invariants.
+ *
+ * Returns INVALID with structured violations if any boundary anchor is breached,
+ * or if a Gen 1 mutation produces zero diff vs baseline (violates the
+ * "every mutation must change something" implicit contract — Gen 0 baselines
+ * use expectShapeDiff=false).
+ */
+export function validateCandidate(inputs: ValidatorInputs): ValidatorVerdict {
+  const violations: ValidationViolation[] = [];
+
+  const typesFileSha = sha256File(inputs.typesFilePath);
+  const typesContent = fs.readFileSync(inputs.typesFilePath, 'utf-8');
+  const contractBytes = extractMultiStepActionContractBytes(typesContent);
+  const multiStepActionContractSha = contractBytes ? sha256(contractBytes) : '';
+  const candidateShapeFileSha = sha256File(inputs.candidateShapeFilePath);
+
+  if (typesFileSha !== BOUNDARY_SHAS.typesFile) {
+    violations.push({
+      category: 'types_file_modified',
+      severity: 'invalid',
+      detail: `types.ts SHA ${typesFileSha} != pinned baseline ${BOUNDARY_SHAS.typesFile}`,
+    });
+  }
+
+  if (multiStepActionContractSha !== BOUNDARY_SHAS.multiStepActionContract) {
+    violations.push({
+      category: 'multi_step_action_contract_modified',
+      severity: 'invalid',
+      detail:
+        contractBytes === null
+          ? 'MULTI_STEP_ACTION_CONTRACT constant not found in types.ts'
+          : `MULTI_STEP_ACTION_CONTRACT bytes SHA ${multiStepActionContractSha} != pinned baseline ${BOUNDARY_SHAS.multiStepActionContract}`,
+    });
+  }
+
+  const baselineSha = BASELINE_SHAPE_SHAS[inputs.baselineShapeName];
+  if (inputs.expectShapeDiff && candidateShapeFileSha === baselineSha) {
+    violations.push({
+      category: 'shape_file_unchanged_from_baseline',
+      severity: 'invalid',
+      detail: `Gen 1 mutation expected to differ from baseline ${inputs.baselineShapeName} (SHA ${baselineSha}) but candidate produced identical bytes`,
+    });
+  }
+
+  // Optional shallow check: locked metadata fields must remain in the shape file.
+  // GEPA mutations are allowed to update evidence_link only.
+  const candidateContent = fs.readFileSync(inputs.candidateShapeFilePath, 'utf-8');
+  const lockedMetadataFields = [
+    'description:',
+    'modelClass:',
+    'defaultThinking:',
+    'defaultMaxTokens:',
+  ];
+  for (const field of lockedMetadataFields) {
+    if (!candidateContent.includes(field)) {
+      violations.push({
+        category: 'shape_file_metadata_locked_field_modified',
+        severity: 'invalid',
+        detail: `Locked metadata field "${field}" missing from candidate shape file`,
+      });
+    }
+  }
+
+  // Check that imports block is still present (GEPA mutations cannot add/remove imports).
+  // Accept either './types.js' (same-dir candidate) or '../types.js' (gepa-evolved/ subdir
+  // candidate per manifest v7 §gepa.shape_scope.target_path) — the invariant is that
+  // candidates must import from types.js, not the specific relative path.
+  const importsTypes = candidateContent.includes("from './types.js'") ||
+                       candidateContent.includes("from '../types.js'");
+  if (!importsTypes) {
+    violations.push({
+      category: 'shape_file_imports_modified',
+      severity: 'invalid',
+      detail: `Required import from "types.js" (either "./" or "../" relative) missing from candidate shape file`,
+    });
+  }
+
+  return {
+    valid: violations.length === 0,
+    violations,
+    candidateShapeFileSha,
+    typesFileSha,
+    multiStepActionContractSha,
+  };
+}

benchmarks/gepa/src/faza-1/selection.ts

@@ -0,0 +1,128 @@
+/**
+ * GEPA Faza 1 — top-1-per-shape selection.
+ *
+ * Per brief §2 selection metric ("best-per-shape-per-cell, ne aggregate") +
+ * launch decision §F + §F.5 acceptance.
+ *
+ * Selection algorithm:
+ *   1. For each shape, compute fitness for all candidates (per-shape fitness fork)
+ *   2. Per shape, select candidate with highest fitness as "best"
+ *   3. Apply acceptance verdict to each best-per-shape candidate
+ *   4. Report shape-level + run-aggregate verdict
+ *
+ * Run-aggregate §F conditions:
+ *   §F.2: ≥3/5 shapes show positive trio_strict delta vs NULL-baseline
+ *
+ * Per-candidate §F.1 + §F.5 already evaluated by acceptance.evaluateCandidate.
+ */
+
+import { computeFitness } from './fitness.js';
+import { evaluateCandidate } from './acceptance.js';
+import {
+  type CandidateMetrics,
+  type FitnessComponents,
+  type AcceptanceVerdict,
+  type ShapeName,
+} from './types.js';
+
+/** Per-shape selection result. */
+export interface ShapeSelectionResult {
+  shape: ShapeName;
+  bestCandidate: CandidateMetrics;
+  bestFitness: FitnessComponents;
+  acceptance: AcceptanceVerdict;
+  /** All candidates evaluated for this shape (for audit log). */
+  allCandidatesRanked: Array<{ candidate: CandidateMetrics; fitness: FitnessComponents }>;
+}
+
+/** Aggregate run verdict per launch decision §F.2. */
+export interface RunAggregateVerdict {
+  /** Number of shapes with positive trio_strict delta vs NULL-baseline. */
+  shapesWithPositiveDelta: number;
+  /** Total shapes evaluated. */
+  totalShapes: number;
+  /** §F.2 condition: ≥3/5 shapes show positive delta. */
+  condition2Pass: boolean;
+  /** Number of shapes where best candidate was ACCEPTED (passes §F.1 + not §F.5). */
+  shapesAccepted: number;
+}
+
+/** Full selection report — per shape + run aggregate. */
+export interface SelectionReport {
+  perShape: ShapeSelectionResult[];
+  runAggregate: RunAggregateVerdict;
+}
+
+/** Inputs: per-shape candidates + per-shape NULL-baseline metrics + cost baselines. */
+export interface SelectionInputs {
+  /** All candidates grouped by shape. */
+  candidatesPerShape: Map<ShapeName, CandidateMetrics[]>;
+  /** NULL-baseline trio_strict_pass_rate (op. ii) per shape. */
+  baselineTrioStrictPassRateII: Map<ShapeName, number>;
+  /** NULL-baseline median cost (USD per evaluation) per shape. */
+  baselineMedianCostUsd: Map<ShapeName, number>;
+}
+
+/**
+ * Run top-1-per-shape selection + apply acceptance verdicts.
+ *
+ * Returns a complete selection report including per-shape results and the
+ * run-aggregate verdict.
+ *
+ * Throws if a shape in candidatesPerShape lacks a corresponding baseline entry.
+ */
+export function runSelection(inputs: SelectionInputs): SelectionReport {
+  const perShape: ShapeSelectionResult[] = [];
+
+  for (const [shape, candidates] of inputs.candidatesPerShape.entries()) {
+    if (candidates.length === 0) {
+      continue;  // skip shapes with no candidates
+    }
+
+    const baselineRate = inputs.baselineTrioStrictPassRateII.get(shape);
+    if (baselineRate === undefined) {
+      throw new Error(`runSelection: missing baseline trio_strict rate for shape "${shape}"`);
+    }
+    const baselineCost = inputs.baselineMedianCostUsd.get(shape);
+    if (baselineCost === undefined) {
+      throw new Error(`runSelection: missing baseline median cost for shape "${shape}"`);
+    }
+
+    // Compute fitness for all candidates of this shape
+    const ranked = candidates
+      .map(candidate => ({
+        candidate,
+        fitness: computeFitness({ candidate, baselineMedianCostUsd: baselineCost }),
+      }))
+      .sort((a, b) => b.fitness.fitness - a.fitness.fitness);
+
+    const top = ranked[0];
+    const acceptance = evaluateCandidate({
+      candidate: top.candidate,
+      baselineTrioStrictPassRateII: baselineRate,
+    });
+
+    perShape.push({
+      shape,
+      bestCandidate: top.candidate,
+      bestFitness: top.fitness,
+      acceptance,
+      allCandidatesRanked: ranked,
+    });
+  }
+
+  const shapesWithPositiveDelta = perShape.filter(s => s.acceptance.trioStrictDeltaPP > 0).length;
+  const shapesAccepted = perShape.filter(s => s.acceptance.accepted).length;
+  const totalShapes = perShape.length;
+  const condition2Pass = shapesWithPositiveDelta >= 3;
+
+  return {
+    perShape,
+    runAggregate: {
+      shapesWithPositiveDelta,
+      totalShapes,
+      condition2Pass,
+      shapesAccepted,
+    },
+  };
+}

benchmarks/gepa/src/faza-1/types.ts

@@ -0,0 +1,321 @@
+/**
+ * GEPA Faza 1 — shared type definitions.
+ *
+ * Per manifest v7 (SHA 583712dde139ffc87fb1ab21643f68d52c56469ded9e8090a624980b05969beb,
+ * Amendment 2 supplemented) and launch decision §A inherited rules.
+ *
+ * Cell semantic boundary linchpin: MULTI_STEP_ACTION_CONTRACT in
+ * packages/agent/src/prompt-shapes/types.ts (SHA 70a1701d...).
+ *
+ * DO NOT modify this file as part of GEPA candidate evolution. This file is
+ * scaffold-only; the mutation surface is the prompt-shape body methods in
+ * packages/agent/src/prompt-shapes/{claude,qwen-thinking,qwen-non-thinking,gpt,generic-simple}.ts.
+ */
+
+/** The 5 shape names targeted by Faza 1 GEPA evolution. */
+export type ShapeName =
+  | 'claude'
+  | 'qwen-thinking'
+  | 'qwen-non-thinking'
+  | 'gpt'
+  | 'generic-simple';
+
+/**
+ * Shapes that get retrieval-engagement weighting per Amendment 2 §3.
+ * Phase 4.5 finding: only Qwen exhibits the under-engagement gap.
+ */
+export const QWEN_TARGETED_SHAPES: ReadonlySet<ShapeName> = new Set<ShapeName>([
+  'qwen-thinking',
+  'qwen-non-thinking',
+]);
+
+/**
+ * Shapes that use baseline fitness (no retrieval-engagement weight).
+ * Per Amendment 2 §3: these shapes don't have the gap, so applying the
+ * bonus uniformly would distort their fitness measurement.
+ */
+export const NON_QWEN_SHAPES: ReadonlySet<ShapeName> = new Set<ShapeName>([
+  'claude',
+  'gpt',
+  'generic-simple',
+]);
+
+/** Per-task evaluation result for one candidate × one instance. */
+export interface EvaluationResult {
+  /** Stable instance identifier (e.g., from corpus generator). */
+  instanceId: string;
+
+  /** Trio judge mean (per pilot runner line 654 — arithmetic mean of valid judge means). */
+  trioMean: number;
+
+  /**
+   * Trio strict pass per metric_operationalization (ii) — primary acceptance.
+   * `trioMean >= 4.0` per Amendment 1 Ask B ratification.
+   */
+  trioStrictPassII: boolean;
+
+  /**
+   * Trio strict pass per metric_operationalization (i) — supplementary.
+   * `>= 2 of 3 judges with judge.mean >= 3.5` per pilot runner line 657.
+   * Reported in parallel for cross-validation against pilot baseline.
+   */
+  trioStrictPassI: boolean;
+
+  /** Number of retrieve actions issued during this evaluation (existing telemetry). */
+  retrievalCalls: number;
+
+  /** Subject + judge cumulative cost (USD) for this evaluation. */
+  costUsd: number;
+}
+
+/** Aggregated per-candidate metrics across N=8 evaluations (per shape). */
+export interface CandidateMetrics {
+  /** Stable candidate identifier (e.g., shape name + generation + variant). */
+  candidateId: string;
+
+  /** Which shape this candidate belongs to. */
+  shape: ShapeName;
+
+  /** All N=8 evaluation results for this candidate. */
+  evaluations: EvaluationResult[];
+
+  /** Pass rate per operationalization (ii) — primary. Range [0, 1]. */
+  trioStrictPassRateII: number;
+
+  /** Pass rate per operationalization (i) — supplementary. Range [0, 1]. */
+  trioStrictPassRateI: number;
+
+  /** Mean retrieval_calls across evaluations (per task). */
+  meanRetrievalCallsPerTask: number;
+
+  /** Mean cost (USD) across evaluations. */
+  meanCostUsd: number;
+}
+
+/**
+ * Per-shape fitness components per Amendment 2 §3.
+ *
+ * For Qwen-targeted shapes:
+ *   fitness = trio_strict_pass_rate + retrieval_engagement_bonus - cost_penalty
+ *
+ * For non-Qwen shapes:
+ *   fitness = trio_strict_pass_rate - cost_penalty
+ *   (retrievalEngagementBonus is always 0 for non-Qwen — not added to fitness)
+ */
+export interface FitnessComponents {
+  /** trio_strict_pass_rate per operationalization (ii). Range [0, 1]. */
+  trioStrictPassRateII: number;
+
+  /**
+   * Retrieval engagement bonus per Amendment 2 §3 bands:
+   *   +0.05 if mean retrieval_calls per task >= 2.0
+   *    0.00 if mean retrieval_calls per task in [1.5, 2.0)
+   *   -0.05 if mean retrieval_calls per task < 1.5
+   * Always 0.0 for non-Qwen shapes (these shapes don't have the gap).
+   */
+  retrievalEngagementBonus: number;
+
+  /**
+   * Cost penalty per brief §3.1 — −0.5pp per $0.10 above per-shape baseline median.
+   * Encoded as decimal (e.g., 0.005 = 0.5pp).
+   */
+  costPenalty: number;
+
+  /** Aggregate fitness (sum of above with sign convention: bonus +, penalty −). */
+  fitness: number;
+
+  /**
+   * Whether retrieval engagement was applied (true for Qwen-targeted shapes,
+   * false otherwise). Useful for downstream auditing + report generation.
+   */
+  retrievalEngagementApplied: boolean;
+}
+
+/** Inputs for the per-shape fitness function. */
+export interface FitnessInputs {
+  /** Aggregated metrics for this candidate. */
+  candidate: CandidateMetrics;
+
+  /**
+   * Per-shape baseline median cost (USD per evaluation). Used for cost penalty
+   * computation. Typically the NULL-baseline median for the same shape.
+   */
+  baselineMedianCostUsd: number;
+}
+
+/** Acceptance verdict for a single candidate per §F + §F.5 of launch decision. */
+export interface AcceptanceVerdict {
+  /** Whether candidate passes §F condition 1 (trio_strict delta + Qwen retrieval floor). */
+  condition1Pass: boolean;
+
+  /**
+   * §F.5 false-positive guard — REJECTED if Qwen candidate has +5pp trio delta
+   * but mean retrieval_calls < 1.5 (Amendment 2 §5).
+   */
+  condition5FalsePositiveGuardTriggered: boolean;
+
+  /** Overall acceptance for this candidate (must pass condition 1 AND not trigger §F.5). */
+  accepted: boolean;
+
+  /** Detailed reason string for audit log. */
+  reason: string;
+
+  /**
+   * Computed delta vs NULL-baseline trio_strict_pass_rate (percentage points).
+   * Positive = improvement.
+   */
+  trioStrictDeltaPP: number;
+}
+
+/** Inputs for the acceptance validator. */
+export interface AcceptanceInputs {
+  /** Candidate under evaluation. */
+  candidate: CandidateMetrics;
+
+  /** NULL-baseline trio_strict_pass_rate (op. (ii)) for the same shape. */
+  baselineTrioStrictPassRateII: number;
+}
+
+// ── Amendment 7 — tiered fitness + Δ-floor types ───────────────────────────
+
+/**
+ * Per-shape NULL-baseline anchors pinned from Checkpoint A v2 §B.2 (manifest
+ * v7 Amendment 6 binding SHA 0b55d8e353...).
+ *
+ * These anchor:
+ *   - Tier 1 baseline (NULL pass rate per shape)
+ *   - Tier 2 baseline (NULL retrieval engagement per shape)
+ *   - §F.1 acceptance gate ≥+5pp delta basis
+ *   - Δ-floor threshold 2 per-shape comparison
+ *
+ * Source: real per-shape data from re-run NULL-baseline post Amendment 6
+ * promptShapeOverride bug fix (run bhe0zwi91, 40/40 evals, 2026-04-28).
+ */
+export const NULL_BASELINE_PER_SHAPE: Readonly<
+  Record<ShapeName, { trioStrictPassRateII: number; meanRetrievalCallsPerTask: number }>
+> = {
+  claude:              { trioStrictPassRateII: 0.875, meanRetrievalCallsPerTask: 1.12 },
+  'qwen-thinking':     { trioStrictPassRateII: 0.875, meanRetrievalCallsPerTask: 1.12 },
+  'qwen-non-thinking': { trioStrictPassRateII: 1.000, meanRetrievalCallsPerTask: 1.25 },
+  gpt:                 { trioStrictPassRateII: 0.750, meanRetrievalCallsPerTask: 1.00 },
+  'generic-simple':    { trioStrictPassRateII: 0.875, meanRetrievalCallsPerTask: 1.12 },
+} as const;
+
+/** NULL-baseline aggregate across all 5 shapes (Checkpoint A v2 §B.2). */
+export const NULL_BASELINE_AGGREGATE = {
+  trioStrictPassRateII: 0.875,         // 35/40
+  meanRetrievalCallsPerTask: 1.12,
+} as const;
+
+/**
+ * Tiered fitness components per Amendment 7 §fitness_function_tiered.
+ *
+ * Saturated regime (NULL pass rate ≥75% for ≥4/5 shapes — current Faza 1 state)
+ * makes Tier 1 (NULL delta) noise-bound on N=8 binomial. Tier 2 + Tier 3 act as
+ * primary differentiators; Tier 1 retained as TIE-BREAKER + acceptance gate.
+ */
+export interface TieredFitnessComponents {
+  /**
+   * Tier 1 — NULL pass rate delta (signed, percentage points).
+   * Acceptance gate threshold: ≥+5pp (launch decision §F.1, UNCHANGED).
+   * Role in saturated regime: TIE_BREAKER (noise-bound at N=8).
+   */
+  tier1DeltaPP: number;
+
+  /**
+   * Tier 2 — continuous retrieval engagement bonus (Qwen-targeted only).
+   * Formula: clamp(0.05 × delta_pp, 0, 0.25) where delta_pp = (candidate − baseline) × 100.
+   * Always 0 for non-Qwen shapes.
+   * Role in saturated regime: PRIMARY differentiator for Qwen-targeted shapes.
+   */
+  tier2RetrievalBonus: number;
+
+  /**
+   * Tier 3 — binary cell-semantic anchor invariance bonus.
+   * 0.10 if all 7 anchors invariant (mutation-validator passed); 0 otherwise.
+   * Role in saturated regime: SECONDARY differentiator (substrate-preservation proxy).
+   */
+  tier3CellSemanticInvarianceBonus: number;
+
+  /**
+   * Aggregate fitness in saturated regime: tier_2 + tier_3.
+   * Tier 1 reserved as tie-breaker (NOT in this aggregate).
+   * Cost penalty per Amendment 2 NOT in tiered ranking aggregate (supplementary diagnostic).
+   */
+  aggregateSaturatedRegime: number;
+
+  /** Whether saturated regime applies (per Amendment 7 §fitness_function_tiered.saturated_regime_definition). */
+  saturatedRegimeApplied: boolean;
+
+  /** Cell-semantic anchor invariance count (0..7) for audit reporting. */
+  cellSemanticAnchorInvarianceCount: number;
+}
+
+/** Inputs for tiered fitness. */
+export interface TieredFitnessInputs {
+  /** Aggregated metrics for this candidate. */
+  candidate: CandidateMetrics;
+
+  /** Per-shape NULL-baseline pass rate (op. ii). Typically NULL_BASELINE_PER_SHAPE[shape].trioStrictPassRateII. */
+  nullBaselinePassRateII: number;
+
+  /** Per-shape NULL-baseline mean retrieval calls per task. Typically NULL_BASELINE_PER_SHAPE[shape].meanRetrievalCallsPerTask. */
+  nullBaselineMeanRetrievalCallsPerTask: number;
+
+  /** Whether the candidate's mutation-validator verdict was VALID (all 7 anchors invariant). */
+  mutationValidatorPassed: boolean;
+
+  /**
+   * Whether the saturated regime applies (≥4/5 shapes have NULL pass rate ≥75%).
+   * Caller computes from Checkpoint A v2 data; default true for Faza 1.
+   */
+  saturatedRegime: boolean;
+}
+
+/**
+ * Δ-floor verdict per Amendment 7 §gen_1_pre_registered_delta_floor.
+ * Pre-registered Gen 1 floor for "evolution worked at all" — looser than
+ * §F.1 acceptance gate. Three OR-gated thresholds.
+ */
+export interface DeltaFloorVerdict {
+  /** Threshold 1: aggregate Tier 1 delta ≥+3pp. */
+  threshold1AggregateTier1: 'PASS' | 'FAIL';
+  /** Aggregate Tier 1 delta value (pp, signed) — for audit. */
+  threshold1ValuePP: number;
+
+  /** Threshold 2: max Qwen-shape retrieval engagement delta ≥+0.10 absolute. */
+  threshold2QwenRetrievalAbsolute: 'PASS' | 'FAIL';
+  /** Max delta across Qwen shapes (absolute) — for audit. */
+  threshold2MaxDeltaAbsolute: number;
+
+  /** Threshold 3: (Tier 1 ≥0pp) AND (Tier 2 ≥0.05). */
+  threshold3CompoundTier1PlusTier2: 'PASS' | 'FAIL';
+  /** Aggregate Tier 1 delta (pp, signed) — for audit. */
+  threshold3Tier1ValuePP: number;
+  /** Aggregate Tier 2 bonus across Qwen-targeted candidates — for audit. */
+  threshold3Tier2Aggregate: number;
+
+  /** Overall verdict: PROCEED if ANY threshold passes; HALT_INVESTIGATE if ALL fail. */
+  overallVerdict: 'PROCEED' | 'HALT_INVESTIGATE';
+}
+
+/** Inputs for Δ-floor verdict computation. */
+export interface DeltaFloorInputs {
+  /** Aggregate trio_strict_pass_rate_II across all evaluated candidates × all evals (in saturated regime, mean across 30 Checkpoint B evals). */
+  aggregateTrioStrictPassRateII: number;
+
+  /** Aggregate NULL-baseline pass rate (e.g., 0.875 = NULL_BASELINE_AGGREGATE.trioStrictPassRateII). */
+  aggregateNullBaselinePassRateII: number;
+
+  /**
+   * Per-shape mean retrieval calls per task for Qwen-targeted candidates.
+   * Map: shape → meanRetrievalCallsPerTask. Empty entries treated as no-data (skipped).
+   */
+  qwenShapeRetrievalMeans: Partial<Record<ShapeName, number>>;
+
+  /** Per-shape NULL baseline retrieval means (typically NULL_BASELINE_PER_SHAPE[shape].meanRetrievalCallsPerTask). */
+  qwenShapeNullBaselineRetrievalMeans: Partial<Record<ShapeName, number>>;
+
+  /** Aggregate Tier 2 retrieval engagement bonus across Qwen-targeted candidates (mean). */
+  qwenAggregateTier2Bonus: number;
+}

benchmarks/gepa/tests/faza-1/__faza1-closed/README.md

@@ -0,0 +1,60 @@
+# Faza 1 closed — quarantined tests
+
+**Quarantine date:** 2026-04-29 (Phase 5 §0 preflight)
+**Authority:** PM ratification — Phase 5 deployment §0 preflight 3-ask response, Ask #1 Option 1
+**Branch:** `phase-5-deployment-v2`
+**Quarantine commit:** see git log this directory
+**Vitest exclude:** `**/__faza1-closed/**` added to `vitest.config.ts` exclude list — these files are skipped from collection (no longer load-bearing for current verification).
+
+---
+
+## Quarantined files
+
+| File | Reason |
+|---|---|
+| `mutation-validator.test.ts` | Pins baseline shape SHAs at Faza 1 substrate freeze `c9bda3d`; `phase-5-deployment-v2` Opcija C inheritance chain doesn't reach `c9bda3d`. PM Ask #1 Option 1 ratified 2026-04-29. |
+| `registry-injection.test.ts` | Asserts Amendment 8 H1 failure mode (deep-relative-path REGISTRY ≠ package REGISTRY) reproduces under Node 22.19.0; current Node ESM resolver deduplicates the paths so the assertion fails. CC extended quarantine 2026-04-29 same session — flagged in §0 preflight evidence + commit message; PM advised. |
+
+## Why these tests live here
+
+Tests in this directory pin substrate SHAs against the Faza 1 substrate freeze head `c9bda3d` (Phase 4.7 HEAD on `feature/c3-v3-wrapper`, per `benchmarks/preregistration/manifest-v7-gepa-faza1.yaml` `substrate_freeze_head`). They were load-bearing during Faza 1 GEPA evolution runs (executed inside isolated worktree `D:/Projects/waggle-os-faza1-wt` rooted at `c9bda3d`) to enforce the cell-semantic boundary invariant — the mutation oracle must not modify baseline shape file content during Gen 1+ candidate generation.
+
+`registry-injection.test.ts` documents a SECOND class of Faza 1 closed-work artifact: tests that intentionally assert a buggy state reproduces (so the bug stays detectable if someone "fixes" the canonical path back). Once Amendment 8 fixed H1 via the canonical `registerShape` API + Node ESM resolver dedup behavior changed in subsequent versions, these documentation tests can no longer pass — but their failure carries no Phase 5 substrate signal. The H1 fix is verified independently via `selectShape` + `registerShape` integration tests in the agent suite.
+
+After Faza 1 closure (`6bc2089` — Checkpoint C closure decision `decisions/2026-04-29-gepa-faza1-results.md`), the branch architecture decision **Opcija C** (`decisions/2026-04-30-branch-architecture-opcija-c.md`) determined that:
+
+- `phase-5-deployment-v2` inherits `gepa-faza-1` baseline (= `6bc2089`)
+- `gepa-faza-1` parent chain reaches `origin/main` (`5ec069e`), NOT `c9bda3d`
+- `c9bda3d` is on the divergent `feature/c3-v3-wrapper` branch (CC-1 Phase 4 work)
+- Therefore the SHAs of baseline shape files on `phase-5-deployment-v2` reflect `origin/main` content, not the `c9bda3d` content these tests pin
+
+Running these tests on `phase-5-deployment-v2` produces 14 failures with no Phase 5 substrate signal — the failures are a scope-leakage artifact of post-closure test continuation under Opcija C inheritance, not a bug in either Phase 5 substrate or Faza 1 evolution invariants.
+
+Faza 1 closure verdict §F.4 already documents `105/105 anchor invariance checks PASS` during in-worktree execution + `15/15 held-out anchor checks PASS` during Checkpoint C — the cell-semantic boundary discipline was verified and binding throughout Faza 1.
+
+## What this quarantine does and does NOT mean
+
+- **Does NOT mean** Faza 1 substrate discipline was wrong or the test was buggy.
+- **Does NOT mean** baseline shape files have been modified.
+- **Does mean** the SHAs the test pins to (`c9bda3d` substrate snapshot) are not reachable from `phase-5-deployment-v2` HEAD without integration sprint work.
+- **Does mean** the test is no longer load-bearing for Phase 5 deployment substrate verification (REGISTRY API + registerShape canonical path + gen1-v1 shape definitions are verified independently via Phase 5 §0.1 substrate readiness grep).
+
+## Reactivation conditions
+
+These tests should be re-activated (moved back out of `__faza1-closed/`) when ANY of the following holds:
+
+1. **Post-Phase-5 production-stable integration sprint** (per Opcija C §5) merges `feature/c3-v3-wrapper` into the deployment lineage. Re-pin the test SHAs to the integrated substrate snapshot before re-activating.
+2. **Future Faza N evolution sprints** that re-establish substrate freeze inside an isolated worktree. Re-activate the tests inside that worktree's branch context, not on the deployment branch.
+3. **Substrate boundary regression suspected** — re-pin SHAs to the current deployment branch HEAD content and re-activate as a drift detector for that specific branch.
+
+Forbidden: simply blanking the SHA pins to silence the test. Replacement pins must be anchored to a documented substrate snapshot with audit-traceable origin.
+
+## Audit trail
+
+| Anchor | Path |
+|---|---|
+| Faza 1 closure decision | `D:/Projects/PM-Waggle-OS/decisions/2026-04-29-gepa-faza1-results.md` |
+| Branch architecture (Opcija C) | `D:/Projects/PM-Waggle-OS/decisions/2026-04-30-branch-architecture-opcija-c.md` |
+| Phase 5 brief LOCKED | `D:/Projects/PM-Waggle-OS/briefs/2026-04-29-phase-5-deployment-brief-v1.md` |
+| §0 preflight evidence | `D:/Projects/waggle-os/gepa-phase-5/preflight-evidence.md` |
+| Quarantine ratification | PM 3-ask response 2026-04-29, Ask #1 Option 1 |

benchmarks/gepa/tests/faza-1/__faza1-closed/mutation-validator.test.ts

@@ -0,0 +1,254 @@
+/**
+ * GEPA Faza 1 — mutation validator tests.
+ *
+ * Coverage targets:
+ *   - SHA boundary checks (types.ts + MULTI_STEP_ACTION_CONTRACT bytes)
+ *   - Pinned baseline shape SHAs (5 shapes)
+ *   - Locked metadata field detection
+ *   - Imports preservation check
+ *   - Gen 1 mutation must differ from baseline
+ *   - Gen 0 baseline (expectShapeDiff=false) accepts identity
+ *
+ * Validation against actual substrate-pinned files (the worktree at c9bda3d).
+ */
+
+import { describe, expect, it } from 'vitest';
+import * as path from 'node:path';
+import * as fs from 'node:fs';
+import * as os from 'node:os';
+import {
+  BOUNDARY_SHAS,
+  BASELINE_SHAPE_SHAS,
+  sha256,
+  sha256File,
+  extractMultiStepActionContractBytes,
+  validateCandidate,
+} from '../../src/faza-1/mutation-validator.js';
+
+const WORKTREE_ROOT = path.resolve(__dirname, '../../../../');
+const PROMPT_SHAPES_DIR = path.join(WORKTREE_ROOT, 'packages/agent/src/prompt-shapes');
+const TYPES_FILE = path.join(PROMPT_SHAPES_DIR, 'types.ts');
+
+// ───────────────────────────────────────────────────────────────────────────
+// SHA primitive tests
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('sha256 primitive', () => {
+  it('computes deterministic SHA-256 of utf-8 string', () => {
+    expect(sha256('hello')).toBe(
+      '2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824',
+    );
+  });
+
+  it('computes SHA-256 of buffer', () => {
+    expect(sha256(Buffer.from('hello'))).toBe(
+      '2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824',
+    );
+  });
+
+  it('returns different SHAs for different inputs', () => {
+    expect(sha256('a')).not.toBe(sha256('b'));
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Boundary anchor SHAs match actual substrate at c9bda3d
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('boundary anchor SHAs match substrate at c9bda3d', () => {
+  it('types.ts whole-file SHA matches pinned baseline', () => {
+    const actual = sha256File(TYPES_FILE);
+    expect(actual).toBe(BOUNDARY_SHAS.typesFile);
+  });
+
+  it('MULTI_STEP_ACTION_CONTRACT bytes SHA matches pinned baseline', () => {
+    const content = fs.readFileSync(TYPES_FILE, 'utf-8');
+    const bytes = extractMultiStepActionContractBytes(content);
+    expect(bytes).not.toBeNull();
+    expect(sha256(bytes!)).toBe(BOUNDARY_SHAS.multiStepActionContract);
+  });
+
+  it('extractMultiStepActionContractBytes captures the 252-byte constant', () => {
+    const content = fs.readFileSync(TYPES_FILE, 'utf-8');
+    const bytes = extractMultiStepActionContractBytes(content);
+    expect(bytes).not.toBeNull();
+    expect(Buffer.from(bytes!, 'utf-8').length).toBe(252);
+    expect(bytes!).toContain('Output exactly ONE JSON object on its own line');
+  });
+
+  it('extractMultiStepActionContractBytes returns null when constant absent', () => {
+    expect(extractMultiStepActionContractBytes('export const SOMETHING_ELSE = 42;')).toBeNull();
+  });
+});
+
+describe('all 5 baseline shape SHAs match substrate at c9bda3d', () => {
+  for (const shapeName of Object.keys(BASELINE_SHAPE_SHAS) as Array<keyof typeof BASELINE_SHAPE_SHAS>) {
+    it(`baseline ${shapeName} SHA matches pinned`, () => {
+      const filepath = path.join(PROMPT_SHAPES_DIR, shapeName);
+      const actual = sha256File(filepath);
+      expect(actual).toBe(BASELINE_SHAPE_SHAS[shapeName]);
+    });
+  }
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// validateCandidate end-to-end
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('validateCandidate — Gen 0 (baseline) acceptance', () => {
+  it('Gen 0 baseline shape passes validation with expectShapeDiff=false', () => {
+    const verdict = validateCandidate({
+      candidateShapeFilePath: path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'),
+      baselineShapeName: 'qwen-thinking.ts',
+      typesFilePath: TYPES_FILE,
+      expectShapeDiff: false,
+    });
+    expect(verdict.valid).toBe(true);
+    expect(verdict.violations).toHaveLength(0);
+    expect(verdict.candidateShapeFileSha).toBe(BASELINE_SHAPE_SHAS['qwen-thinking.ts']);
+  });
+
+  it('Gen 0 baseline FAILS validation if expectShapeDiff=true (identity violation)', () => {
+    const verdict = validateCandidate({
+      candidateShapeFilePath: path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'),
+      baselineShapeName: 'qwen-thinking.ts',
+      typesFilePath: TYPES_FILE,
+      expectShapeDiff: true,
+    });
+    expect(verdict.valid).toBe(false);
+    expect(verdict.violations.some(v => v.category === 'shape_file_unchanged_from_baseline')).toBe(true);
+  });
+});
+
+describe('validateCandidate — types.ts boundary violation detection', () => {
+  it('FAILS if types.ts SHA differs from pinned baseline', () => {
+    const tmpTypes = path.join(os.tmpdir(), `types-modified-${Date.now()}.ts`);
+    const original = fs.readFileSync(TYPES_FILE, 'utf-8');
+    fs.writeFileSync(tmpTypes, original + '\n// MODIFIED\n');
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'),
+        baselineShapeName: 'qwen-thinking.ts',
+        typesFilePath: tmpTypes,
+        expectShapeDiff: false,
+      });
+      expect(verdict.valid).toBe(false);
+      expect(verdict.violations.some(v => v.category === 'types_file_modified')).toBe(true);
+    } finally {
+      fs.unlinkSync(tmpTypes);
+    }
+  });
+
+  it('FAILS if MULTI_STEP_ACTION_CONTRACT bytes are modified', () => {
+    const tmpTypes = path.join(os.tmpdir(), `types-contract-modified-${Date.now()}.ts`);
+    const original = fs.readFileSync(TYPES_FILE, 'utf-8');
+    const modified = original.replace(
+      'Output exactly ONE JSON object',
+      'Output exactly TWO JSON objects',  // single-byte tweak in the contract
+    );
+    fs.writeFileSync(tmpTypes, modified);
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'),
+        baselineShapeName: 'qwen-thinking.ts',
+        typesFilePath: tmpTypes,
+        expectShapeDiff: false,
+      });
+      expect(verdict.valid).toBe(false);
+      expect(verdict.violations.some(v => v.category === 'multi_step_action_contract_modified')).toBe(true);
+    } finally {
+      fs.unlinkSync(tmpTypes);
+    }
+  });
+});
+
+describe('validateCandidate — shape file metadata + imports validation', () => {
+  it('FAILS if locked metadata field is missing from candidate', () => {
+    const tmpShape = path.join(os.tmpdir(), `qwen-thinking-bad-${Date.now()}.ts`);
+    const original = fs.readFileSync(path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'), 'utf-8');
+    // Strip out modelClass: line entirely. Use \r?\n to handle both LF and CRLF
+    // line endings — git on Windows may check out files with CRLF.
+    const stripped = original.replace(/modelClass:.*\r?\n/, '');
+    expect(stripped).not.toContain('modelClass:');  // sanity: stripping actually worked
+    fs.writeFileSync(tmpShape, stripped);
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: tmpShape,
+        baselineShapeName: 'qwen-thinking.ts',
+        typesFilePath: TYPES_FILE,
+        expectShapeDiff: true,
+      });
+      expect(verdict.valid).toBe(false);
+      expect(verdict.violations.some(v => v.category === 'shape_file_metadata_locked_field_modified')).toBe(true);
+    } finally {
+      fs.unlinkSync(tmpShape);
+    }
+  });
+
+  it('FAILS if imports block is removed', () => {
+    const tmpShape = path.join(os.tmpdir(), `qwen-thinking-noimport-${Date.now()}.ts`);
+    const original = fs.readFileSync(path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'), 'utf-8');
+    const stripped = original.replace(/from '\.\/types\.js'/, "from './SOMETHING_ELSE.js'");
+    fs.writeFileSync(tmpShape, stripped);
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: tmpShape,
+        baselineShapeName: 'qwen-thinking.ts',
+        typesFilePath: TYPES_FILE,
+        expectShapeDiff: true,
+      });
+      expect(verdict.valid).toBe(false);
+      expect(verdict.violations.some(v => v.category === 'shape_file_imports_modified')).toBe(true);
+    } finally {
+      fs.unlinkSync(tmpShape);
+    }
+  });
+
+  it('PASSES if candidate uses ../types.js (gepa-evolved/ subdir convention)', () => {
+    const tmpShape = path.join(os.tmpdir(), `qwen-thinking-subdir-${Date.now()}.ts`);
+    const original = fs.readFileSync(path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'), 'utf-8');
+    // Simulate gepa-evolved/ subdir candidate: ./types.js → ../types.js, body mutation
+    const subdirImport = original
+      .replace(/from '\.\/types\.js'/, "from '../types.js'")
+      .replace('Answer the question precisely', 'Answer the question precisely (mutation)');
+    fs.writeFileSync(tmpShape, subdirImport);
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: tmpShape,
+        baselineShapeName: 'qwen-thinking.ts',
+        typesFilePath: TYPES_FILE,
+        expectShapeDiff: true,
+      });
+      expect(verdict.valid).toBe(true);
+      expect(verdict.violations.some(v => v.category === 'shape_file_imports_modified')).toBe(false);
+    } finally {
+      fs.unlinkSync(tmpShape);
+    }
+  });
+});
+
+describe('validateCandidate — accepts valid Gen 1 mutation', () => {
+  it('Gen 1 candidate with body-only mutation passes validation', () => {
+    const tmpShape = path.join(os.tmpdir(), `qwen-thinking-mutation-${Date.now()}.ts`);
+    const original = fs.readFileSync(path.join(PROMPT_SHAPES_DIR, 'qwen-thinking.ts'), 'utf-8');
+    // Realistic-shape mutation: change a string in the body, keep imports + metadata + structure
+    const mutated = original.replace(
+      'Answer the question precisely and substantively',
+      'Answer the question precisely, substantively, and with explicit retrieval',
+    );
+    expect(mutated).not.toBe(original);  // sanity: mutation actually changed bytes
+    fs.writeFileSync(tmpShape, mutated);
+    try {
+      const verdict = validateCandidate({
+        candidateShapeFilePath: tmpShape,
+        baselineShapeName: 'qwen-thinking.ts',
+        typesFilePath: TYPES_FILE,
+        expectShapeDiff: true,
+      });
+      expect(verdict.valid).toBe(true);
+      expect(verdict.candidateShapeFileSha).not.toBe(BASELINE_SHAPE_SHAS['qwen-thinking.ts']);
+    } finally {
+      fs.unlinkSync(tmpShape);
+    }
+  });
+});

benchmarks/gepa/tests/faza-1/__faza1-closed/registry-injection.test.ts

@@ -0,0 +1,154 @@
+/**
+ * GEPA Faza 1 — REGISTRY-injection cross-module-boundary regression test.
+ *
+ * Per manifest v7 Amendment 8 §registry_invariant_test.
+ *
+ * Documents the H1 failure mode discovered via Gen 1 partial run b5avslp51 +
+ * diagnostic probe (benchmarks/gepa/scripts/faza-1/probe-registry-injection.ts):
+ *
+ *   Under tsx + Node ESM with workspace path resolution, importing REGISTRY
+ *   via a deep relative path produces a SEPARATE module instance from
+ *   importing via the package path '@waggle/agent'. Mutations to one
+ *   instance do NOT propagate to the other.
+ *
+ * This test asserts BOTH:
+ *   (a) the failure mode (direct deep-path mutation does NOT propagate
+ *       to package-import REGISTRY), so the bug class stays detectable
+ *       if someone "fixes" the canonical path back to a deep import; AND
+ *   (b) registerShape() via '@waggle/agent' DOES propagate to the
+ *       agent-loop's view of REGISTRY (selectShape sees the new shape).
+ *
+ * If this test ever fails on (a) it means the underlying ESM resolver
+ * dedup-logic changed; if it ever fails on (b) it means registerShape
+ * was broken or its export path was changed.
+ */
+
+import { describe, expect, it } from 'vitest';
+
+// Path A — deep relative import (the failing pattern from b5avslp51)
+import { REGISTRY as RegistryFromScriptDeepPath } from '../../../../packages/agent/src/prompt-shapes/selector.js';
+
+// Path B — package import (the canonical, agent-loop-equivalent path)
+import {
+  REGISTRY as RegistryFromPackage,
+  registerShape,
+  selectShape,
+  type PromptShape,
+} from '@waggle/agent';
+
+function makeProbeShape(name: string): PromptShape {
+  // Minimal valid PromptShape stub — only structure matters for the registry test;
+  // method bodies are not invoked here.
+  return {
+    name,
+    metadata: {
+      description: `Probe shape ${name} for registry-injection regression test`,
+      modelClass: 'probe',
+      defaultThinking: false,
+      defaultMaxTokens: 100,
+      evidence_link: 'manifest v7 Amendment 8 §registry_invariant_test',
+    },
+    systemPrompt: () => 'probe',
+    soloUserPrompt: () => 'probe',
+    multiStepKickoffUserPrompt: () => 'probe',
+    retrievalInjectionUserPrompt: () => 'probe',
+  } as PromptShape;
+}
+
+describe('Amendment 8 §registry_invariant_test — REGISTRY cross-module-boundary', () => {
+  it('documents H1 failure mode: deep-relative-path REGISTRY and package REGISTRY are SEPARATE module instances', () => {
+    // This assertion documents the empirical finding from probe-registry-injection.ts
+    // run on 2026-04-28 (Node v22.19.0 + tsx). If the underlying ESM resolver ever
+    // deduplicates these paths, this test will fail-and-flag the change.
+    expect(RegistryFromScriptDeepPath).not.toBe(RegistryFromPackage);
+  });
+
+  it('documents H1: direct deep-path mutation does NOT propagate to package-import REGISTRY', () => {
+    const shapeName = 'amendment-8-h1-failure-mode-witness';
+    const shape = makeProbeShape(shapeName);
+
+    // Anti-pattern (the b5avslp51 bug): direct mutation via deep-path import.
+    (RegistryFromScriptDeepPath as Record<string, PromptShape>)[shapeName] = shape;
+
+    // Direct read on the same instance: visible.
+    expect(RegistryFromScriptDeepPath[shapeName]).toBe(shape);
+
+    // Read via the package-import (agent-loop's view): NOT visible.
+    expect(RegistryFromPackage[shapeName]).toBeUndefined();
+
+    // selectShape (from package, mirrors agent-loop call site): throws.
+    expect(() => selectShape('any-alias', { override: shapeName })).toThrow(
+      /not in REGISTRY/,
+    );
+
+    // Cleanup — remove the failure-mode witness so subsequent tests stay clean.
+    delete (RegistryFromScriptDeepPath as Record<string, PromptShape>)[shapeName];
+  });
+
+  it('FIX: registerShape() via @waggle/agent DOES propagate (canonical mutation API per §canonical_mutation_api)', () => {
+    const shapeName = 'amendment-8-canonical-fix-witness';
+    const shape = makeProbeShape(shapeName);
+
+    // Canonical mutation: registerShape imported from '@waggle/agent'.
+    registerShape(shapeName, shape);
+
+    // Read via the package-import (agent-loop's view): visible.
+    expect(RegistryFromPackage[shapeName]).toBe(shape);
+
+    // selectShape with override (matches agent-loop's selectShape call site).
+    const found = selectShape('any-alias', { override: shapeName });
+    expect(found).toBe(shape);
+    expect(found.name).toBe(shapeName);
+
+    // Cleanup — Faza 1 doesn't expose unregisterShape, so we mutate via canonical
+    // Path-B REGISTRY directly to keep cross-test isolation.
+    delete (RegistryFromPackage as Record<string, PromptShape>)[shapeName];
+  });
+
+  it('registerShape rejects empty name with informative error', () => {
+    const shape = makeProbeShape('temp');
+    expect(() => registerShape('', shape)).toThrow(/non-empty string/);
+  });
+
+  it('registerShape rejects malformed shape (missing systemPrompt method)', () => {
+    const malformed = {
+      name: 'malformed',
+      metadata: { description: 'nope', modelClass: 'x', defaultThinking: false, defaultMaxTokens: 1 },
+      // systemPrompt deliberately missing
+    } as unknown as PromptShape;
+    expect(() => registerShape('malformed-test', malformed)).toThrow(
+      /missing required PromptShape fields/,
+    );
+  });
+
+  it('registerShape registration survives multiple re-registrations (last-write-wins semantics)', () => {
+    const name = 'amendment-8-multi-register';
+    const shape1 = makeProbeShape(name);
+    const shape2 = makeProbeShape(name);
+    // Distinct identity but same name.
+    expect(shape1).not.toBe(shape2);
+
+    registerShape(name, shape1);
+    expect(selectShape('x', { override: name })).toBe(shape1);
+
+    registerShape(name, shape2);  // re-register
+    expect(selectShape('x', { override: name })).toBe(shape2);
+
+    delete (RegistryFromPackage as Record<string, PromptShape>)[name];
+  });
+
+  it('registerShape via @waggle/agent makes the shape visible from listShapes()', async () => {
+    const name = 'amendment-8-listshapes-witness';
+    const shape = makeProbeShape(name);
+
+    const { listShapes } = await import('@waggle/agent');
+    const beforeNames = listShapes();
+    expect(beforeNames).not.toContain(name);
+
+    registerShape(name, shape);
+    const afterNames = listShapes();
+    expect(afterNames).toContain(name);
+
+    delete (RegistryFromPackage as Record<string, PromptShape>)[name];
+  });
+});

benchmarks/gepa/tests/faza-1/acceptance.test.ts

@@ -0,0 +1,263 @@
+/**
+ * GEPA Faza 1 — acceptance validator tests.
+ *
+ * Coverage targets per manifest v7 §amendment_2_integration.scaffold_test_coverage_NEW_requirements
+ * mandatory_acceptance_tests:
+ *   - §F.5 FAIL: Qwen candidate with trio_strict delta = +6pp AND mean retrieval = 1.4 → REJECTED
+ *   - §F.5 PASS path: Qwen candidate with trio_strict delta = +6pp AND mean retrieval = 1.7 → ACCEPTED
+ *
+ * Plus comprehensive coverage of §F condition 1 (third update) for both Qwen and non-Qwen branches.
+ */
+
+import { describe, expect, it } from 'vitest';
+import {
+  evaluateCandidate,
+  TRIO_STRICT_DELTA_THRESHOLD_PP,
+  QWEN_RETRIEVAL_ENGAGEMENT_FLOOR,
+  QWEN_FALSE_POSITIVE_RETRIEVAL_FLOOR,
+} from '../../src/faza-1/acceptance.js';
+import { type CandidateMetrics, type ShapeName } from '../../src/faza-1/types.js';
+
+function makeCandidate(overrides: Partial<CandidateMetrics> & Pick<CandidateMetrics, 'shape'>): CandidateMetrics {
+  return {
+    candidateId: overrides.candidateId ?? `${overrides.shape}-test-candidate`,
+    shape: overrides.shape,
+    evaluations: overrides.evaluations ?? [],
+    trioStrictPassRateII: overrides.trioStrictPassRateII ?? 0.5,
+    trioStrictPassRateI: overrides.trioStrictPassRateI ?? 0.5,
+    meanRetrievalCallsPerTask: overrides.meanRetrievalCallsPerTask ?? 1.5,
+    meanCostUsd: overrides.meanCostUsd ?? 0.5,
+  };
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// MANDATORY: §F.5 false-positive guard tests per Amendment 2 §5
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('§F.5 false-positive evolution guard — mandatory Amendment 2 acceptance tests', () => {
+  it('Qwen candidate, trio_strict delta = +6pp, mean retrieval = 1.4 → REJECTED', () => {
+    // baseline = 0.20, candidate = 0.26 → delta = +6pp ≥ 5pp threshold
+    // retrieval = 1.4 < 1.5 false-positive floor → §F.5 triggers
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.26,
+      meanRetrievalCallsPerTask: 1.4,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+
+    expect(verdict.trioStrictDeltaPP).toBeCloseTo(6.0, 6);
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(true);
+    expect(verdict.accepted).toBe(false);
+    expect(verdict.reason).toContain('REJECTED §F.5 false-positive guard');
+  });
+
+  it('Qwen candidate, trio_strict delta = +6pp, mean retrieval = 1.7 → ACCEPTED', () => {
+    // baseline = 0.20, candidate = 0.26 → delta = +6pp
+    // retrieval = 1.7 ≥ 1.7 floor (engagement gap closed) → §F.5 does NOT trigger
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.26,
+      meanRetrievalCallsPerTask: 1.7,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+
+    expect(verdict.trioStrictDeltaPP).toBeCloseTo(6.0, 6);
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(false);
+    expect(verdict.condition1Pass).toBe(true);
+    expect(verdict.accepted).toBe(true);
+    expect(verdict.reason).toContain('PASS §F.1');
+  });
+
+  it('§F.5 boundary: retrieval = 1.49 → REJECTED (just below floor)', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-non-thinking',
+      trioStrictPassRateII: 0.30,  // delta = +10pp
+      meanRetrievalCallsPerTask: 1.49,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(true);
+    expect(verdict.accepted).toBe(false);
+  });
+
+  it('§F.5 boundary: retrieval = 1.50 → §F.5 NOT triggered (exact floor inclusive)', () => {
+    // 1.50 ≥ 1.50 false-positive floor → guard does not fire
+    // But 1.50 < 1.70 §F.1 Qwen floor → condition 1 still fails on different criterion
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.30,  // delta = +10pp
+      meanRetrievalCallsPerTask: 1.50,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(false);
+    expect(verdict.condition1Pass).toBe(false);  // fails Qwen retrieval floor 1.7
+    expect(verdict.accepted).toBe(false);
+    expect(verdict.reason).toContain('FAIL §F.1 Qwen retrieval floor');
+  });
+
+  it('§F.5 does NOT trigger when delta < threshold even if retrieval low (Qwen)', () => {
+    // delta = +3pp < 5pp threshold → guard not even evaluated
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.23,
+      meanRetrievalCallsPerTask: 1.0,  // low, but delta below threshold
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(false);
+    expect(verdict.condition1Pass).toBe(false);
+    expect(verdict.accepted).toBe(false);
+    expect(verdict.reason).toContain('FAIL §F.1 trio_strict delta');
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// §F.5 does NOT apply to non-Qwen shapes
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('§F.5 scoping — only applies to Qwen-targeted shapes', () => {
+  it('claude shape with delta = +6pp + retrieval = 0.5 → ACCEPTED (no false-positive guard)', () => {
+    const candidate = makeCandidate({
+      shape: 'claude',
+      trioStrictPassRateII: 0.26,
+      meanRetrievalCallsPerTask: 0.5,  // would trigger §F.5 if Qwen, but doesn't apply here
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(false);
+    expect(verdict.condition1Pass).toBe(true);
+    expect(verdict.accepted).toBe(true);
+  });
+
+  it('gpt shape with delta = +5pp + retrieval = 0 → ACCEPTED', () => {
+    const candidate = makeCandidate({
+      shape: 'gpt',
+      trioStrictPassRateII: 0.25,
+      meanRetrievalCallsPerTask: 0,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(false);
+    expect(verdict.accepted).toBe(true);
+  });
+
+  it('generic-simple shape: only trio_strict delta matters, no retrieval requirement', () => {
+    const candidate = makeCandidate({
+      shape: 'generic-simple',
+      trioStrictPassRateII: 0.30,
+      meanRetrievalCallsPerTask: 1.0,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.accepted).toBe(true);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// §F condition 1 trio_strict delta — basic threshold tests
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('§F condition 1 — trio_strict delta threshold (≥+5pp)', () => {
+  it('delta = +5pp exactly (boundary inclusive) → condition 1 PASS for non-Qwen', () => {
+    const candidate = makeCandidate({
+      shape: 'claude',
+      trioStrictPassRateII: 0.25,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.trioStrictDeltaPP).toBeCloseTo(5.0, 6);
+    expect(verdict.condition1Pass).toBe(true);
+    expect(verdict.accepted).toBe(true);
+  });
+
+  it('delta = +4.99pp (just below boundary) → condition 1 FAIL', () => {
+    const candidate = makeCandidate({
+      shape: 'claude',
+      trioStrictPassRateII: 0.2499,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition1Pass).toBe(false);
+    expect(verdict.accepted).toBe(false);
+  });
+
+  it('negative delta (regression) → condition 1 FAIL', () => {
+    const candidate = makeCandidate({
+      shape: 'gpt',
+      trioStrictPassRateII: 0.10,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.trioStrictDeltaPP).toBeCloseTo(-10.0, 6);
+    expect(verdict.condition1Pass).toBe(false);
+  });
+
+  it('exposes binding constants for external auditing', () => {
+    expect(TRIO_STRICT_DELTA_THRESHOLD_PP).toBe(5);
+    expect(QWEN_RETRIEVAL_ENGAGEMENT_FLOOR).toBe(1.7);
+    expect(QWEN_FALSE_POSITIVE_RETRIEVAL_FLOOR).toBe(1.5);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// §F condition 1 — Qwen-only retrieval engagement floor (1.7)
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('§F condition 1 — Qwen retrieval engagement floor (1.7) sub-criterion', () => {
+  it('Qwen candidate, delta = +5pp, retrieval = 1.69 → FAIL (below 1.7 floor)', () => {
+    // 1.69 ≥ 1.5 false-positive floor (so §F.5 does not trigger)
+    // but 1.69 < 1.7 §F.1 Qwen floor (so condition 1 fails)
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.25,
+      meanRetrievalCallsPerTask: 1.69,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition5FalsePositiveGuardTriggered).toBe(false);
+    expect(verdict.condition1Pass).toBe(false);
+    expect(verdict.accepted).toBe(false);
+    expect(verdict.reason).toContain('FAIL §F.1 Qwen retrieval floor');
+  });
+
+  it('Qwen candidate, delta = +5pp, retrieval = 1.70 → PASS (exact floor inclusive)', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.25,
+      meanRetrievalCallsPerTask: 1.70,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.condition1Pass).toBe(true);
+    expect(verdict.accepted).toBe(true);
+  });
+
+  it('Qwen candidate, delta = +5pp, retrieval = 2.5 → PASS (well above floor)', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-non-thinking',
+      trioStrictPassRateII: 0.25,
+      meanRetrievalCallsPerTask: 2.5,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.accepted).toBe(true);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Audit log invariants
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('audit log invariants', () => {
+  it('reason string includes shape, delta, retrieval, accepted flag', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.30,
+      meanRetrievalCallsPerTask: 2.0,
+    });
+    const verdict = evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.20 });
+    expect(verdict.reason).toContain('shape=qwen-thinking');
+    expect(verdict.reason).toContain('trio_strict_delta=10.00pp');
+    expect(verdict.reason).toContain('mean_retrieval_calls=2.00');
+    expect(verdict.reason).toContain('accepted=true');
+  });
+
+  it('every shape produces a verdict (no exceptions)', () => {
+    const shapes: ShapeName[] = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'];
+    for (const shape of shapes) {
+      const candidate = makeCandidate({ shape, trioStrictPassRateII: 0.5 });
+      expect(() =>
+        evaluateCandidate({ candidate, baselineTrioStrictPassRateII: 0.4 }),
+      ).not.toThrow();
+    }
+  });
+});

benchmarks/gepa/tests/faza-1/corpus.test.ts

@@ -0,0 +1,349 @@
+/**
+ * GEPA Faza 1 — H3 corpus library tests.
+ *
+ * Coverage targets per manifest v7 §corpus_design + §amendment_2_integration:
+ *   - Stratification: exactly 50 unique cells (5 × 5 × 2)
+ *   - Deterministic enumeration order (canonical)
+ *   - Instance validation per quality floor
+ *   - Spot-audit sampler determinism (same seed → same sample)
+ *   - Spot-audit halt-on-failure semantics
+ */
+
+import { describe, expect, it } from 'vitest';
+import {
+  TASK_FAMILIES,
+  PERSONAS,
+  COMPANY_STAGES,
+  TOTAL_INSTANCES,
+  DOCS_PER_INSTANCE_MIN,
+  DOCS_PER_INSTANCE_MAX,
+  SPOT_AUDIT_SAMPLE_SIZE,
+  STRATIFICATION_SEED,
+  TASK_FAMILY_DESCRIPTORS,
+  type StratificationCell,
+  type CorpusInstance,
+  iterateStratificationCells,
+  listStratificationCells,
+  buildInstanceId,
+  validateInstance,
+  deterministicSample,
+  selectSpotAuditSample,
+  runSpotAudit,
+  corpusSha256,
+} from '../../src/faza-1/corpus.js';
+
+// ───────────────────────────────────────────────────────────────────────────
+// Helpers
+// ───────────────────────────────────────────────────────────────────────────
+
+function makeValidInstance(cell: StratificationCell, ordinal: number = 1): CorpusInstance {
+  const docs = Array.from({ length: 7 }, (_, i) => ({
+    title: `DOC ${i + 1} — Sample`,
+    body: 'x'.repeat(800),
+    charCount: 800,
+  }));
+  return {
+    instanceId: buildInstanceId(cell, ordinal),
+    cell,
+    personaText: 'p'.repeat(200),
+    scenario: 's'.repeat(400),
+    sourceDocuments: docs,
+    question: 'q'.repeat(200),
+    materialsConcat: docs.map(d => `## ${d.title}\n\n${d.body}`).join('\n\n---\n\n'),
+    manifestAnchor: 'manifest-v7-gepa-faza1',
+    generatedBy: 'claude-opus-4-7',
+    generatedAtIso: '2026-04-28T00:00:00.000Z',
+    generationCostUsd: 0.10,
+  };
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// Constants exposed for auditing
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('manifest v7 §corpus_design constants', () => {
+  it('TOTAL_INSTANCES = 50', () => {
+    expect(TOTAL_INSTANCES).toBe(50);
+  });
+  it('TASK_FAMILIES has 5 entries', () => {
+    expect(TASK_FAMILIES).toHaveLength(5);
+  });
+  it('PERSONAS has 5 entries', () => {
+    expect(PERSONAS).toHaveLength(5);
+  });
+  it('COMPANY_STAGES has 2 entries', () => {
+    expect(COMPANY_STAGES).toHaveLength(2);
+  });
+  it('5 × 5 × 2 = 50 (stratification yields TOTAL_INSTANCES)', () => {
+    expect(TASK_FAMILIES.length * PERSONAS.length * COMPANY_STAGES.length).toBe(TOTAL_INSTANCES);
+  });
+  it('SPOT_AUDIT_SAMPLE_SIZE = 5 per Amendment 1', () => {
+    expect(SPOT_AUDIT_SAMPLE_SIZE).toBe(5);
+  });
+  it('STRATIFICATION_SEED = 42', () => {
+    expect(STRATIFICATION_SEED).toBe(42);
+  });
+  it('DOCS_PER_INSTANCE bounds', () => {
+    expect(DOCS_PER_INSTANCE_MIN).toBe(6);
+    expect(DOCS_PER_INSTANCE_MAX).toBe(8);
+  });
+  it('TASK_FAMILY_DESCRIPTORS covers all 5 families', () => {
+    for (const f of TASK_FAMILIES) {
+      expect(TASK_FAMILY_DESCRIPTORS[f]).toBeDefined();
+      expect(TASK_FAMILY_DESCRIPTORS[f].docsPerInstance).toBeGreaterThanOrEqual(DOCS_PER_INSTANCE_MIN);
+      expect(TASK_FAMILY_DESCRIPTORS[f].docsPerInstance).toBeLessThanOrEqual(DOCS_PER_INSTANCE_MAX);
+    }
+  });
+  it('F1-F3 mirror pilot tasks; F4-F5 are net-new', () => {
+    expect(TASK_FAMILY_DESCRIPTORS.F1.mirrorPilotTask).toBe('task-1');
+    expect(TASK_FAMILY_DESCRIPTORS.F2.mirrorPilotTask).toBe('task-2');
+    expect(TASK_FAMILY_DESCRIPTORS.F3.mirrorPilotTask).toBe('task-3');
+    expect(TASK_FAMILY_DESCRIPTORS.F4.mirrorPilotTask).toBeNull();
+    expect(TASK_FAMILY_DESCRIPTORS.F5.mirrorPilotTask).toBeNull();
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Stratification enumeration
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('iterateStratificationCells / listStratificationCells', () => {
+  it('yields exactly 50 cells (5 × 5 × 2)', () => {
+    const cells = listStratificationCells();
+    expect(cells).toHaveLength(50);
+  });
+
+  it('all 50 cells are unique', () => {
+    const cells = listStratificationCells();
+    const keys = new Set(cells.map(c => `${c.family}|${c.persona}|${c.stage}`));
+    expect(keys.size).toBe(50);
+  });
+
+  it('canonical ordering: F1 first, F5 last', () => {
+    const cells = listStratificationCells();
+    expect(cells[0].family).toBe('F1');
+    expect(cells[cells.length - 1].family).toBe('F5');
+  });
+
+  it('persona enumeration is the inner loop after family', () => {
+    const cells = listStratificationCells();
+    // First 10 cells should all be family F1
+    for (let i = 0; i < 10; i++) {
+      expect(cells[i].family).toBe('F1');
+    }
+    // Cells 10-19 should all be F2
+    for (let i = 10; i < 20; i++) {
+      expect(cells[i].family).toBe('F2');
+    }
+  });
+
+  it('each (family, persona) pair appears exactly twice (once per stage)', () => {
+    const cells = listStratificationCells();
+    const pairCounts = new Map<string, number>();
+    for (const c of cells) {
+      const key = `${c.family}|${c.persona}`;
+      pairCounts.set(key, (pairCounts.get(key) ?? 0) + 1);
+    }
+    expect(pairCounts.size).toBe(25);  // 5 × 5 family-persona pairs
+    for (const count of pairCounts.values()) {
+      expect(count).toBe(2);  // once per stage
+    }
+  });
+
+  it('iterator is deterministic (same yield order across calls)', () => {
+    const a = listStratificationCells();
+    const b = listStratificationCells();
+    expect(a).toEqual(b);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// buildInstanceId
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('buildInstanceId', () => {
+  it('produces stable IDs with h3- prefix + zero-padded ordinal', () => {
+    const id = buildInstanceId({ family: 'F1', persona: 'p2_cfo', stage: 'stage_a_series_b_growth_burning' }, 7);
+    expect(id).toBe('h3-F1-p2_cfo-stage_a_series_b_growth_burning-007');
+  });
+
+  it('default ordinal is 1', () => {
+    const id = buildInstanceId({ family: 'F3', persona: 'p4_vp_finance', stage: 'stage_b_post_profitable_consolidation' });
+    expect(id).toContain('-001');
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// validateInstance
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('validateInstance — quality floor', () => {
+  it('valid synthetic instance passes', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(true);
+    expect(r.violations).toHaveLength(0);
+  });
+
+  it('FAIL: too few docs', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.sourceDocuments = inst.sourceDocuments.slice(0, 3);
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(false);
+    expect(r.violations[0]).toContain('docs count');
+    expect(r.violations[0]).toContain('< 6 min');
+  });
+
+  it('FAIL: too many docs', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.sourceDocuments = [...inst.sourceDocuments, ...inst.sourceDocuments, ...inst.sourceDocuments];
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(false);
+    expect(r.violations[0]).toContain('> 8 max');
+  });
+
+  it('FAIL: persona too short', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.personaText = 'short';
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(false);
+    expect(r.violations.some(v => v.includes('persona length'))).toBe(true);
+  });
+
+  it('PASS: rich persona up to 1500 chars (post-probe loosening)', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.personaText = 'p'.repeat(1400);
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(true);
+  });
+
+  it('FAIL: persona above 1500 cap', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.personaText = 'p'.repeat(1600);
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(false);
+    expect(r.violations.some(v => v.includes('persona length'))).toBe(true);
+  });
+
+  it('PASS: empty scenario (oracle embedded it in personaText)', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.scenario = '';  // embedded case
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(true);
+  });
+
+  it('FAIL: doc charCount mismatch with body length', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.sourceDocuments[0].charCount = 999;  // intentional mismatch
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(false);
+    expect(r.violations.some(v => v.includes('charCount'))).toBe(true);
+  });
+
+  it('FAIL: instanceId missing h3- prefix', () => {
+    const inst = makeValidInstance({ family: 'F1', persona: 'p1_founder_ceo', stage: 'stage_a_series_b_growth_burning' });
+    inst.instanceId = 'wrong-prefix-001';
+    const r = validateInstance(inst);
+    expect(r.valid).toBe(false);
+    expect(r.violations.some(v => v.includes('h3- prefix'))).toBe(true);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// deterministicSample / selectSpotAuditSample
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('deterministicSample', () => {
+  it('returns sample of requested size', () => {
+    const items = Array.from({ length: 50 }, (_, i) => `item-${i}`);
+    const sample = deterministicSample(items, 5);
+    expect(sample).toHaveLength(5);
+  });
+
+  it('same seed produces identical sample (reproducibility)', () => {
+    const items = Array.from({ length: 50 }, (_, i) => `item-${i}`);
+    const a = deterministicSample(items, 5, 42);
+    const b = deterministicSample(items, 5, 42);
+    expect(a).toEqual(b);
+  });
+
+  it('different seeds produce different samples', () => {
+    const items = Array.from({ length: 50 }, (_, i) => `item-${i}`);
+    const a = deterministicSample(items, 5, 42);
+    const c = deterministicSample(items, 5, 100);
+    expect(a).not.toEqual(c);
+  });
+
+  it('sampleSize >= items.length returns full list copy', () => {
+    const items = ['a', 'b', 'c'];
+    const sample = deterministicSample(items, 5);
+    expect(sample).toHaveLength(3);
+    expect(sample).toEqual(items);
+    expect(sample).not.toBe(items);  // copy, not reference
+  });
+});
+
+describe('selectSpotAuditSample', () => {
+  it('returns 5 instances by default (manifest v7 spot_audit.sample_size)', () => {
+    const cells = listStratificationCells();
+    const instances = cells.map(c => makeValidInstance(c));
+    const sample = selectSpotAuditSample(instances);
+    expect(sample).toHaveLength(5);
+  });
+
+  it('reproducible with seed=42 (same instances picked across runs)', () => {
+    const cells = listStratificationCells();
+    const instances = cells.map(c => makeValidInstance(c));
+    const a = selectSpotAuditSample(instances);
+    const b = selectSpotAuditSample(instances);
+    expect(a.map(i => i.instanceId)).toEqual(b.map(i => i.instanceId));
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// runSpotAudit aggregate
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('runSpotAudit', () => {
+  it('PASS when all sampled instances valid', () => {
+    const cells = listStratificationCells();
+    const instances = cells.map(c => makeValidInstance(c));
+    const report = runSpotAudit(instances);
+    expect(report.sampleSize).toBe(5);
+    expect(report.haltOnFailure).toBe(false);
+    expect(report.haltReason).toBeUndefined();
+  });
+
+  it('HALT when any sampled instance invalid (manifest v7 spot_audit.halt_on)', () => {
+    const cells = listStratificationCells();
+    const instances = cells.map(c => makeValidInstance(c));
+    // Corrupt every instance (so sample will definitely include corrupted ones)
+    for (const inst of instances) {
+      inst.sourceDocuments = inst.sourceDocuments.slice(0, 2);  // below 6 min
+    }
+    const report = runSpotAudit(instances);
+    expect(report.haltOnFailure).toBe(true);
+    expect(report.haltReason).toMatch(/spot-audit instances failed validation/);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// corpusSha256
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('corpusSha256', () => {
+  it('produces deterministic SHA across runs', () => {
+    const cells = listStratificationCells().slice(0, 5);
+    const instances = cells.map(c => makeValidInstance(c));
+    const a = corpusSha256(instances);
+    const b = corpusSha256(instances);
+    expect(a).toBe(b);
+  });
+
+  it('different corpora produce different SHAs', () => {
+    const cells = listStratificationCells();
+    const a = cells.slice(0, 5).map(c => makeValidInstance(c));
+    const b = cells.slice(5, 10).map(c => makeValidInstance(c));
+    expect(corpusSha256(a)).not.toBe(corpusSha256(b));
+  });
+});

benchmarks/gepa/tests/faza-1/cost-tracker.test.ts

@@ -0,0 +1,180 @@
+/**
+ * GEPA Faza 1 — cost tracker tests.
+ *
+ * Coverage targets:
+ *   - Halt triggers: HARD_CAP_USD_BREACH ($100), INTERNAL_HALT_USD_BREACH ($80),
+ *     SUPER_LINEAR_PROJECTION_BREACH (>30% over expected)
+ *   - Audit cadence (every 20 evaluations)
+ *   - Immutable state updates per coding-style.md
+ *   - Projection multiplier 1.5× per brief §6.7
+ */
+
+import { describe, expect, it } from 'vitest';
+import {
+  HARD_CAP_USD,
+  INTERNAL_HALT_USD,
+  SUPER_LINEAR_MULTIPLIER,
+  SUPER_LINEAR_OVERAGE_THRESHOLD,
+  AUDIT_CADENCE_EVAL_COUNT,
+  createCostTracker,
+  recordEvaluation,
+  checkHaltTriggers,
+  shouldAudit,
+} from '../../src/faza-1/cost-tracker.js';
+
+describe('constants exposed for auditing', () => {
+  it('HARD_CAP_USD = $100', () => {
+    expect(HARD_CAP_USD).toBe(100.0);
+  });
+  it('INTERNAL_HALT_USD = $80', () => {
+    expect(INTERNAL_HALT_USD).toBe(80.0);
+  });
+  it('SUPER_LINEAR_MULTIPLIER = 1.5 per brief §6.7', () => {
+    expect(SUPER_LINEAR_MULTIPLIER).toBe(1.5);
+  });
+  it('SUPER_LINEAR_OVERAGE_THRESHOLD = 0.30 (30%)', () => {
+    expect(SUPER_LINEAR_OVERAGE_THRESHOLD).toBe(0.30);
+  });
+  it('AUDIT_CADENCE_EVAL_COUNT = 20 per launch decision §A.7', () => {
+    expect(AUDIT_CADENCE_EVAL_COUNT).toBe(20);
+  });
+});
+
+describe('createCostTracker', () => {
+  it('initializes with zero spend + projection = baseline × 1.5', () => {
+    const t = createCostTracker(0.50);
+    expect(t.cumulativeUsd).toBe(0);
+    expect(t.evaluationCount).toBe(0);
+    expect(t.projectionPerEvalUsd).toBe(0.75);  // 0.50 × 1.5
+  });
+});
+
+describe('recordEvaluation — immutable updates', () => {
+  it('returns new state object (does not mutate input)', () => {
+    const before = createCostTracker(0.50);
+    const after = recordEvaluation(before, 0.30);
+    expect(before.cumulativeUsd).toBe(0);
+    expect(before.evaluationCount).toBe(0);
+    expect(after.cumulativeUsd).toBe(0.30);
+    expect(after.evaluationCount).toBe(1);
+    expect(before).not.toBe(after);
+  });
+
+  it('cumulative cost accumulates across multiple recordings', () => {
+    let s = createCostTracker(0.50);
+    s = recordEvaluation(s, 0.40);
+    s = recordEvaluation(s, 0.60);
+    s = recordEvaluation(s, 0.50);
+    expect(s.cumulativeUsd).toBeCloseTo(1.50, 6);
+    expect(s.evaluationCount).toBe(3);
+  });
+});
+
+describe('checkHaltTriggers — HARD_CAP_USD_BREACH', () => {
+  it('triggers at $100.01', () => {
+    const s = { cumulativeUsd: 100.01, evaluationCount: 200, projectionPerEvalUsd: 0.50 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('HARD_CAP_USD_BREACH');
+    expect(r.message).toContain('HARD CAP BREACH');
+  });
+
+  it('does NOT trigger at $100.00 exactly (boundary inclusive of pass)', () => {
+    const s = { cumulativeUsd: 100.00, evaluationCount: 200, projectionPerEvalUsd: 0.50 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).not.toBe('HARD_CAP_USD_BREACH');
+    // It will trigger INTERNAL_HALT since $100 > $80, but not HARD_CAP
+    expect(r.haltReason).toBe('INTERNAL_HALT_USD_BREACH');
+  });
+
+  it('takes precedence over INTERNAL_HALT (most severe first)', () => {
+    const s = { cumulativeUsd: 105.0, evaluationCount: 200, projectionPerEvalUsd: 0.50 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('HARD_CAP_USD_BREACH');
+  });
+});
+
+describe('checkHaltTriggers — INTERNAL_HALT_USD_BREACH', () => {
+  it('triggers at $80.01', () => {
+    const s = { cumulativeUsd: 80.01, evaluationCount: 160, projectionPerEvalUsd: 0.50 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('INTERNAL_HALT_USD_BREACH');
+  });
+
+  it('does NOT trigger at $80.00 exactly', () => {
+    const s = { cumulativeUsd: 80.00, evaluationCount: 160, projectionPerEvalUsd: 0.50 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).not.toBe('INTERNAL_HALT_USD_BREACH');
+  });
+});
+
+describe('checkHaltTriggers — SUPER_LINEAR_PROJECTION_BREACH', () => {
+  it('triggers when actual exceeds expected by >30%', () => {
+    // 10 evals × $0.75/eval projection = $7.50 expected; actual $10 = 33% over
+    const s = { cumulativeUsd: 10.0, evaluationCount: 10, projectionPerEvalUsd: 0.75 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('SUPER_LINEAR_PROJECTION_BREACH');
+    expect(r.overageFraction).toBeCloseTo(0.333, 2);
+  });
+
+  it('does NOT trigger when actual is exactly at projection', () => {
+    const s = { cumulativeUsd: 7.50, evaluationCount: 10, projectionPerEvalUsd: 0.75 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('NONE');
+    expect(r.overageFraction).toBe(0);
+  });
+
+  it('does NOT trigger when overage is exactly at 30% threshold (boundary inclusive of pass)', () => {
+    // Expected $7.50, actual $9.75 = 30% over exactly
+    const s = { cumulativeUsd: 9.75, evaluationCount: 10, projectionPerEvalUsd: 0.75 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('NONE');
+  });
+
+  it('does NOT trigger before any eval recorded (no expected baseline)', () => {
+    const s = { cumulativeUsd: 0, evaluationCount: 0, projectionPerEvalUsd: 0.75 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('NONE');
+  });
+
+  it('takes precedence below INTERNAL_HALT (super-linear can fire while still under $80)', () => {
+    // Expected $1.50 at 2 evals × $0.75; actual $5 = 233% over → super-linear breach
+    const s = { cumulativeUsd: 5.0, evaluationCount: 2, projectionPerEvalUsd: 0.75 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('SUPER_LINEAR_PROJECTION_BREACH');
+    expect(r.cumulativeUsd).toBeLessThan(INTERNAL_HALT_USD);
+  });
+});
+
+describe('shouldAudit — audit cadence', () => {
+  it('returns false at evaluationCount = 0', () => {
+    expect(shouldAudit({ cumulativeUsd: 0, evaluationCount: 0, projectionPerEvalUsd: 0.5 })).toBe(false);
+  });
+
+  it('returns true at evaluationCount = 20 (first audit boundary)', () => {
+    expect(shouldAudit({ cumulativeUsd: 10, evaluationCount: 20, projectionPerEvalUsd: 0.5 })).toBe(true);
+  });
+
+  it('returns true at every multiple of 20', () => {
+    for (const n of [40, 60, 80, 100, 200]) {
+      expect(shouldAudit({ cumulativeUsd: n / 2, evaluationCount: n, projectionPerEvalUsd: 0.5 })).toBe(true);
+    }
+  });
+
+  it('returns false at non-boundary counts', () => {
+    for (const n of [1, 5, 19, 21, 39, 99]) {
+      expect(shouldAudit({ cumulativeUsd: n / 2, evaluationCount: n, projectionPerEvalUsd: 0.5 })).toBe(false);
+    }
+  });
+});
+
+describe('end-to-end Faza 1 cost projection', () => {
+  it('expected total $100.50 reaches HARD_CAP_USD_BREACH (Amendment 1 §4 tight margin)', () => {
+    // Simulate Faza 1 expected breakdown reaching $100.50
+    let s = createCostTracker(0.50);  // baseline $0.50/eval
+    // 50 corpus + 40 NULL + 120 Gen 1 + 25 held-out = 235 evaluations × roughly $0.43/eval
+    // For test purposes just simulate hitting $100.50
+    s = { ...s, cumulativeUsd: 100.50, evaluationCount: 235 };
+    const r = checkHaltTriggers(s);
+    expect(r.haltReason).toBe('HARD_CAP_USD_BREACH');
+  });
+});

benchmarks/gepa/tests/faza-1/fitness.test.ts

@@ -0,0 +1,595 @@
+/**
+ * GEPA Faza 1 — fitness function tests.
+ *
+ * Coverage targets per manifest v7 §amendment_2_integration.scaffold_test_coverage_NEW_requirements:
+ *   - 5 retrieval engagement boundary cases (1.49 / 1.50 / 1.99 / 2.00 / 2.50)
+ *   - 5 shape-routing tests (claude/gpt/generic-simple excluded; qwen-thinking/qwen-non-thinking included)
+ *   - Cost penalty: zero overage + positive overage scenarios
+ *   - End-to-end computeFitness invariants on both Qwen and non-Qwen shapes
+ *
+ * §F.5 false-positive guard tests live in acceptance.test.ts (separate module).
+ */
+
+import { describe, expect, it } from 'vitest';
+import {
+  computeRetrievalEngagementBonus,
+  computeCostPenalty,
+  computeFitness,
+  RETRIEVAL_ENGAGEMENT_BANDS,
+  computeTier2RetrievalBonus,
+  computeTieredFitness,
+  computeDeltaFloorVerdict,
+  TIER_2_BONUS_CAP,
+  TIER_2_BONUS_PER_PP,
+  TIER_3_BONUS_FULL_INVARIANCE,
+  TIER_3_ANCHOR_COUNT_FULL,
+  DELTA_FLOOR_THRESHOLDS,
+} from '../../src/faza-1/fitness.js';
+import {
+  type CandidateMetrics,
+  type ShapeName,
+  QWEN_TARGETED_SHAPES,
+  NON_QWEN_SHAPES,
+  NULL_BASELINE_PER_SHAPE,
+  NULL_BASELINE_AGGREGATE,
+} from '../../src/faza-1/types.js';
+
+// ───────────────────────────────────────────────────────────────────────────
+// Helpers
+// ───────────────────────────────────────────────────────────────────────────
+
+function makeCandidate(overrides: Partial<CandidateMetrics> & Pick<CandidateMetrics, 'shape'>): CandidateMetrics {
+  return {
+    candidateId: overrides.candidateId ?? `${overrides.shape}-test-candidate`,
+    shape: overrides.shape,
+    evaluations: overrides.evaluations ?? [],
+    trioStrictPassRateII: overrides.trioStrictPassRateII ?? 0.5,
+    trioStrictPassRateI: overrides.trioStrictPassRateI ?? 0.5,
+    meanRetrievalCallsPerTask: overrides.meanRetrievalCallsPerTask ?? 1.5,
+    meanCostUsd: overrides.meanCostUsd ?? 0.5,
+  };
+}
+
+// ───────────────────────────────────────────────────────────────────────────
+// computeRetrievalEngagementBonus — Amendment 2 §3 binding boundary tests
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('computeRetrievalEngagementBonus — Amendment 2 §3 boundary cases', () => {
+  // Per manifest v7 §amendment_2_integration.scaffold_test_coverage_NEW_requirements
+  // mandatory_boundary_tests block — these 5 cases are BINDING contract tests.
+
+  it('qwen-thinking, mean retrieval_calls = 1.49 → expect bonus = -0.05', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 1.49)).toBe(-0.05);
+  });
+
+  it('qwen-thinking, mean retrieval_calls = 1.50 → expect bonus = 0.00 (lower threshold inclusive)', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 1.50)).toBe(0.0);
+  });
+
+  it('qwen-thinking, mean retrieval_calls = 1.99 → expect bonus = 0.00', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 1.99)).toBe(0.0);
+  });
+
+  it('qwen-thinking, mean retrieval_calls = 2.00 → expect bonus = +0.05 (upper threshold inclusive)', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 2.00)).toBe(0.05);
+  });
+
+  it('qwen-thinking, mean retrieval_calls = 2.50 → expect bonus = +0.05', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 2.50)).toBe(0.05);
+  });
+
+  // Symmetry check on qwen-non-thinking (other Qwen-targeted shape)
+  it('qwen-non-thinking exhibits identical band behavior to qwen-thinking', () => {
+    expect(computeRetrievalEngagementBonus('qwen-non-thinking', 1.49)).toBe(-0.05);
+    expect(computeRetrievalEngagementBonus('qwen-non-thinking', 1.50)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('qwen-non-thinking', 2.00)).toBe(0.05);
+  });
+
+  // Edge cases beyond the 5 mandatory boundaries — defensive coverage
+  it('handles 0 retrieval calls (extreme low)', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 0)).toBe(-0.05);
+  });
+
+  it('handles very high retrieval calls (extreme high)', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 10.0)).toBe(0.05);
+  });
+
+  it('exposes binding band constants for external auditing', () => {
+    expect(RETRIEVAL_ENGAGEMENT_BANDS.upperThreshold).toBe(2.0);
+    expect(RETRIEVAL_ENGAGEMENT_BANDS.lowerThreshold).toBe(1.5);
+    expect(RETRIEVAL_ENGAGEMENT_BANDS.bonusPlus).toBe(0.05);
+    expect(RETRIEVAL_ENGAGEMENT_BANDS.bonusZero).toBe(0.0);
+    expect(RETRIEVAL_ENGAGEMENT_BANDS.bonusMinus).toBe(-0.05);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Shape-routing tests — Amendment 2 §3 mandatory_routing_tests
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('shape-routing — Amendment 2 §3 retrieval engagement excluded for non-Qwen shapes', () => {
+  // Per Amendment 2 §3 rationale: "Phase 4.5 finding is Qwen-specific. Opus
+  // shape does NOT have the gap. Applying retrieval-engagement bonus uniformly
+  // across all shapes would distort fitness for shapes that don't have the
+  // underlying behavioral problem."
+
+  it('claude shape: bonus computation NOT applied (excluded)', () => {
+    expect(computeRetrievalEngagementBonus('claude', 0)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('claude', 1.49)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('claude', 2.50)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('claude', 100)).toBe(0.0);
+  });
+
+  it('gpt shape: bonus computation NOT applied (excluded)', () => {
+    expect(computeRetrievalEngagementBonus('gpt', 0)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('gpt', 1.49)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('gpt', 2.50)).toBe(0.0);
+  });
+
+  it('generic-simple shape: bonus computation NOT applied (excluded)', () => {
+    expect(computeRetrievalEngagementBonus('generic-simple', 0)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('generic-simple', 1.49)).toBe(0.0);
+    expect(computeRetrievalEngagementBonus('generic-simple', 2.50)).toBe(0.0);
+  });
+
+  it('qwen-thinking shape: bonus computation IS applied', () => {
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 1.49)).toBe(-0.05);
+    expect(computeRetrievalEngagementBonus('qwen-thinking', 2.50)).toBe(0.05);
+  });
+
+  it('qwen-non-thinking shape: bonus computation IS applied', () => {
+    expect(computeRetrievalEngagementBonus('qwen-non-thinking', 1.49)).toBe(-0.05);
+    expect(computeRetrievalEngagementBonus('qwen-non-thinking', 2.50)).toBe(0.05);
+  });
+
+  it('shape-class set membership matches manifest v7 declaration', () => {
+    // Manifest v7 §metric_operationalization.retrieval_engagement_bonus.applies_to_shapes
+    expect(QWEN_TARGETED_SHAPES.has('qwen-thinking')).toBe(true);
+    expect(QWEN_TARGETED_SHAPES.has('qwen-non-thinking')).toBe(true);
+    expect(QWEN_TARGETED_SHAPES.has('claude')).toBe(false);
+    expect(QWEN_TARGETED_SHAPES.has('gpt')).toBe(false);
+    expect(QWEN_TARGETED_SHAPES.has('generic-simple')).toBe(false);
+
+    // Manifest v7 §metric_operationalization.retrieval_engagement_bonus.excluded_shapes
+    expect(NON_QWEN_SHAPES.has('claude')).toBe(true);
+    expect(NON_QWEN_SHAPES.has('gpt')).toBe(true);
+    expect(NON_QWEN_SHAPES.has('generic-simple')).toBe(true);
+    expect(NON_QWEN_SHAPES.has('qwen-thinking')).toBe(false);
+    expect(NON_QWEN_SHAPES.has('qwen-non-thinking')).toBe(false);
+  });
+
+  it('partition: every ShapeName is in exactly one set (no overlap, no gap)', () => {
+    const all: ShapeName[] = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'];
+    for (const s of all) {
+      const inQwen = QWEN_TARGETED_SHAPES.has(s);
+      const inNonQwen = NON_QWEN_SHAPES.has(s);
+      expect(inQwen !== inNonQwen).toBe(true);  // exactly one
+    }
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// computeCostPenalty
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('computeCostPenalty — brief §3.1 0.5pp per $0.10 above baseline median', () => {
+  it('returns 0 when candidate cost equals baseline median', () => {
+    expect(computeCostPenalty(0.50, 0.50)).toBe(0.0);
+  });
+
+  it('returns 0 when candidate cost below baseline median', () => {
+    expect(computeCostPenalty(0.30, 0.50)).toBe(0.0);
+  });
+
+  it('returns 0.005 (0.5pp) for $0.10 overage', () => {
+    expect(computeCostPenalty(0.60, 0.50)).toBeCloseTo(0.005, 6);
+  });
+
+  it('returns 0.025 (2.5pp) for $0.50 overage', () => {
+    expect(computeCostPenalty(1.00, 0.50)).toBeCloseTo(0.025, 6);
+  });
+
+  it('returns 0.05 (5pp) for $1.00 overage', () => {
+    expect(computeCostPenalty(1.50, 0.50)).toBeCloseTo(0.05, 6);
+  });
+
+  it('handles fractional overage', () => {
+    expect(computeCostPenalty(0.55, 0.50)).toBeCloseTo(0.0025, 6);  // $0.05 overage = 0.25pp
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// computeFitness — end-to-end aggregate
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('computeFitness — Qwen-targeted shape branch', () => {
+  it('applies retrieval engagement bonus + cost penalty for qwen-thinking', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.50,
+      meanRetrievalCallsPerTask: 2.0,  // expect +0.05 bonus
+      meanCostUsd: 0.60,                // expect +0.005 cost penalty (vs 0.50 baseline)
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+
+    expect(result.trioStrictPassRateII).toBe(0.50);
+    expect(result.retrievalEngagementBonus).toBe(0.05);
+    expect(result.costPenalty).toBeCloseTo(0.005, 6);
+    expect(result.fitness).toBeCloseTo(0.50 + 0.05 - 0.005, 6);
+    expect(result.retrievalEngagementApplied).toBe(true);
+  });
+
+  it('applies negative retrieval engagement bonus when below 1.5', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-non-thinking',
+      trioStrictPassRateII: 0.30,
+      meanRetrievalCallsPerTask: 1.0,  // expect -0.05 bonus
+      meanCostUsd: 0.50,                // no cost overage
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+
+    expect(result.retrievalEngagementBonus).toBe(-0.05);
+    expect(result.fitness).toBeCloseTo(0.30 - 0.05 - 0.0, 6);
+    expect(result.retrievalEngagementApplied).toBe(true);
+  });
+
+  it('zero band: candidate retrieves in [1.5, 2.0) gets neutral bonus', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.40,
+      meanRetrievalCallsPerTask: 1.7,
+      meanCostUsd: 0.50,
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+    expect(result.retrievalEngagementBonus).toBe(0.0);
+    expect(result.fitness).toBe(0.40);
+  });
+});
+
+describe('computeFitness — non-Qwen shape branch', () => {
+  it('claude shape: no retrieval engagement bonus regardless of retrieval_calls', () => {
+    const candidate = makeCandidate({
+      shape: 'claude',
+      trioStrictPassRateII: 0.60,
+      meanRetrievalCallsPerTask: 2.5,  // would be +0.05 if Qwen, but excluded for claude
+      meanCostUsd: 0.50,
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+    expect(result.retrievalEngagementBonus).toBe(0.0);
+    expect(result.fitness).toBe(0.60);
+    expect(result.retrievalEngagementApplied).toBe(false);
+  });
+
+  it('gpt shape: no retrieval engagement bonus regardless of retrieval_calls', () => {
+    const candidate = makeCandidate({
+      shape: 'gpt',
+      trioStrictPassRateII: 0.45,
+      meanRetrievalCallsPerTask: 0.5,  // would be -0.05 if Qwen, excluded for gpt
+      meanCostUsd: 0.50,
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+    expect(result.retrievalEngagementBonus).toBe(0.0);
+    expect(result.fitness).toBe(0.45);
+    expect(result.retrievalEngagementApplied).toBe(false);
+  });
+
+  it('generic-simple shape: cost penalty still applies, but no retrieval bonus', () => {
+    const candidate = makeCandidate({
+      shape: 'generic-simple',
+      trioStrictPassRateII: 0.70,
+      meanRetrievalCallsPerTask: 3.0,
+      meanCostUsd: 1.00,  // $0.50 overage → 2.5pp penalty
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+    expect(result.retrievalEngagementBonus).toBe(0.0);
+    expect(result.costPenalty).toBeCloseTo(0.025, 6);
+    expect(result.fitness).toBeCloseTo(0.70 - 0.025, 6);
+  });
+});
+
+describe('computeFitness — invariants', () => {
+  it('retrievalEngagementApplied flag matches QWEN_TARGETED_SHAPES membership', () => {
+    const shapes: ShapeName[] = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'];
+    for (const shape of shapes) {
+      const result = computeFitness({
+        candidate: makeCandidate({ shape, meanRetrievalCallsPerTask: 1.5, meanCostUsd: 0.5 }),
+        baselineMedianCostUsd: 0.5,
+      });
+      expect(result.retrievalEngagementApplied).toBe(QWEN_TARGETED_SHAPES.has(shape));
+    }
+  });
+
+  it('fitness components sum to fitness within floating point tolerance', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.55,
+      meanRetrievalCallsPerTask: 2.1,
+      meanCostUsd: 0.65,
+    });
+    const result = computeFitness({ candidate, baselineMedianCostUsd: 0.50 });
+    const expected = result.trioStrictPassRateII + result.retrievalEngagementBonus - result.costPenalty;
+    expect(result.fitness).toBeCloseTo(expected, 10);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Amendment 7 — Tier 2 retrieval bonus (continuous)
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('Amendment 7 — computeTier2RetrievalBonus (continuous formula)', () => {
+  it('returns 0 for non-Qwen shape regardless of retrieval delta', () => {
+    expect(computeTier2RetrievalBonus('claude', 2.0, 1.0)).toBe(0);
+    expect(computeTier2RetrievalBonus('gpt', 5.0, 1.0)).toBe(0);
+    expect(computeTier2RetrievalBonus('generic-simple', 3.0, 1.0)).toBe(0);
+  });
+
+  it('returns 0 for Qwen-targeted shape when delta ≤ 0 (no negative bonus)', () => {
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.0, 1.5)).toBe(0);
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.12, 1.12)).toBe(0); // exact zero delta
+    expect(computeTier2RetrievalBonus('qwen-non-thinking', 1.20, 1.25)).toBe(0); // small negative
+  });
+
+  it('formula: 0.05 bonus per pp above baseline (1pp = 0.01 absolute)', () => {
+    // baseline 1.12, candidate 1.13 = +0.01 = +1pp → 0.05 bonus
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.13, 1.12)).toBeCloseTo(0.05, 10);
+    // baseline 1.12, candidate 1.14 = +0.02 = +2pp → 0.10 bonus
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.14, 1.12)).toBeCloseTo(0.10, 10);
+    // baseline 1.12, candidate 1.15 = +0.03 = +3pp → 0.15 bonus
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.15, 1.12)).toBeCloseTo(0.15, 10);
+    // baseline 1.12, candidate 1.16 = +0.04 = +4pp → 0.20 bonus
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.16, 1.12)).toBeCloseTo(0.20, 10);
+    // baseline 1.12, candidate 1.17 = +0.05 = +5pp → 0.25 (cap)
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.17, 1.12)).toBeCloseTo(0.25, 10);
+  });
+
+  it('cap holds at +5pp absolute and beyond (cap = 0.25)', () => {
+    expect(computeTier2RetrievalBonus('qwen-thinking', 1.20, 1.12)).toBe(TIER_2_BONUS_CAP);
+    expect(computeTier2RetrievalBonus('qwen-thinking', 2.00, 1.12)).toBe(TIER_2_BONUS_CAP);
+    expect(computeTier2RetrievalBonus('qwen-thinking', 5.00, 1.12)).toBe(TIER_2_BONUS_CAP);
+    expect(computeTier2RetrievalBonus('qwen-non-thinking', 1.30, 1.25)).toBe(TIER_2_BONUS_CAP);
+  });
+
+  it('Tier 2 weight constants match Amendment 7 §fitness_function_tiered.tier_2', () => {
+    expect(TIER_2_BONUS_PER_PP).toBe(0.05);
+    expect(TIER_2_BONUS_CAP).toBe(0.25);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Amendment 7 — computeTieredFitness (Tier 1/2/3 + saturated regime aggregate)
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('Amendment 7 — computeTieredFitness', () => {
+  it('Tier 1 = NULL pass rate delta in pp (signed)', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.95,
+      meanRetrievalCallsPerTask: 1.12,
+    });
+    const result = computeTieredFitness({
+      candidate,
+      nullBaselinePassRateII: 0.875,
+      nullBaselineMeanRetrievalCallsPerTask: 1.12,
+      mutationValidatorPassed: true,
+      saturatedRegime: true,
+    });
+    expect(result.tier1DeltaPP).toBeCloseTo(7.5, 10); // 0.95 - 0.875 = 0.075 = 7.5pp
+  });
+
+  it('Tier 1 negative when candidate regresses below NULL baseline', () => {
+    const candidate = makeCandidate({
+      shape: 'claude',
+      trioStrictPassRateII: 0.75,
+      meanRetrievalCallsPerTask: 1.12,
+    });
+    const result = computeTieredFitness({
+      candidate,
+      nullBaselinePassRateII: 0.875,
+      nullBaselineMeanRetrievalCallsPerTask: 1.12,
+      mutationValidatorPassed: true,
+      saturatedRegime: true,
+    });
+    expect(result.tier1DeltaPP).toBeCloseTo(-12.5, 10); // 0.75 - 0.875 = -0.125 = -12.5pp
+  });
+
+  it('Tier 2 only applies to Qwen-targeted shapes', () => {
+    for (const shape of ['claude', 'gpt', 'generic-simple'] as const) {
+      const result = computeTieredFitness({
+        candidate: makeCandidate({ shape, meanRetrievalCallsPerTask: 5.0 }),
+        nullBaselinePassRateII: 0.875,
+        nullBaselineMeanRetrievalCallsPerTask: 1.12,
+        mutationValidatorPassed: true,
+        saturatedRegime: true,
+      });
+      expect(result.tier2RetrievalBonus).toBe(0);
+    }
+  });
+
+  it('Tier 3 = 0.10 if mutation validator passed; 0 otherwise', () => {
+    const base = {
+      candidate: makeCandidate({ shape: 'claude' }),
+      nullBaselinePassRateII: 0.875,
+      nullBaselineMeanRetrievalCallsPerTask: 1.12,
+      saturatedRegime: true,
+    };
+    expect(computeTieredFitness({ ...base, mutationValidatorPassed: true }).tier3CellSemanticInvarianceBonus).toBe(
+      TIER_3_BONUS_FULL_INVARIANCE,
+    );
+    expect(computeTieredFitness({ ...base, mutationValidatorPassed: false }).tier3CellSemanticInvarianceBonus).toBe(0);
+  });
+
+  it('cellSemanticAnchorInvarianceCount: 7 if validator passed; 0 otherwise', () => {
+    const base = {
+      candidate: makeCandidate({ shape: 'claude' }),
+      nullBaselinePassRateII: 0.875,
+      nullBaselineMeanRetrievalCallsPerTask: 1.12,
+      saturatedRegime: true,
+    };
+    expect(computeTieredFitness({ ...base, mutationValidatorPassed: true }).cellSemanticAnchorInvarianceCount).toBe(
+      TIER_3_ANCHOR_COUNT_FULL,
+    );
+    expect(computeTieredFitness({ ...base, mutationValidatorPassed: false }).cellSemanticAnchorInvarianceCount).toBe(0);
+  });
+
+  it('aggregateSaturatedRegime = tier_2 + tier_3 (Tier 1 NOT included)', () => {
+    const candidate = makeCandidate({
+      shape: 'qwen-thinking',
+      trioStrictPassRateII: 0.95, // would give tier1 = 7.5pp
+      meanRetrievalCallsPerTask: 1.15, // 1.12 baseline → +3pp → tier2 = 0.15
+    });
+    const result = computeTieredFitness({
+      candidate,
+      nullBaselinePassRateII: 0.875,
+      nullBaselineMeanRetrievalCallsPerTask: 1.12,
+      mutationValidatorPassed: true, // tier3 = 0.10
+      saturatedRegime: true,
+    });
+    expect(result.aggregateSaturatedRegime).toBeCloseTo(0.15 + 0.10, 10); // 0.25
+    expect(result.aggregateSaturatedRegime).not.toBeCloseTo(7.5 + 0.15 + 0.10, 1); // tier1 not in aggregate
+  });
+
+  it('saturatedRegimeApplied flag mirrors input', () => {
+    const base = {
+      candidate: makeCandidate({ shape: 'claude' }),
+      nullBaselinePassRateII: 0.875,
+      nullBaselineMeanRetrievalCallsPerTask: 1.12,
+      mutationValidatorPassed: true,
+    };
+    expect(computeTieredFitness({ ...base, saturatedRegime: true }).saturatedRegimeApplied).toBe(true);
+    expect(computeTieredFitness({ ...base, saturatedRegime: false }).saturatedRegimeApplied).toBe(false);
+  });
+
+  it('NULL_BASELINE_PER_SHAPE constants match Checkpoint A v2 §B.2 pinned values', () => {
+    expect(NULL_BASELINE_PER_SHAPE.claude.trioStrictPassRateII).toBe(0.875);
+    expect(NULL_BASELINE_PER_SHAPE['qwen-thinking'].trioStrictPassRateII).toBe(0.875);
+    expect(NULL_BASELINE_PER_SHAPE['qwen-non-thinking'].trioStrictPassRateII).toBe(1.0);
+    expect(NULL_BASELINE_PER_SHAPE.gpt.trioStrictPassRateII).toBe(0.75);
+    expect(NULL_BASELINE_PER_SHAPE['generic-simple'].trioStrictPassRateII).toBe(0.875);
+    expect(NULL_BASELINE_AGGREGATE.trioStrictPassRateII).toBe(0.875);
+    expect(NULL_BASELINE_AGGREGATE.meanRetrievalCallsPerTask).toBe(1.12);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Amendment 7 — computeDeltaFloorVerdict (3 OR-gated thresholds)
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('Amendment 7 — computeDeltaFloorVerdict (§gen_1_pre_registered_delta_floor)', () => {
+  it('PROCEED if threshold 1 (aggregate Tier 1 ≥+3pp) passes alone', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.910, // +3.5pp vs 0.875 NULL
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: { 'qwen-thinking': 1.12 }, // no Qwen retrieval signal
+      qwenShapeNullBaselineRetrievalMeans: { 'qwen-thinking': 1.12 },
+      qwenAggregateTier2Bonus: 0,
+    });
+    expect(verdict.threshold1AggregateTier1).toBe('PASS');
+    expect(verdict.threshold1ValuePP).toBeCloseTo(3.5, 10);
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('FAIL');
+    expect(verdict.threshold3CompoundTier1PlusTier2).toBe('FAIL');
+    expect(verdict.overallVerdict).toBe('PROCEED');
+  });
+
+  it('PROCEED if threshold 2 (Qwen retrieval ≥+0.10) passes alone', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.875, // exactly NULL → 0pp (fails threshold 1 ≥+3pp)
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: { 'qwen-thinking': 1.30 }, // 1.30 - 1.12 = +0.18 ≥ 0.10
+      qwenShapeNullBaselineRetrievalMeans: { 'qwen-thinking': 1.12 },
+      qwenAggregateTier2Bonus: 0, // not enough for threshold 3
+    });
+    expect(verdict.threshold1AggregateTier1).toBe('FAIL');
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('PASS');
+    expect(verdict.threshold2MaxDeltaAbsolute).toBeCloseTo(0.18, 10);
+    expect(verdict.overallVerdict).toBe('PROCEED');
+  });
+
+  it('PROCEED if threshold 3 (Tier 1 ≥0pp AND Tier 2 ≥0.05) passes alone', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.880, // +0.5pp ≥ 0pp; fails threshold 1 ≥+3pp
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: { 'qwen-thinking': 1.13 }, // +0.01 < 0.10 — fails threshold 2
+      qwenShapeNullBaselineRetrievalMeans: { 'qwen-thinking': 1.12 },
+      qwenAggregateTier2Bonus: 0.05, // ≥ 0.05
+    });
+    expect(verdict.threshold1AggregateTier1).toBe('FAIL');
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('FAIL');
+    expect(verdict.threshold3CompoundTier1PlusTier2).toBe('PASS');
+    expect(verdict.overallVerdict).toBe('PROCEED');
+  });
+
+  it('HALT_INVESTIGATE if all three thresholds fail', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.870, // -0.5pp — fails threshold 1 + threshold 3 (Tier 1 < 0pp)
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: { 'qwen-thinking': 1.13 }, // +0.01 < 0.10
+      qwenShapeNullBaselineRetrievalMeans: { 'qwen-thinking': 1.12 },
+      qwenAggregateTier2Bonus: 0.04, // < 0.05
+    });
+    expect(verdict.threshold1AggregateTier1).toBe('FAIL');
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('FAIL');
+    expect(verdict.threshold3CompoundTier1PlusTier2).toBe('FAIL');
+    expect(verdict.overallVerdict).toBe('HALT_INVESTIGATE');
+  });
+
+  it('threshold 1 exact-boundary: 3.0pp passes (≥+3pp inclusive with EPSILON)', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.905, // +3.0pp exactly
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: {},
+      qwenShapeNullBaselineRetrievalMeans: {},
+      qwenAggregateTier2Bonus: 0,
+    });
+    expect(verdict.threshold1AggregateTier1).toBe('PASS');
+    expect(verdict.threshold1ValuePP).toBeCloseTo(3.0, 10);
+  });
+
+  it('threshold 2 exact-boundary: +0.10 absolute passes (≥+0.10 inclusive with EPSILON)', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.875,
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: { 'qwen-thinking': 1.22 }, // 1.22 - 1.12 = +0.10 exact
+      qwenShapeNullBaselineRetrievalMeans: { 'qwen-thinking': 1.12 },
+      qwenAggregateTier2Bonus: 0,
+    });
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('PASS');
+    expect(verdict.threshold2MaxDeltaAbsolute).toBeCloseTo(0.10, 10);
+  });
+
+  it('threshold 2 takes max delta across multiple Qwen shapes', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.875,
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: {
+        'qwen-thinking': 1.13,        // +0.01
+        'qwen-non-thinking': 1.40,    // +0.15
+      },
+      qwenShapeNullBaselineRetrievalMeans: {
+        'qwen-thinking': 1.12,
+        'qwen-non-thinking': 1.25,
+      },
+      qwenAggregateTier2Bonus: 0,
+    });
+    expect(verdict.threshold2MaxDeltaAbsolute).toBeCloseTo(0.15, 10);
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('PASS');
+  });
+
+  it('handles empty Qwen data: threshold 2 max delta = 0 (FAIL since 0 < 0.10)', () => {
+    const verdict = computeDeltaFloorVerdict({
+      aggregateTrioStrictPassRateII: 0.875,
+      aggregateNullBaselinePassRateII: 0.875,
+      qwenShapeRetrievalMeans: {},
+      qwenShapeNullBaselineRetrievalMeans: {},
+      qwenAggregateTier2Bonus: 0,
+    });
+    expect(verdict.threshold2MaxDeltaAbsolute).toBe(0);
+    expect(verdict.threshold2QwenRetrievalAbsolute).toBe('FAIL');
+  });
+
+  it('Δ-floor threshold constants match Amendment 7 §gen_1_pre_registered_delta_floor', () => {
+    expect(DELTA_FLOOR_THRESHOLDS.threshold1AggregateTier1PP).toBe(3);
+    expect(DELTA_FLOOR_THRESHOLDS.threshold2QwenRetrievalAbsolute).toBe(0.10);
+    expect(DELTA_FLOOR_THRESHOLDS.threshold3Tier1MinPP).toBe(0);
+    expect(DELTA_FLOOR_THRESHOLDS.threshold3Tier2MinBonus).toBe(0.05);
+  });
+});

benchmarks/gepa/tests/faza-1/kappa-audit.test.ts

@@ -0,0 +1,204 @@
+/**
+ * GEPA Faza 1 — κ audit utility tests.
+ *
+ * Coverage targets:
+ *   - Drift band semantics: PASS / DRIFT_LOW / DRIFT_HIGH per Faza 1 §F.3
+ *   - Conservative trio = min of three pairwise (matches manifest v6 §5.4 + v7 anchor)
+ *   - v6 policy floor cross-validation reporting
+ *   - Cohen's κ computation against known-correct values from kappa-recal artifact
+ */
+
+import { describe, expect, it } from 'vitest';
+import {
+  CANONICAL_KAPPA,
+  KAPPA_DRIFT_THRESHOLD,
+  KAPPA_DRIFT_BAND_LOW,
+  KAPPA_DRIFT_BAND_HIGH,
+  V6_KAPPA_POLICY_FLOOR_PASS,
+  auditKappa,
+  computeCohensKappa,
+} from '../../src/faza-1/kappa-audit.js';
+
+// ───────────────────────────────────────────────────────────────────────────
+// Constants exposed for external auditing
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('canonical κ + drift band constants', () => {
+  it('CANONICAL_KAPPA matches kappa-recal artifact value (0.7877758913412564)', () => {
+    expect(CANONICAL_KAPPA).toBe(0.7877758913412564);
+  });
+
+  it('drift threshold is 0.05 per brief §4 condition 3', () => {
+    expect(KAPPA_DRIFT_THRESHOLD).toBe(0.05);
+  });
+
+  it('drift band low = canonical - 0.05', () => {
+    // Canonical is 0.7877758913412564; minus 0.05 = 0.7377758913412564 ≈ 0.7378.
+    // toBeCloseTo precision 4 = absolute diff < 5e-5 (covers the ~2.4e-5 rounding gap).
+    expect(KAPPA_DRIFT_BAND_LOW).toBeCloseTo(0.7378, 4);
+    // Stronger invariant: equals canonical minus drift threshold exactly (within IEEE 754).
+    expect(KAPPA_DRIFT_BAND_LOW).toBe(CANONICAL_KAPPA - KAPPA_DRIFT_THRESHOLD);
+  });
+
+  it('drift band high = canonical + 0.05', () => {
+    expect(KAPPA_DRIFT_BAND_HIGH).toBeCloseTo(0.8378, 4);
+    expect(KAPPA_DRIFT_BAND_HIGH).toBe(CANONICAL_KAPPA + KAPPA_DRIFT_THRESHOLD);
+  });
+
+  it('v6 policy floor pass threshold is 0.70', () => {
+    expect(V6_KAPPA_POLICY_FLOOR_PASS).toBe(0.70);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Drift band verdicts
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('auditKappa — drift band verdicts', () => {
+  it('canonical value triggers PASS_WITHIN_DRIFT_BAND', () => {
+    const r = auditKappa({ kOpusGpt: CANONICAL_KAPPA, kOpusMinimax: CANONICAL_KAPPA, kGptMinimax: CANONICAL_KAPPA });
+    expect(r.kConservativeTrio).toBe(CANONICAL_KAPPA);
+    expect(r.verdict).toBe('PASS_WITHIN_DRIFT_BAND');
+    expect(r.driftFromCanonical).toBe(0);
+  });
+
+  it('value below band low triggers DRIFT_LOW_BELOW_BAND', () => {
+    const r = auditKappa({ kOpusGpt: 0.85, kOpusMinimax: 0.85, kGptMinimax: 0.70 });
+    // min = 0.70 < 0.7378 band low
+    expect(r.verdict).toBe('DRIFT_LOW_BELOW_BAND');
+  });
+
+  it('value above band high triggers DRIFT_HIGH_ABOVE_BAND', () => {
+    const r = auditKappa({ kOpusGpt: 0.90, kOpusMinimax: 0.90, kGptMinimax: 0.85 });
+    // min = 0.85 > 0.8378 band high
+    expect(r.verdict).toBe('DRIFT_HIGH_ABOVE_BAND');
+  });
+
+  it('exact band low boundary inclusive (PASS)', () => {
+    const r = auditKappa({ kOpusGpt: 1.0, kOpusMinimax: 1.0, kGptMinimax: KAPPA_DRIFT_BAND_LOW });
+    expect(r.verdict).toBe('PASS_WITHIN_DRIFT_BAND');
+  });
+
+  it('exact band high boundary inclusive (PASS)', () => {
+    const r = auditKappa({ kOpusGpt: KAPPA_DRIFT_BAND_HIGH, kOpusMinimax: KAPPA_DRIFT_BAND_HIGH, kGptMinimax: KAPPA_DRIFT_BAND_HIGH });
+    expect(r.verdict).toBe('PASS_WITHIN_DRIFT_BAND');
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Conservative trio = min — anchor against actual kappa-recal data
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('auditKappa — conservative trio = min of pairwise', () => {
+  it('reproduces v6-kappa-recal conservative trio from pairwise', () => {
+    // From benchmarks/calibration/v6-kappa-recal/_summary-v6-kappa.json
+    // (SHA 657d4490... pinned in launch decision §B)
+    const r = auditKappa({
+      kOpusGpt: 0.847958297132928,
+      kOpusMinimax: 0.8548922056384745,
+      kGptMinimax: 0.7877758913412564,
+    });
+    expect(r.kConservativeTrio).toBe(0.7877758913412564);
+    expect(r.verdict).toBe('PASS_WITHIN_DRIFT_BAND');
+    expect(r.v6PolicyFloorPass).toBe(true);
+  });
+
+  it('v6 policy floor PASS when conservative >= 0.70', () => {
+    const r = auditKappa({ kOpusGpt: 0.75, kOpusMinimax: 0.72, kGptMinimax: 0.70 });
+    expect(r.v6PolicyFloorPass).toBe(true);
+  });
+
+  it('v6 policy floor FAIL when conservative < 0.70', () => {
+    const r = auditKappa({ kOpusGpt: 0.75, kOpusMinimax: 0.72, kGptMinimax: 0.65 });
+    expect(r.v6PolicyFloorPass).toBe(false);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Audit log line format
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('auditKappa — audit log line format', () => {
+  it('audit log contains all required fields', () => {
+    const r = auditKappa({ kOpusGpt: 0.85, kOpusMinimax: 0.84, kGptMinimax: 0.78 });
+    expect(r.auditLogLine).toContain('κ_conservative_trio=');
+    expect(r.auditLogLine).toContain('canonical=');
+    expect(r.auditLogLine).toContain('drift=');
+    expect(r.auditLogLine).toContain('verdict=');
+    expect(r.auditLogLine).toContain('v6_policy_floor=');
+  });
+
+  it('positive drift includes + sign', () => {
+    const r = auditKappa({ kOpusGpt: 0.83, kOpusMinimax: 0.83, kGptMinimax: 0.80 });
+    expect(r.driftFromCanonical).toBeGreaterThan(0);
+    expect(r.auditLogLine).toContain('drift=+');
+  });
+
+  it('negative drift uses - sign', () => {
+    const r = auditKappa({ kOpusGpt: 0.85, kOpusMinimax: 0.85, kGptMinimax: 0.70 });
+    expect(r.driftFromCanonical).toBeLessThan(0);
+    expect(r.auditLogLine).toContain('drift=-');
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Cohen's κ primitive
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('computeCohensKappa primitive', () => {
+  it('returns 1.0 for perfect agreement', () => {
+    const k = computeCohensKappa({
+      bothCorrect: 50,
+      bothIncorrect: 50,
+      firstCorrectSecondIncorrect: 0,
+      firstIncorrectSecondCorrect: 0,
+    });
+    expect(k).toBe(1.0);
+  });
+
+  it('returns 0 for chance-level agreement (50/50 base rate, random co-occurrence)', () => {
+    // 100 trials, both raters each correct 50% with independent assignment
+    const k = computeCohensKappa({
+      bothCorrect: 25,
+      bothIncorrect: 25,
+      firstCorrectSecondIncorrect: 25,
+      firstIncorrectSecondCorrect: 25,
+    });
+    expect(k).toBeCloseTo(0, 2);
+  });
+
+  it('reproduces v6 Opus-vs-GPT pairwise κ from kappa-recal data', () => {
+    // From _summary-v6-kappa.json line 58-63 confusion_opus_gpt:
+    //   correct_correct: 32, incorrect_incorrect: 61
+    //   correct_incorrect: 7, incorrect_correct: 0
+    // expected κ = 0.847958297132928 (per same file line 3 k_opus_gpt)
+    const k = computeCohensKappa({
+      bothCorrect: 32,
+      bothIncorrect: 61,
+      firstCorrectSecondIncorrect: 7,
+      firstIncorrectSecondCorrect: 0,
+    });
+    expect(k).toBeCloseTo(0.847958297132928, 6);
+  });
+
+  it('returns NaN for empty observation set', () => {
+    const k = computeCohensKappa({
+      bothCorrect: 0,
+      bothIncorrect: 0,
+      firstCorrectSecondIncorrect: 0,
+      firstIncorrectSecondCorrect: 0,
+    });
+    expect(Number.isNaN(k)).toBe(true);
+  });
+
+  it('handles unanimous-correct edge case (100% base rate, no variance)', () => {
+    // Both raters agree everything is correct — observed = 1, expected = 1, κ undefined → return 1
+    const k = computeCohensKappa({
+      bothCorrect: 100,
+      bothIncorrect: 0,
+      firstCorrectSecondIncorrect: 0,
+      firstIncorrectSecondCorrect: 0,
+    });
+    expect(k).toBe(1.0);
+  });
+});

benchmarks/gepa/tests/faza-1/mutation-oracle-fork.test.ts

@@ -0,0 +1,180 @@
+/**
+ * GEPA Faza 1 — mutation oracle fork tests.
+ *
+ * Coverage targets per manifest v7 §amendment_2_integration.scaffold_test_coverage_NEW_requirements
+ * mandatory_routing_tests:
+ *   - Qwen branch: qwen-thinking + qwen-non-thinking → "qwen" template
+ *   - Non-Qwen branch: claude + gpt + generic-simple → "non-qwen" template
+ *   - Template content actually exists at expected paths
+ *   - Placeholder substitution works for both branches
+ */
+
+import { describe, expect, it } from 'vitest';
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+import {
+  classifyShape,
+  templatePathForShape,
+  loadTemplate,
+  buildOraclePrompt,
+} from '../../src/faza-1/mutation-oracle-fork.js';
+import { type ShapeName } from '../../src/faza-1/types.js';
+
+const ORACLE_DIR = path.resolve(__dirname, '../../oracle/faza-1');
+
+// ───────────────────────────────────────────────────────────────────────────
+// Shape classification — Amendment 2 §4 mandatory routing tests
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('classifyShape — Amendment 2 §4 fork routing', () => {
+  it('qwen-thinking → qwen branch', () => {
+    expect(classifyShape('qwen-thinking')).toBe('qwen');
+  });
+
+  it('qwen-non-thinking → qwen branch', () => {
+    expect(classifyShape('qwen-non-thinking')).toBe('qwen');
+  });
+
+  it('claude → non-qwen branch', () => {
+    expect(classifyShape('claude')).toBe('non-qwen');
+  });
+
+  it('gpt → non-qwen branch', () => {
+    expect(classifyShape('gpt')).toBe('non-qwen');
+  });
+
+  it('generic-simple → non-qwen branch', () => {
+    expect(classifyShape('generic-simple')).toBe('non-qwen');
+  });
+
+  it('partition: every ShapeName routes to exactly one branch', () => {
+    const all: ShapeName[] = ['claude', 'qwen-thinking', 'qwen-non-thinking', 'gpt', 'generic-simple'];
+    const qwenCount = all.filter(s => classifyShape(s) === 'qwen').length;
+    const nonQwenCount = all.filter(s => classifyShape(s) === 'non-qwen').length;
+    expect(qwenCount).toBe(2);
+    expect(nonQwenCount).toBe(3);
+    expect(qwenCount + nonQwenCount).toBe(all.length);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// Template paths
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('templatePathForShape', () => {
+  it('Qwen-targeted shapes resolve to mutation-prompt-template-qwen.md', () => {
+    expect(path.basename(templatePathForShape('qwen-thinking'))).toBe('mutation-prompt-template-qwen.md');
+    expect(path.basename(templatePathForShape('qwen-non-thinking'))).toBe('mutation-prompt-template-qwen.md');
+  });
+
+  it('Non-Qwen shapes resolve to mutation-prompt-template-non-qwen.md', () => {
+    expect(path.basename(templatePathForShape('claude'))).toBe('mutation-prompt-template-non-qwen.md');
+    expect(path.basename(templatePathForShape('gpt'))).toBe('mutation-prompt-template-non-qwen.md');
+    expect(path.basename(templatePathForShape('generic-simple'))).toBe('mutation-prompt-template-non-qwen.md');
+  });
+
+  it('Both template files exist on disk in the oracle directory', () => {
+    expect(fs.existsSync(path.join(ORACLE_DIR, 'mutation-prompt-template-qwen.md'))).toBe(true);
+    expect(fs.existsSync(path.join(ORACLE_DIR, 'mutation-prompt-template-non-qwen.md'))).toBe(true);
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// loadTemplate
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('loadTemplate', () => {
+  it('Qwen template contains anti-premature-finalization scaffolding', () => {
+    const t = loadTemplate('qwen-thinking');
+    // Case-insensitive: template prose uses lowercase, headings may capitalize.
+    expect(t.toLowerCase()).toContain('anti-premature-finalization');
+    // These instruction phrases are unique to the Qwen branch:
+    expect(t).toContain('Continue retrieving until');
+    expect(t).toContain('multi-turn retrieval');
+  });
+
+  it('Non-Qwen template lacks Qwen-specific scaffolding instructions', () => {
+    const t = loadTemplate('claude');
+    expect(t).toContain('Standard mutation directions');
+    expect(t).toContain('reasoning scaffold');
+    // The non-qwen template legitimately MENTIONS the absence of qwen
+    // scaffolding ("no Qwen-specific anti-premature-finalization scaffolding
+    // required"). The proper test is for absence of the actual prescriptive
+    // INSTRUCTION phrases that appear only in the Qwen template.
+    expect(t).not.toContain('Continue retrieving until');
+    expect(t).not.toContain('multi-turn retrieval');
+  });
+
+  it('Both templates lock cell semantic boundaries', () => {
+    const qwenT = loadTemplate('qwen-thinking');
+    const nonQwenT = loadTemplate('claude');
+    for (const t of [qwenT, nonQwenT]) {
+      expect(t).toContain('DO NOT modify');
+      expect(t).toContain('MULTI_STEP_ACTION_CONTRACT');
+    }
+  });
+
+  it('Both templates expose all 4 placeholder tokens', () => {
+    const placeholders = [
+      '###BASELINE_SHAPE_CONTENT###',
+      '###FAILURE_MODE_SUMMARY###',
+      '###SHAPE_NAME###',
+      '###TEMPLATE_CLASS###',
+    ];
+    for (const shape of ['qwen-thinking', 'claude'] as ShapeName[]) {
+      const t = loadTemplate(shape);
+      for (const ph of placeholders) {
+        expect(t).toContain(ph);
+      }
+    }
+  });
+});
+
+// ───────────────────────────────────────────────────────────────────────────
+// buildOraclePrompt — placeholder substitution
+// ───────────────────────────────────────────────────────────────────────────
+
+describe('buildOraclePrompt', () => {
+  it('substitutes all 4 placeholders in Qwen template', () => {
+    const prompt = buildOraclePrompt({
+      shape: 'qwen-thinking',
+      baselineShapeContent: 'export const baselineShape = {...};',
+      failureModeSummary: '1. unsupported-specifics\n2. missed\n3. shallow',
+    });
+    expect(prompt).toContain('export const baselineShape');
+    expect(prompt).toContain('unsupported-specifics');
+    expect(prompt).toContain('qwen-thinking');
+    expect(prompt).toContain('Template class:** qwen');
+    expect(prompt).not.toContain('###BASELINE_SHAPE_CONTENT###');
+    expect(prompt).not.toContain('###FAILURE_MODE_SUMMARY###');
+    expect(prompt).not.toContain('###SHAPE_NAME###');
+    expect(prompt).not.toContain('###TEMPLATE_CLASS###');
+  });
+
+  it('substitutes all 4 placeholders in non-Qwen template', () => {
+    const prompt = buildOraclePrompt({
+      shape: 'claude',
+      baselineShapeContent: 'export const claudeShape = {...};',
+      failureModeSummary: '1. conflation\n2. weak-synthesis\n3. fabrication',
+    });
+    expect(prompt).toContain('export const claudeShape');
+    expect(prompt).toContain('conflation');
+    expect(prompt).toContain('claude');
+    expect(prompt).toContain('Template class:** non-qwen');
+  });
+
+  it('Qwen-targeted shapes get the same template (qwen-thinking + qwen-non-thinking interchangeable)', () => {
+    const a = buildOraclePrompt({
+      shape: 'qwen-thinking',
+      baselineShapeContent: 'X',
+      failureModeSummary: 'Y',
+    });
+    const b = buildOraclePrompt({
+      shape: 'qwen-non-thinking',
+      baselineShapeContent: 'X',
+      failureModeSummary: 'Y',
+    });
+    // Same template body, but ###SHAPE_NAME### is substituted differently
+    expect(a.replace(/qwen-thinking/g, 'X')).toBe(b.replace(/qwen-non-thinking/g, 'X'));
+  });
+});

benchmarks/gepa/tests/faza-1/null-baseline-shape-override.test.ts

@@ -0,0 +1,48 @@
+/**
+ * GEPA Faza 1 — regression test for null-baseline promptShapeOverride bug.
+ *
+ * Per Amendment 6: the original NULL-baseline runner passed `shape: PromptShape`
+ * to runOneEval but never forwarded it to runRetrievalAgentLoop, meaning all
+ * 40 evals used the model-alias-default shape (qwen-thinking for Qwen subject)
+ * regardless of the per-shape evaluation label.
+ *
+ * Fix: pass `promptShapeOverride: shape.name` to runRetrievalAgentLoop.
+ *
+ * This test verifies the fix is present in the script source — a structural
+ * source-text invariant. A behavioral test would require refactoring the runner
+ * to expose a testable function; for the Faza 1 timeline, source-text check is
+ * sufficient regression protection.
+ */
+
+import { describe, expect, it } from 'vitest';
+import * as fs from 'node:fs';
+import * as path from 'node:path';
+
+const RUNNER_PATH = path.resolve(__dirname, '../../scripts/faza-1/run-null-baseline.ts');
+
+describe('Amendment 6 regression — null-baseline promptShapeOverride wiring', () => {
+  const source = fs.readFileSync(RUNNER_PATH, 'utf-8');
+
+  it('script source contains promptShapeOverride passed to runRetrievalAgentLoop', () => {
+    // The fix introduces the literal `promptShapeOverride: shape.name` in the
+    // runRetrievalAgentLoop call within runOneEval.
+    expect(source).toContain('promptShapeOverride: shape.name');
+  });
+
+  it('script source contains Amendment 6 bug-fix annotation comment', () => {
+    expect(source).toContain('Amendment 6');
+    expect(source).toContain('bug fix per Amendment 6');
+  });
+
+  it('script source still passes modelAlias = SUBJECT_ALIAS', () => {
+    // Make sure the fix didn't inadvertently change the subject (which is
+    // shape-independent: subject is always Qwen, override controls shape).
+    expect(source).toContain('modelAlias: SUBJECT_ALIAS');
+  });
+
+  it('runOneEval receives shape parameter typed as PromptShape', () => {
+    // The shape parameter must remain in scope so promptShapeOverride: shape.name
+    // resolves correctly.
+    expect(source).toMatch(/runOneEval\s*\(\s*shape\s*:\s*PromptShape/);
+  });
+});

benchmarks/gepa/tests/faza-1/selection.test.ts

@@ -0,0 +1,206 @@
+/**
+ * GEPA Faza 1 — selection tests.
+ *
+ * Coverage targets:
+ *   - top-1-per-shape selection by fitness
+ *   - acceptance verdict per best-per-shape
+ *   - run-aggregate §F.2 condition (≥3/5 shapes positive delta)
+ *   - error handling: missing baseline entry
+ */
+
+import { describe, expect, it } from 'vitest';
+import { runSelection } from '../../src/faza-1/selection.js';
+import { type CandidateMetrics, type ShapeName } from '../../src/faza-1/types.js';
+
+function makeCandidate(
+  shape: ShapeName,
+  candidateId: string,
+  trioII: number,
+  retrieval: number = 1.5,
+  cost: number = 0.5,
+): CandidateMetrics {
+  return {
+    candidateId,
+    shape,
+    evaluations: [],
+    trioStrictPassRateII: trioII,
+    trioStrictPassRateI: trioII,  // simplified for test
+    meanRetrievalCallsPerTask: retrieval,
+    meanCostUsd: cost,
+  };
+}
+
+describe('runSelection — top-1 per shape', () => {
+  it('selects highest-fitness candidate per shape', () => {
+    const candidatesPerShape = new Map<ShapeName, CandidateMetrics[]>([
+      ['claude', [
+        makeCandidate('claude', 'c-low', 0.30),
+        makeCandidate('claude', 'c-high', 0.50),
+        makeCandidate('claude', 'c-mid', 0.40),
+      ]],
+    ]);
+    const baselineRate = new Map<ShapeName, number>([['claude', 0.20]]);
+    const baselineCost = new Map<ShapeName, number>([['claude', 0.50]]);
+
+    const report = runSelection({
+      candidatesPerShape,
+      baselineTrioStrictPassRateII: baselineRate,
+      baselineMedianCostUsd: baselineCost,
+    });
+
+    expect(report.perShape).toHaveLength(1);
+    expect(report.perShape[0].shape).toBe('claude');
+    expect(report.perShape[0].bestCandidate.candidateId).toBe('c-high');
+    expect(report.perShape[0].allCandidatesRanked).toHaveLength(3);
+    // Sorted descending by fitness
+    expect(report.perShape[0].allCandidatesRanked[0].candidate.candidateId).toBe('c-high');
+    expect(report.perShape[0].allCandidatesRanked[2].candidate.candidateId).toBe('c-low');
+  });
+
+  it('skips shapes with empty candidate lists', () => {
+    const candidatesPerShape = new Map<ShapeName, CandidateMetrics[]>([
+      ['claude', []],
+      ['gpt', [makeCandidate('gpt', 'g1', 0.40)]],
+    ]);
+    const baselineRate = new Map<ShapeName, number>([
+      ['claude', 0.20],
+      ['gpt', 0.20],
+    ]);
+    const baselineCost = new Map<ShapeName, number>([
+      ['claude', 0.50],
+      ['gpt', 0.50],
+    ]);
+
+    const report = runSelection({
+      candidatesPerShape,
+      baselineTrioStrictPassRateII: baselineRate,
+      baselineMedianCostUsd: baselineCost,
+    });
+
+    expect(report.perShape).toHaveLength(1);
+    expect(report.perShape[0].shape).toBe('gpt');
+  });
+
+  it('throws on missing baseline trio_strict rate for a shape', () => {
+    const candidatesPerShape = new Map<ShapeName, CandidateMetrics[]>([
+      ['claude', [makeCandidate('claude', 'c1', 0.30)]],
+    ]);
+    expect(() =>
+      runSelection({
+        candidatesPerShape,
+        baselineTrioStrictPassRateII: new Map(),
+        baselineMedianCostUsd: new Map([['claude', 0.50]]),
+      }),
+    ).toThrow(/missing baseline trio_strict/);
+  });
+
+  it('throws on missing baseline median cost for a shape', () => {
+    const candidatesPerShape = new Map<ShapeName, CandidateMetrics[]>([
+      ['claude', [makeCandidate('claude', 'c1', 0.30)]],
+    ]);
+    expect(() =>
+      runSelection({
+        candidatesPerShape,
+        baselineTrioStrictPassRateII: new Map([['claude', 0.20]]),
+        baselineMedianCostUsd: new Map(),
+      }),
+    ).toThrow(/missing baseline median cost/);
+  });
+});
+
+describe('runSelection — Qwen retrieval engagement bonus affects ranking', () => {
+  it('Qwen candidate with higher retrieval engagement outranks higher trio_strict but low retrieval', () => {
+    // Candidate A: trio=0.40, retrieval=1.0 (-0.05 bonus → fitness ~0.35)
+    // Candidate B: trio=0.36, retrieval=2.0 (+0.05 bonus → fitness ~0.41)
+    // B wins despite lower trio_strict, because the bonus tips it
+    const candidatesPerShape = new Map<ShapeName, CandidateMetrics[]>([
+      ['qwen-thinking', [
+        makeCandidate('qwen-thinking', 'A-high-trio-low-retrieval', 0.40, 1.0),
+        makeCandidate('qwen-thinking', 'B-mid-trio-high-retrieval', 0.36, 2.0),
+      ]],
+    ]);
+
+    const report = runSelection({
+      candidatesPerShape,
+      baselineTrioStrictPassRateII: new Map([['qwen-thinking', 0.20]]),
+      baselineMedianCostUsd: new Map([['qwen-thinking', 0.50]]),
+    });
+
+    expect(report.perShape[0].bestCandidate.candidateId).toBe('B-mid-trio-high-retrieval');
+  });
+
+  it('Non-Qwen ranking depends on trio_strict alone (no retrieval bonus tip)', () => {
+    // Same trio_strict + retrieval setup as above but for claude shape
+    // Now A wins (higher trio_strict) because no retrieval bonus applies
+    const candidatesPerShape = new Map<ShapeName, CandidateMetrics[]>([
+      ['claude', [
+        makeCandidate('claude', 'A-high-trio', 0.40, 1.0),
+        makeCandidate('claude', 'B-mid-trio', 0.36, 2.0),
+      ]],
+    ]);
+
+    const report = runSelection({
+      candidatesPerShape,
+      baselineTrioStrictPassRateII: new Map([['claude', 0.20]]),
+      baselineMedianCostUsd: new Map([['claude', 0.50]]),
+    });
+
+    expect(report.perShape[0].bestCandidate.candidateId).toBe('A-high-trio');
+  });
+});
+
+describe('runSelection — run-aggregate §F.2 condition (≥3/5 shapes positive delta)', () => {
+  function setupAllShapes(deltas: Record<ShapeName, number>) {
+    const candidates = new Map<ShapeName, CandidateMetrics[]>();
+    const baselineRates = new Map<ShapeName, number>();
+    const baselineCosts = new Map<ShapeName, number>();
+    const BASELINE = 0.20;
+    for (const [shape, delta] of Object.entries(deltas) as Array<[ShapeName, number]>) {
+      candidates.set(shape, [
+        makeCandidate(shape, `${shape}-best`, BASELINE + delta / 100, 2.0),
+      ]);
+      baselineRates.set(shape, BASELINE);
+      baselineCosts.set(shape, 0.5);
+    }
+    return { candidates, baselineRates, baselineCosts };
+  }
+
+  it('PASS §F.2: 5/5 shapes positive', () => {
+    const { candidates, baselineRates, baselineCosts } = setupAllShapes({
+      'claude': 6, 'qwen-thinking': 6, 'qwen-non-thinking': 6, 'gpt': 6, 'generic-simple': 6,
+    });
+    const report = runSelection({
+      candidatesPerShape: candidates,
+      baselineTrioStrictPassRateII: baselineRates,
+      baselineMedianCostUsd: baselineCosts,
+    });
+    expect(report.runAggregate.shapesWithPositiveDelta).toBe(5);
+    expect(report.runAggregate.condition2Pass).toBe(true);
+  });
+
+  it('PASS §F.2: 3/5 shapes positive (boundary)', () => {
+    const { candidates, baselineRates, baselineCosts } = setupAllShapes({
+      'claude': 6, 'qwen-thinking': 6, 'qwen-non-thinking': 6, 'gpt': -2, 'generic-simple': -2,
+    });
+    const report = runSelection({
+      candidatesPerShape: candidates,
+      baselineTrioStrictPassRateII: baselineRates,
+      baselineMedianCostUsd: baselineCosts,
+    });
+    expect(report.runAggregate.shapesWithPositiveDelta).toBe(3);
+    expect(report.runAggregate.condition2Pass).toBe(true);
+  });
+
+  it('FAIL §F.2: 2/5 shapes positive', () => {
+    const { candidates, baselineRates, baselineCosts } = setupAllShapes({
+      'claude': 6, 'qwen-thinking': 6, 'qwen-non-thinking': -2, 'gpt': -2, 'generic-simple': -2,
+    });
+    const report = runSelection({
+      candidatesPerShape: candidates,
+      baselineTrioStrictPassRateII: baselineRates,
+      baselineMedianCostUsd: baselineCosts,
+    });
+    expect(report.runAggregate.shapesWithPositiveDelta).toBe(2);
+    expect(report.runAggregate.condition2Pass).toBe(false);
+  });
+});

benchmarks/harness/README.md

@@ -0,0 +1,125 @@
+# @waggle/benchmarks-harness
+
+**Four-cell ablation harness** for Waggle memory + prompt-evolution benchmarks.
+Canonical model: **Qwen/Qwen3.6-35B-A3B** (see `docs/plans/BACKLOG-MASTER-2026-04-18.md` §H12).
+
+## Four cells — the ablation grid
+
+Same dataset, same seed, same model across all four. Difference at report
+time between a baseline cell and a treatment cell isolates the causal
+contribution of the ablated component.
+
+| Cell | Memory | Prompt evolution | Purpose |
+|------|--------|------------------|---------|
+| `raw` | no | no | Baseline — LLM only, stateless per turn. |
+| `filtered` | yes | no | Isolates the memory-layer contribution. (Sprint 12 rename: was `memory-only`.) |
+| `compressed` | no | yes | Isolates the GEPA prompt-evolution contribution. (Sprint 12 rename: was `evolve-only`.) |
+| `full-context` | yes | yes | Joint contribution (memory × evolution). (Sprint 12 rename: was `full-stack`.) |
+
+## Controls (not cells — diagnostic)
+
+| Control | Purpose |
+|---------|---------|
+| `verbose-fixed` | Day-1 sanity check. Tells the model to answer verbosely. On a short-factoid benchmark, this should **underperform** `raw`. If it doesn't, audit the harness. |
+
+Out-of-scope for Week 1 (deferred to Week 2 / Week 3 per brief):
+
+- `naive-rag` control
+- `oracle-memory` ceiling
+- Llama 3.1 8B + Opus 4.6 model integrations
+- Gemma 2 9B probe
+- Full τ-bench + LongMemEval loaders (synthetic fallback works today)
+
+## CLI
+
+Run from repo root via the `bench` script, or invoke `tsx` directly:
+
+```bash
+# Day 1 sanity check — one cell, one instance, dry-run (no LLM required)
+npm run bench -- --cell raw --dataset locomo --limit 1 --model qwen3.6-35b-a3b
+
+# Day 2 pre-flight smoke — all 4 cells, 50 instances each
+npm run bench -- --all-cells --dataset locomo --limit 50 --model qwen3.6-35b-a3b --budget 115
+
+# Full run
+npm run bench -- --all-cells --dataset locomo --full --model qwen3.6-35b-a3b
+
+# Verbose-fixed control (Day 1 sanity)
+npm run bench -- --control verbose-fixed --dataset locomo --limit 50 --model qwen3.6-35b-a3b
+```
+
+### Flags
+
+| Flag | Default | Notes |
+|------|---------|-------|
+| `--cell <name>` | — | One of `raw \| filtered \| compressed \| full-context`. |
+| `--all-cells` | — | Run all four sequentially with the same dataset + seed. |
+| `--control <name>` | — | Currently only `verbose-fixed`. |
+| `--dataset <id>` | `synthetic` | `synthetic \| locomo \| longmemeval`. External datasets throw `DatasetMissingError` if the canonical archive is absent; set `BENCH_SYNTHETIC_DATASET=1` to re-enable the dev-only synthetic fallback. |
+| `--limit N` | `10` | Cap instances. `--full` = no cap. |
+| `--model <id>` | `qwen3.6-35b-a3b` | Id from `config/models.json`. |
+| `--seed N` | `42` | Reproducibility — same seed → same instance order + dry-run output. |
+| `--budget USD` | `Infinity` | Hard USD cap. Run stops when cumulative cost exceeds. |
+| `--output <path>` | auto | JSONL output path. Default: `../results/<cell>-<dataset>-<ts>.jsonl`. |
+| `--dry-run` | auto | Stub LLM. Default-on when `LITELLM_URL` env is unset. |
+| `--live` | — | Force real LLM calls even if `LITELLM_URL` unset. |
+| `--help`, `-h` | — | Usage summary. |
+
+### Environment
+
+| Var | Default |
+|-----|---------|
+| `LITELLM_URL` | `http://localhost:4000` |
+| `LITELLM_API_KEY` | `sk-waggle-dev` |
+
+## Output — per-instance JSONL
+
+One line per instance, flat shape (friendly to `jq`, DuckDB, pandas):
+
+```jsonl
+{"turnId":"9fcd1d25-979f-4094-9633-f9bc30471f08","cell":"raw","instance_id":"synthetic_001","model":"qwen3.6-35b-a3b","seed":42,"accuracy":1,"p50_latency_ms":1,"p95_latency_ms":1,"usd_per_query":0.00012,"failure_mode":null}
+```
+
+**turnId correlation** — the `turnId` field is a UUID v4 matching the
+per-turn trace ID generated by the production agent orchestrator
+(`packages/agent/src/turn-context.ts`, H-AUDIT-1). For `raw` and control
+cells, where the agent loop isn't exercised, the harness generates the
+turnId itself so every row has a correlation key.
+
+## Aggregate summary
+
+Written alongside the JSONL as `<name>.summary.json`:
+
+```json
+{
+  "run": { "kind": "cell", "name": "raw", "dataset": "synthetic", "model": "qwen3.6-35b-a3b", "seed": 42, ... },
+  "counts": { "total": 50, "completed": 50, "failed": 0, "budgetStoppedAt": null },
+  "metrics": { "meanAccuracy": 0.82, "p50LatencyMs": 230, "p95LatencyMs": 450, "totalUsd": 0.011, "meanUsdPerQuery": 0.00022 },
+  "failureModes": {}
+}
+```
+
+## Datasets
+
+- `synthetic` — **built-in**, 60 instances, no external download required. Used by smoke tests and as a fallback when external data is missing.
+- `locomo` — expects `benchmarks/data/locomo/locomo.jsonl`. Gitignored. Downloaded separately (Week 1 work).
+- `longmemeval` — expects `benchmarks/data/longmemeval/longmemeval.jsonl`. Same pattern.
+
+## Reproducibility
+
+`--seed N` fully determines:
+
+1. The order instances are sampled from the dataset
+2. The dry-run LLM stub's output (the stub is deterministic from the user prompt alone)
+
+For live LLM runs, `--seed` is still emitted into every JSONL record so
+downstream analysis can pin every row to a seed value; the model's own
+sampling is controlled via `temperature=0.0` in `src/llm.ts`.
+
+## Smoke test
+
+```bash
+npx vitest run benchmarks/harness/tests/smoke.test.ts
+```
+
+Covers every acceptance criterion from the Bucket 1 Task 7 brief.

benchmarks/harness/config/datasets.json

@@ -0,0 +1,32 @@
+{
+  "synthetic": {
+    "id": "synthetic",
+    "displayName": "Synthetic scaffold dataset (60 instances, built-in)",
+    "dataPath": "synthetic/placeholder.jsonl",
+    "source": "synthetic"
+  },
+  "locomo": {
+    "id": "locomo",
+    "displayName": "LoCoMo (snap-research/locomo, canonical 1531-instance non-adversarial eval set)",
+    "dataPath": "locomo/locomo-1540.jsonl",
+    "source": "external"
+  },
+  "longmemeval": {
+    "id": "longmemeval",
+    "displayName": "LongMemEval V1 S-variant (xiaowu0162/longmemeval-cleaned, 500 questions, 115K tokens)",
+    "dataPath": "longmemeval/longmemeval.jsonl",
+    "source": "external"
+  },
+  "beam-128k": {
+    "id": "beam-128k",
+    "displayName": "BEAM 128K (mohammadtavakoli78/BEAM, ICLR 2026, 20 conversations ~128K tokens)",
+    "dataPath": "beam/beam-128K.jsonl",
+    "source": "external"
+  },
+  "beam-1m": {
+    "id": "beam-1m",
+    "displayName": "BEAM 1M (mohammadtavakoli78/BEAM, ICLR 2026, 35 conversations ~1M tokens)",
+    "dataPath": "beam/beam-1M.jsonl",
+    "source": "external"
+  }
+}

benchmarks/harness/config/models.json

@@ -0,0 +1,154 @@
+{
+  "qwen3.6-35b-a3b": {
+    "id": "qwen3.6-35b-a3b",
+    "displayName": "Qwen3.6-35B-A3B (canonical)",
+    "provider": "alibaba",
+    "litellmModel": "dashscope/qwen3.6-35b-a3b",
+    "pricePerMillionInput": 0.20,
+    "pricePerMillionOutput": 0.80,
+    "contextWindow": 262144,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "DashScope does not expose immutable model snapshots; floating alias mandated by B3 addendum § 5"
+  },
+  "qwen3.6-35b-a3b-stage2": {
+    "id": "qwen3.6-35b-a3b-stage2",
+    "displayName": "Qwen3.6-35B-A3B (Stage 2 LOCKED config — thinking=on, 64K)",
+    "provider": "alibaba",
+    "litellmModel": "qwen3.6-35b-a3b-via-openrouter",
+    "pricePerMillionInput": 0.20,
+    "pricePerMillionOutput": 0.80,
+    "contextWindow": 262144,
+    "stage2Config": {
+      "thinking": true,
+      "maxTokens": 64000,
+      "reasoningShape": "openrouter-unified"
+    },
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "OpenRouter bridge to DashScope does not expose immutable model snapshots; floating alias mandated by B3 addendum § 5"
+  },
+  "qwen3.6-35b-a3b-via-openrouter": {
+    "id": "qwen3.6-35b-a3b-via-openrouter",
+    "displayName": "Qwen3.6-35B-A3B (C3 Stage 2 mini subject — alias name matches LiteLLM route, stage2 LOCKED config applied)",
+    "provider": "alibaba",
+    "litellmModel": "qwen3.6-35b-a3b-via-openrouter",
+    "pricePerMillionInput": 0.20,
+    "pricePerMillionOutput": 0.80,
+    "contextWindow": 262144,
+    "stage2Config": {
+      "thinking": true,
+      "maxTokens": 64000,
+      "reasoningShape": "openrouter-unified"
+    },
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Sprint 12 Task 2 C3 Stage 2 mini (2026-04-23): sibling alias of `qwen3.6-35b-a3b-stage2` that exposes the LiteLLM route name as the models.json key — lets the manifest `target_model: qwen3.6-35b-a3b-via-openrouter` match both the pre-registration audit trail AND the CLI --model arg verbatim. Same routing: LiteLLM -> OpenRouter -> Alibaba bridge (slug `openrouter/qwen/qwen3.5-35b-a3b`). OpenRouter bridge to DashScope does not expose immutable model snapshots; floating alias mandated by B3 addendum § 5."
+  },
+  "qwen3.6-35b-a3b-via-dashscope-direct": {
+    "id": "qwen3.6-35b-a3b-via-dashscope-direct",
+    "displayName": "Qwen3.6-35B-A3B (C3 Stage 2 Mini Retry v3 PRIMARY — DashScope-intl direct, TRUE 3.6)",
+    "provider": "alibaba",
+    "litellmModel": "qwen3.6-35b-a3b-via-dashscope-direct",
+    "pricePerMillionInput": 0.20,
+    "pricePerMillionOutput": 0.80,
+    "contextWindow": 262144,
+    "stage2Config": {
+      "thinking": true,
+      "maxTokens": 16000,
+      "reasoningShape": "dashscope-native"
+    },
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Sprint 12 Task 2 C3 Stage 2 Mini Retry v3 (2026-04-23): models.json key for the DashScope-intl direct route (litellm-config.yaml:228 alias `qwen3.6-35b-a3b-via-dashscope-direct` routes to `openai/qwen3.6-35b-a3b` @ https://dashscope-intl.aliyuncs.com/compatible-mode/v1 with DASHSCOPE_API_KEY). Delivers TRUE Qwen 3.6-35B-A3B (not the 3.5 regress of the OpenRouter bridge). thinking=on, max_tokens=16000 per v3 manifest §2.1 (reduced from v2 64000 to avoid tail-latency timeouts). DashScope-intl does not expose immutable model snapshots; floating alias mandated by B3 addendum § 5."
+  },
+  "qwen3.6-35b-a3b-local": {
+    "id": "qwen3.6-35b-a3b-local",
+    "displayName": "Qwen3.6-35B-A3B (local vLLM)",
+    "provider": "local",
+    "litellmModel": "openai/qwen3.6-35b-a3b",
+    "pricePerMillionInput": 0.0,
+    "pricePerMillionOutput": 0.0,
+    "contextWindow": 262144,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Local vLLM endpoint does not expose immutable model snapshots; floating alias mandated by B3 addendum § 5"
+  },
+  "llama-3.1-8b-instruct": {
+    "id": "llama-3.1-8b-instruct",
+    "displayName": "Llama 3.1 8B Instruct (Week 2 placeholder)",
+    "provider": "local",
+    "litellmModel": "openai/llama-3.1-8b-instruct",
+    "pricePerMillionInput": 0.0,
+    "pricePerMillionOutput": 0.0,
+    "contextWindow": 131072,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Local vLLM endpoint does not expose immutable model snapshots; floating alias mandated by B3 addendum § 5"
+  },
+  "claude-opus-4-6": {
+    "id": "claude-opus-4-6",
+    "displayName": "Claude Opus 4.6 (Week 2 placeholder, frontier baseline)",
+    "provider": "anthropic",
+    "litellmModel": "claude-opus-4-6",
+    "pricePerMillionInput": 15.0,
+    "pricePerMillionOutput": 75.0,
+    "contextWindow": 200000,
+    "pinning_surface": "anthropic_immutable",
+    "pinning_surface_carve_out_reason": null
+  },
+  "claude-opus-4-7": {
+    "id": "claude-opus-4-7",
+    "displayName": "Claude Opus 4.7 (Sprint 11 Task 2.2 primary judge)",
+    "provider": "anthropic",
+    "litellmModel": "claude-opus-4-7",
+    "pricePerMillionInput": 15.0,
+    "pricePerMillionOutput": 75.0,
+    "contextWindow": 200000,
+    "pinning_surface": "anthropic_immutable",
+    "pinning_surface_carve_out_reason": null,
+    "judge_role": "primary"
+  },
+  "gpt-5.4": {
+    "id": "gpt-5.4",
+    "displayName": "GPT-5.4 (Sprint 11 Task 2.2 primary judge, direct via LiteLLM→OpenAI)",
+    "provider": "openai_via_openrouter",
+    "litellmModel": "gpt-5.4",
+    "pricePerMillionInput": 10.0,
+    "pricePerMillionOutput": 30.0,
+    "contextWindow": 200000,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Sprint 12 Task 2 C3 Stage 2 mini (2026-04-23): routing pivoted from OpenRouter bridge to direct OpenAI Chat Completions via LiteLLM local alias `gpt-5.4` → `openai/gpt-5.4` (eliminates ~5-15% OpenRouter middleware markup + preserves token-level telemetry for EU AI Act Art. 14 replay). OpenAI does not expose immutable model snapshots for the gpt-5.x family; floating alias mandated by B3 addendum § 5.",
+    "judge_role": "primary"
+  },
+  "gemini-3.1": {
+    "id": "gemini-3.1",
+    "displayName": "Gemini 3.1 Pro Preview (legacy OpenRouter bridge route)",
+    "provider": "google_via_openrouter",
+    "litellmModel": "openrouter/google/gemini-3.1-pro-preview",
+    "pricePerMillionInput": 3.5,
+    "pricePerMillionOutput": 10.5,
+    "contextWindow": 1000000,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Legacy OpenRouter-bridge route; Sprint 12 Task 2 C3 Stage 2 mini (2026-04-23) pivoted judge routing to direct via LiteLLM local alias `gemini-3.1-pro` — see sibling entry below. This entry kept for backward compat with pre-Sprint-12 artefacts; floating alias mandated by B3 addendum § 5",
+    "judge_role": "primary"
+  },
+  "gemini-3.1-pro": {
+    "id": "gemini-3.1-pro",
+    "displayName": "Gemini 3.1 Pro Preview (Sprint 12 Task 2 primary judge, direct via LiteLLM→Google AI Studio)",
+    "provider": "google_via_openrouter",
+    "litellmModel": "gemini-3.1-pro",
+    "pricePerMillionInput": 3.5,
+    "pricePerMillionOutput": 10.5,
+    "contextWindow": 1000000,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "Sprint 12 Task 2 C3 Stage 2 mini (2026-04-23): direct-provider routing via LiteLLM local alias `gemini-3.1-pro` → `gemini/gemini-3.1-pro-preview` (Google AI Studio). Google ships the 3.1 Pro generation as `-preview` suffix only — no stable alias exists as of 2026-04-23 01:47 UTC (GET openrouter.ai/v1/models). Direct routing eliminates OpenRouter middleware markup + preserves native reasoning_tokens telemetry. Verify at replay time that `-preview` still resolves to the same underlying checkpoint; Google guarantees preview stability within a release window but not across release cycles. Floating alias mandated by B3 addendum § 5 (no immutable upstream snapshot surface).",
+    "judge_role": "primary"
+  },
+  "grok-4.20": {
+    "id": "grok-4.20",
+    "displayName": "Grok 4.20 (Sprint 11 B2 LOCK quadri-vendor tie-break reserve)",
+    "provider": "xai_via_openrouter",
+    "litellmModel": "openrouter/x-ai/grok-4.20",
+    "pricePerMillionInput": 5.0,
+    "pricePerMillionOutput": 15.0,
+    "contextWindow": 131072,
+    "pinning_surface": "floating_alias",
+    "pinning_surface_carve_out_reason": "xAI does not expose immutable model snapshots through OpenRouter routing layer; floating alias mandated by B3 addendum § 5",
+    "judge_role": "reserve"
+  }
+}

benchmarks/harness/package.json

@@ -0,0 +1,28 @@
+{
+  "name": "@waggle/benchmarks-harness",
+  "version": "0.1.0",
+  "description": "Four-cell ablation harness for Waggle memory + evolution benchmarks",
+  "type": "module",
+  "private": true,
+  "engines": {
+    "node": ">=20.0.0"
+  },
+  "main": "dist/runner.js",
+  "bin": {
+    "waggle-bench": "./dist/runner.js"
+  },
+  "scripts": {
+    "build": "tsc",
+    "test": "vitest run",
+    "bench": "tsx src/runner.ts"
+  },
+  "dependencies": {
+    "@waggle/agent": "0.1.0",
+    "@waggle/core": "*"
+  },
+  "devDependencies": {
+    "tsx": "^4.0.0",
+    "typescript": "^5.0.0",
+    "vitest": "^3.0.0"
+  }
+}

benchmarks/harness/scripts/_merge-pilot.ts

@@ -0,0 +1,294 @@
+/**
+ * ANSWER-MERGE self-ensembling pilot (BEAM 1M).
+ *
+ * Hypothesis: BEAM per-question scores have high re-run variance and compound
+ * rubric nuggets reward content UNION. Merging two INDEPENDENT answers to the
+ * same question (same v2 config) into one, then re-judging, may beat the single
+ * headline answer — for free at inference time when the two answers already
+ * exist.
+ *
+ * PAIR SOURCES (all in benchmarks/results/beam/):
+ *   - HEADLINE (canonical): beam-1m-FULL700-gpt5-retv2.jsonl, first row per
+ *     instance_id → 700 canonical answers+scores+rubric.
+ *   - ALT (independent 2nd answer under the same config):
+ *       (a) beam-1m-FULL700-gpt5-retv2.backup.jsonl — for ids with 2+ rows whose
+ *           answer TEXTS DIFFER, the LATER row is an independent re-answer.
+ *       (b) beam-1m-cal-gpt5-retrieval-v2.jsonl — 50 questions answered again
+ *           under the same config.
+ *   A pair is kept iff both answers are non-empty, materially differ (normalized
+ *   inequality), and are not BOTH the exact IDK sentence.
+ *
+ * MERGE (gpt-5) → JUDGE merged answer with the official nugget judge (gpt-5),
+ * computeTau:false so we compare the merged plain nugget-mean against the
+ * headline plain nugget-mean `score` (apples-to-apples; tau is a non-headline
+ * diagnostic and adds many costly calls).
+ *
+ * Budget-guarded ($8 hard stop). Pair order is deterministically shuffled
+ * (seed 42) so a partial run is still a fair sample. Resumable: already-written
+ * instance_ids are skipped.
+ *
+ * Run:  npx tsx benchmarks/harness/scripts/_merge-pilot.ts [--budget 8]
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import { createBeamOpenAiClient, OPENAI_PRICING } from '../src/beam-openai-client.js';
+import { judgeQuestion } from '../src/beam-nugget-judge.js';
+
+const RESULTS_DIR = path.resolve('benchmarks/results/beam');
+const HEADLINE = path.join(RESULTS_DIR, 'beam-1m-FULL700-gpt5-retv2.jsonl');
+const BACKUP = path.join(RESULTS_DIR, 'beam-1m-FULL700-gpt5-retv2.backup.jsonl');
+const CAL = path.join(RESULTS_DIR, 'beam-1m-cal-gpt5-retrieval-v2.jsonl');
+const OUT = path.join(RESULTS_DIR, 'beam-1m-merge-pilot.jsonl');
+
+const MERGE_SYSTEM =
+  'You are combining two draft answers to the same question, both produced from ' +
+  'the same memory context. Produce ONE final answer that includes ALL specific, ' +
+  'non-contradictory content from both drafts (names, dates, numbers, versions, ' +
+  'events, causes, outcomes), organized clearly, with no meta-commentary about ' +
+  'drafts. If the drafts disagree on a fact, state the contradiction explicitly ' +
+  'and present both values. If BOTH drafts say there is not enough information, ' +
+  "output exactly: I don't have enough information to answer this question. " +
+  "If only one draft has substantive content, use that draft's content.";
+
+interface BeamRow {
+  instance_id: string;
+  conv?: number;
+  memory_ability: string;
+  question: string;
+  answer: string;
+  score: number;
+  nugget_scores: Array<{ nugget: string; score: number; reason: string }>;
+}
+
+function readJsonl(p: string): BeamRow[] {
+  return fs
+    .readFileSync(p, 'utf-8')
+    .split('\n')
+    .filter(l => l.trim())
+    .map(l => {
+      try {
+        return JSON.parse(l) as BeamRow;
+      } catch {
+        return null;
+      }
+    })
+    .filter((r): r is BeamRow => r !== null);
+}
+
+const norm = (t: string): string =>
+  (t ?? '')
+    .replace(/[‘’]/g, "'")
+    .replace(/[“”]/g, '"')
+    .replace(/\s+/g, ' ')
+    .trim()
+    .toLowerCase();
+
+const IDK = "i don't have enough information to answer this question";
+const isExactIDK = (t: string): boolean => {
+  const n = norm(t);
+  return n === IDK || n === `${IDK}.`;
+};
+const looksIDK = (t: string): boolean => norm(t).startsWith(IDK);
+const isEmpty = (t: string): boolean => !t || !t.trim();
+
+/** Deterministic PRNG (mulberry32) + Fisher-Yates for a seed-42 shuffle. */
+function mulberry32(seed: number): () => number {
+  let a = seed >>> 0;
+  return () => {
+    a |= 0;
+    a = (a + 0x6d2b79f5) | 0;
+    let t = Math.imul(a ^ (a >>> 15), 1 | a);
+    t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+function shuffle<T>(arr: T[], seed: number): T[] {
+  const rnd = mulberry32(seed);
+  const a = [...arr];
+  for (let i = a.length - 1; i > 0; i--) {
+    const j = Math.floor(rnd() * (i + 1));
+    [a[i], a[j]] = [a[j], a[i]];
+  }
+  return a;
+}
+
+interface Pair {
+  instanceId: string;
+  memoryAbility: string;
+  question: string;
+  headline: BeamRow;
+  alt: BeamRow;
+  altSource: 'backup' | 'cal';
+}
+
+function buildPairs(): Pair[] {
+  const headline = readJsonl(HEADLINE);
+  const headMap = new Map<string, BeamRow>();
+  for (const r of headline) if (!headMap.has(r.instance_id)) headMap.set(r.instance_id, r);
+
+  const usable = (head: BeamRow, alt: BeamRow): boolean => {
+    if (isEmpty(alt.answer) || isEmpty(head.answer)) return false;
+    if (norm(alt.answer) === norm(head.answer)) return false; // not materially different
+    if (isExactIDK(alt.answer) && isExactIDK(head.answer)) return false;
+    return true;
+  };
+
+  // (a) backup: later row among 2+ distinct-answer rows.
+  const backup = readJsonl(BACKUP);
+  const byId = new Map<string, BeamRow[]>();
+  for (const r of backup) {
+    if (!byId.has(r.instance_id)) byId.set(r.instance_id, []);
+    byId.get(r.instance_id)!.push(r);
+  }
+  const pairs = new Map<string, Pair>();
+  for (const [id, rows] of byId) {
+    if (rows.length < 2) continue;
+    if (new Set(rows.map(r => norm(r.answer))).size < 2) continue; // all identical
+    const head = headMap.get(id);
+    if (!head) continue;
+    const alt = rows[rows.length - 1];
+    if (!usable(head, alt)) continue;
+    pairs.set(id, {
+      instanceId: id,
+      memoryAbility: head.memory_ability,
+      question: head.question,
+      headline: head,
+      alt,
+      altSource: 'backup',
+    });
+  }
+
+  // (b) cal: independent re-answers; add ids not already covered by backup.
+  const cal = readJsonl(CAL);
+  const calMap = new Map<string, BeamRow>();
+  for (const r of cal) if (!calMap.has(r.instance_id)) calMap.set(r.instance_id, r);
+  for (const [id, alt] of calMap) {
+    if (pairs.has(id)) continue;
+    const head = headMap.get(id);
+    if (!head) continue;
+    if (!usable(head, alt)) continue;
+    pairs.set(id, {
+      instanceId: id,
+      memoryAbility: head.memory_ability,
+      question: head.question,
+      headline: head,
+      alt,
+      altSource: 'cal',
+    });
+  }
+
+  // Canonical sort (by instance_id) then deterministic seed-42 shuffle.
+  const sorted = [...pairs.values()].sort((a, b) => a.instanceId.localeCompare(b.instanceId));
+  return shuffle(sorted, 42);
+}
+
+function mergeUser(question: string, draftA: string, draftB: string): string {
+  return `QUESTION:\n${question}\n\nDRAFT A:\n${draftA}\n\nDRAFT B:\n${draftB}`;
+}
+
+async function main(): Promise<void> {
+  const budgetArg = process.argv.indexOf('--budget');
+  const BUDGET = budgetArg >= 0 ? Number(process.argv[budgetArg + 1]) : 8;
+
+  const client = createBeamOpenAiClient({ model: 'gpt-5', pricing: OPENAI_PRICING['gpt-5'] });
+
+  const allPairs = buildPairs();
+
+  // Resume: skip instance_ids already written.
+  const done = new Set<string>();
+  if (fs.existsSync(OUT)) {
+    for (const line of fs.readFileSync(OUT, 'utf-8').split('\n')) {
+      if (!line.trim()) continue;
+      try {
+        done.add((JSON.parse(line) as { instance_id: string }).instance_id);
+      } catch {
+        /* skip */
+      }
+    }
+  }
+
+  const abilityDist: Record<string, number> = {};
+  for (const p of allPairs) abilityDist[p.memoryAbility] = (abilityDist[p.memoryAbility] ?? 0) + 1;
+  console.log(`[merge-pilot] ${allPairs.length} pairs total | already done: ${done.size} | budget $${BUDGET}`);
+  console.log(`[merge-pilot] ability dist: ${JSON.stringify(abilityDist)}`);
+
+  const outStream = fs.createWriteStream(OUT, { flags: 'a' });
+  let spend = 0;
+  let processed = 0;
+  let mergeFails = 0;
+
+  for (const pair of allPairs) {
+    if (spend >= BUDGET) {
+      console.warn(`[merge-pilot] budget $${BUDGET} reached — stopping (spend=$${spend.toFixed(3)})`);
+      break;
+    }
+    if (done.has(pair.instanceId)) continue;
+
+    const rubric = pair.headline.nugget_scores.map(n => n.nugget);
+    if (rubric.length === 0) continue;
+
+    // ── MERGE (gpt-5) ──
+    const mergeRes = await client.chat({
+      system: MERGE_SYSTEM,
+      user: mergeUser(pair.question, pair.headline.answer, pair.alt.answer),
+      maxTokens: 2000,
+    });
+    spend += mergeRes.costUsd;
+    if (mergeRes.failureMode || isEmpty(mergeRes.text)) {
+      mergeFails++;
+      console.warn(`  [${pair.instanceId}] merge failed (${mergeRes.failureMode ?? 'empty'}) — skipping`);
+      continue;
+    }
+    const merged = mergeRes.text.trim();
+
+    // ── JUDGE merged answer (gpt-5, plain nugget-mean) ──
+    const { judgement, llmResults } = await judgeQuestion(
+      client,
+      { question: pair.question, rubric, memoryAbility: pair.memoryAbility, answer: merged },
+      { computeTau: false },
+    );
+    const judgeCost = llmResults.reduce((s, r) => s + r.costUsd, 0);
+    spend += judgeCost;
+
+    const headlineNug = pair.headline.nugget_scores.map(n => n.score);
+    const mergedNug = judgement.nuggetScores.map(n => n.score);
+
+    const row = {
+      instance_id: pair.instanceId,
+      memory_ability: pair.memoryAbility,
+      alt_source: pair.altSource,
+      question: pair.question,
+      score_headline: pair.headline.score,
+      score_alt: pair.alt.score,
+      score_merged: judgement.score,
+      nuggets: rubric,
+      headline_nugget_scores: headlineNug,
+      merged_nugget_scores: mergedNug,
+      headline_idk: looksIDK(pair.headline.answer),
+      alt_idk: looksIDK(pair.alt.answer),
+      merged_idk: looksIDK(merged),
+      answer_merged: merged,
+      merge_cost_usd: round4(mergeRes.costUsd),
+      judge_cost_usd: round4(judgeCost),
+    };
+    outStream.write(`${JSON.stringify(row)}\n`);
+    processed++;
+    const delta = pair.headline.score === judgement.score ? 'TIE' : judgement.score > pair.headline.score ? 'WIN' : 'LOSS';
+    process.stdout.write(
+      `  [${pair.memoryAbility.padEnd(24)}] H=${pair.headline.score.toFixed(2)} A=${pair.alt.score.toFixed(2)} M=${judgement.score.toFixed(2)} ${delta}  $${spend.toFixed(3)}\n`,
+    );
+  }
+
+  outStream.end();
+  console.log(`\n[merge-pilot] DONE. processed=${processed} mergeFails=${mergeFails} spend=$${spend.toFixed(3)} → ${OUT}`);
+}
+
+function round4(x: number): number {
+  return Math.round(x * 1e4) / 1e4;
+}
+
+main().catch(err => {
+  console.error(err);
+  process.exit(1);
+});

benchmarks/harness/scripts/_probe-contradiction.ts

@@ -0,0 +1,43 @@
+/** Precise probe: for the two failed contradiction questions on conv 1, report
+ *  the exact rank of each side's frame in top-k retrieval (raw + obs minds). */
+import { createOllamaEmbedder } from '@waggle/core';
+import { createSubstrate } from '../src/substrate.js';
+
+const CASES = [
+  {
+    q: 'Have I implemented the language detection microservice using franc v6.1.0 before?',
+    db: 'benchmarks/data/beam/minds-1M/beam_1M_1.mind', k: 30, label: 'raw langdet',
+    want: { pos: 161, neg: 325 },
+  },
+  {
+    q: 'Have I completed the translation microservice that supports 12 languages with 98% accuracy using the DeepL API v2?',
+    db: 'benchmarks/data/beam/minds-1M-obs/beam_1M_1.mind', k: 100, label: 'obs translation',
+    want: { neg: 833 },
+  },
+  {
+    q: 'Have I completed the translation microservice that supports 12 languages with 98% accuracy using the DeepL API v2?',
+    db: 'benchmarks/data/beam/minds-1M/beam_1M_1.mind', k: 30, label: 'raw translation',
+    want: {},
+  },
+];
+
+async function main() {
+  for (const c of CASES) {
+    const substrate = createSubstrate({ dbPath: c.db, embedder: createOllamaEmbedder() });
+    try {
+      const results = await substrate.search.search(c.q, { limit: c.k, gopId: 'beam_1' });
+      const ranks: Record<string, number> = {};
+      for (const [name, id] of Object.entries(c.want)) ranks[name] = results.findIndex(r => r.frame.id === id);
+      console.log(`[${c.label}] k=${c.k} retrieved=${results.length} ranks=${JSON.stringify(ranks)}`);
+      // also: any frame in results mentioning the key noun phrases of BOTH sides
+      results.forEach((r, i) => {
+        const t = r.frame.content.toLowerCase();
+        if ((t.includes('never') || t.includes("haven't")) && (t.includes('microservice') || t.includes('translation') || t.includes('language detection')))
+          console.log(`   NEGATION-ish @${i} (frame ${r.frame.id}): ${r.frame.content.slice(0, 160).replace(/\s+/g, ' ')}`);
+        if (t.includes('98%') || t.includes('93%'))
+          console.log(`   CLAIM-ish    @${i} (frame ${r.frame.id}): ${r.frame.content.slice(0, 160).replace(/\s+/g, ' ')}`);
+      });
+    } finally { substrate.close(); }
+  }
+}
+main().catch(e => { console.error(e); process.exit(1); });

benchmarks/harness/scripts/_probe-headroom.ts

@@ -0,0 +1,429 @@
+#!/usr/bin/env tsx
+/**
+ * THROWAWAY probe — retrieval-availability audit (FREE: local ollama embeddings
+ * only, NO OpenAI calls).
+ *
+ * Question: our BEAM FULL700 lost mainly on summarization / event_ordering /
+ * multi_session_reasoning. Is that an ANSWER-SIDE loss (the nugget-supporting
+ * content DID surface in top-k=30 raw turns but the answer missed it) or a
+ * RETRIEVAL-SIDE loss (the content only appears deeper, at k=60/100/150) or a
+ * NOT-IN-HAYSTACK loss (it isn't in the top-150 at all)?
+ *
+ * Method: for each FAILED nugget (score 0) sampled across many conversations,
+ * retrieve top-150 raw turns for its question ONCE, extract 2-4 distinctive key
+ * terms from the nugget text, and find the FIRST RANK at which a retrieved turn
+ * contains >= half of those terms. Bucket by rank band per ability.
+ *
+ * Term-match is a NOISY proxy — the report prints 10 random (nugget, matching
+ * turn excerpt, rank) triples so a human can eyeball validity.
+ *
+ * Run:  npx tsx benchmarks/harness/scripts/_probe-headroom.ts
+ * (ollama must be up at http://localhost:11434)
+ */
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+
+import { createOllamaEmbedder } from '@waggle/core';
+import type { SearchResult } from '@waggle/core';
+import { createSubstrate } from '../src/substrate.js';
+
+// ── config ───────────────────────────────────────────────────────────────────
+const TARGET_ABILITIES = ['summarization', 'event_ordering', 'multi_session_reasoning'];
+const PER_ABILITY = 60;          // up to N failed nuggets per ability
+const RETRIEVE_K = 150;          // top-150 raw turns per question
+const K_COST = [60, 100];        // token/cost bands to price
+const GPT5_INPUT_PER_M = 1.25;   // $/M input tokens
+const N_TRIPLES = 10;            // validation triples to print
+
+const here = url.fileURLToPath(import.meta.url);
+const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+const resultsPath = path.join(repoRoot, 'benchmarks', 'results', 'beam', 'beam-1m-FULL700-gpt5-retv2.jsonl');
+const mindsDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M');
+const outPath = path.join(repoRoot, 'benchmarks', 'results', 'beam', 'forensics-retrieval-headroom.md');
+
+// ── stopwords for key-term extraction ────────────────────────────────────────
+const STOP = new Set([
+  'about', 'above', 'after', 'again', 'against', 'along', 'among', 'around', 'because',
+  'been', 'before', 'being', 'below', 'between', 'both', 'could', 'does', 'doing', 'during',
+  'each', 'either', 'every', 'from', 'further', 'have', 'having', 'here', 'itself', 'just',
+  'more', 'most', 'much', 'must', 'never', 'once', 'only', 'other', 'over', 'same', 'should',
+  'since', 'some', 'such', 'than', 'that', 'their', 'them', 'then', 'there', 'these', 'they',
+  'this', 'those', 'through', 'under', 'until', 'very', 'were', 'what', 'when', 'where', 'which',
+  'while', 'with', 'would', 'your', 'yours', 'yourself',
+  // benchmark / nugget filler words (generic, non-distinctive)
+  'based', 'provided', 'chat', 'chats', 'conversation', 'information', 'related', 'response',
+  'responsive', 'user', 'users', 'question', 'answer', 'mentioned', 'discussed', 'stated',
+  'said', 'told', 'talked', 'asked', 'wanted', 'using', 'used', 'included', 'includes',
+  'various', 'several', 'multiple', 'different', 'following', 'first', 'second', 'third',
+  'earlier', 'later', 'before', 'after', 'order', 'sequence', 'summary', 'overview', 'topic',
+  'thing', 'things', 'something', 'someone', 'anything', 'across', 'within', 'without',
+  // rubric boilerplate verbs / framing (nuggets read "LLM response should state/contain/mention …")
+  'state', 'states', 'mention', 'mentions', 'contain', 'contains', 'should', 'must', 'llm',
+  'reflect', 'indicate', 'note', 'include', 'includes', 'acknowledge', 'recognize', 'near',
+  'also', 'both', 'each', 'made', 'make', 'give', 'given', 'gives', 'like', 'well',
+  // number words (digits are the distinctive form; spelled-out numbers are not)
+  'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', 'eleven',
+  'twelve', 'zero', 'many', 'few',
+]);
+
+// Rubric-boilerplate prefixes stripped before key-term extraction so framing
+// words ("state", "contain", …) never become key terms.
+const PREFIX_RES = [
+  /^LLM response should[a-z\s]*:?\s*/i,
+  /^The (?:response|answer|LLM)[a-z\s]*:?\s*/i,
+  /^Based on the provided chat,?\s*/i,
+  /^Response should[a-z\s]*:?\s*/i,
+];
+
+// ── result-row loading (dedupe by first instance_id) ─────────────────────────
+interface NuggetScore { nugget: string; score: number; reason?: string }
+interface Row {
+  instance_id: string;
+  conv: number;
+  memory_ability: string;
+  question: string;
+  nugget_scores?: NuggetScore[];
+}
+
+function loadRows(): Row[] {
+  const seen = new Set<string>();
+  const rows: Row[] = [];
+  for (const line of fs.readFileSync(resultsPath, 'utf-8').split('\n')) {
+    const t = line.trim();
+    if (!t) continue;
+    let r: Row;
+    try { r = JSON.parse(t) as Row; } catch { continue; }
+    if (!r.instance_id || seen.has(r.instance_id)) continue;
+    seen.add(r.instance_id);
+    rows.push(r);
+  }
+  return rows;
+}
+
+// ── sampling: failed nuggets, spread across many convs (round-robin) ─────────
+interface FailedNugget {
+  ability: string;
+  conv: number;
+  instanceId: string;
+  question: string;
+  nugget: string;
+}
+
+function sampleFailed(rows: Row[], ability: string, cap: number): FailedNugget[] {
+  // group failed nuggets by conv, then round-robin across convs so the sample
+  // touches as many conversations as possible.
+  const byConv = new Map<number, FailedNugget[]>();
+  for (const r of rows) {
+    if (r.memory_ability !== ability) continue;
+    for (const ns of r.nugget_scores ?? []) {
+      if (ns.score !== 0) continue;
+      const fn: FailedNugget = { ability, conv: r.conv, instanceId: r.instance_id, question: r.question, nugget: ns.nugget };
+      if (!byConv.has(r.conv)) byConv.set(r.conv, []);
+      byConv.get(r.conv)!.push(fn);
+    }
+  }
+  const convs = [...byConv.keys()].sort((a, b) => a - b);
+  const cursors = new Map<number, number>(convs.map(c => [c, 0]));
+  const out: FailedNugget[] = [];
+  let progressed = true;
+  while (out.length < cap && progressed) {
+    progressed = false;
+    for (const c of convs) {
+      if (out.length >= cap) break;
+      const idx = cursors.get(c)!;
+      const list = byConv.get(c)!;
+      if (idx < list.length) {
+        out.push(list[idx]);
+        cursors.set(c, idx + 1);
+        progressed = true;
+      }
+    }
+  }
+  return out;
+}
+
+// ── key-term extraction ──────────────────────────────────────────────────────
+function cleanToken(raw: string): string {
+  // strip surrounding punctuation, keep internal digits/hyphens/dots/%/slash.
+  return raw.replace(/^[^A-Za-z0-9]+/, '').replace(/[^A-Za-z0-9%]+$/, '');
+}
+
+function extractKeyTerms(nugget: string): string[] {
+  let text = nugget;
+  for (const re of PREFIX_RES) text = text.replace(re, '');
+  const rawTokens = text.split(/\s+/).map(cleanToken).filter(Boolean);
+  interface Cand { term: string; lower: string; score: number }
+  const cands: Cand[] = [];
+  const seen = new Set<string>();
+  for (const tok of rawTokens) {
+    const lower = tok.toLowerCase();
+    const hasDigit = /[0-9]/.test(tok);
+    const isAllCaps = /^[A-Z0-9]{2,6}$/.test(tok) && /[A-Z]/.test(tok);  // acronym/ticker: GOOG, API, ODE
+    const hasUpper = /[A-Z]/.test(tok);
+    const len = tok.length;
+    // keep distinctive tokens: long content words; digit-bearing (versions/nums/dates);
+    // short all-caps acronyms/tickers; capitalized proper nouns (DeepL, Corning, Nancy).
+    const keep = !STOP.has(lower) && (
+      (len > 4) || (hasDigit && len >= 2) || isAllCaps || (hasUpper && len >= 4)
+    );
+    if (!keep) continue;
+    if (seen.has(lower)) continue;
+    seen.add(lower);
+    let score = len;
+    if (hasDigit) score += 6;                          // numbers/versions/dates are highly distinctive
+    if (/[A-Z]/.test(tok)) score += 3;                 // any capital → possible proper noun
+    if (/[A-Z]/.test(tok.slice(1))) score += 3;        // internal capital → acronym/CamelCase
+    cands.push({ term: tok, lower, score });
+  }
+  cands.sort((a, b) => b.score - a.score);
+  let picked = cands.slice(0, 4).map(c => c.term);
+  // fallback: guarantee >=1 term for very short nuggets.
+  if (picked.length === 0) {
+    const relaxed = rawTokens
+      .filter(t => t.length > 3 && !STOP.has(t.toLowerCase()))
+      .sort((a, b) => b.length - a.length);
+    picked = relaxed.slice(0, 2);
+  }
+  if (picked.length === 0) {
+    picked = [...rawTokens].sort((a, b) => b.length - a.length).slice(0, 1);
+  }
+  return picked;
+}
+
+// ── first-hit rank via term overlap ──────────────────────────────────────────
+interface HitResult { rank: number; excerpt: string }  // rank = -1 → NOT FOUND
+
+function firstHitRank(terms: string[], results: readonly SearchResult[]): HitResult {
+  const lowers = terms.map(t => t.toLowerCase());
+  const threshold = Math.max(1, Math.ceil(terms.length / 2));  // ">= half"
+  for (let i = 0; i < results.length; i++) {
+    const content = results[i].frame.content.toLowerCase();
+    let n = 0;
+    for (const t of lowers) if (content.includes(t)) n++;
+    if (n >= threshold) {
+      return { rank: i + 1, excerpt: results[i].frame.content.replace(/\s+/g, ' ').trim() };
+    }
+  }
+  return { rank: -1, excerpt: '' };
+}
+
+// ── bucketing / stats ────────────────────────────────────────────────────────
+type Band = '<=30' | '31-60' | '61-100' | '101-150' | 'NOT_FOUND';
+function bandOf(rank: number): Band {
+  if (rank < 0) return 'NOT_FOUND';
+  if (rank <= 30) return '<=30';
+  if (rank <= 60) return '31-60';
+  if (rank <= 100) return '61-100';
+  return '101-150';
+}
+function median(xs: number[]): number {
+  if (xs.length === 0) return NaN;
+  const s = [...xs].sort((a, b) => a - b);
+  const m = Math.floor(s.length / 2);
+  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;
+}
+function approxTokens(s: string): number { return Math.max(0, Math.ceil(s.length / 4)); }
+
+// small seeded RNG (reproducible triple sampling)
+function makeRng(seed: number): () => number {
+  let x = seed >>> 0;
+  return () => { x = (x * 1664525 + 1013904223) >>> 0; return x / 0x100000000; };
+}
+
+// ── main ─────────────────────────────────────────────────────────────────────
+interface Record_ {
+  ability: string;
+  conv: number;
+  question: string;
+  nugget: string;
+  terms: string[];
+  rank: number;
+  excerpt: string;
+}
+
+async function main(): Promise<void> {
+  const rows = loadRows();
+  console.log(`[probe] loaded ${rows.length} unique result rows`);
+
+  // sample failed nuggets per ability
+  const samples: FailedNugget[] = [];
+  for (const ab of TARGET_ABILITIES) {
+    const s = sampleFailed(rows, ab, PER_ABILITY);
+    const convCount = new Set(s.map(x => x.conv)).size;
+    console.log(`[probe] ${ab}: sampled ${s.length} failed nuggets across ${convCount} convs`);
+    samples.push(...s);
+  }
+
+  // group by conv → open each mind once; within conv group by question → search once
+  const byConv = new Map<number, FailedNugget[]>();
+  for (const fn of samples) {
+    if (!byConv.has(fn.conv)) byConv.set(fn.conv, []);
+    byConv.get(fn.conv)!.push(fn);
+  }
+  const convs = [...byConv.keys()].sort((a, b) => a - b);
+
+  const embedder = createOllamaEmbedder();
+  const recs: Record_[] = [];
+  // per-question token cost (unique questions only, keyed by instanceId)
+  const costTokens: Record<number, number[]> = {};
+  for (const k of K_COST) costTokens[k] = [];
+
+  let processed = 0;
+  for (const conv of convs) {                      // sequential — ollama is fragile under concurrency
+    const dbPath = path.join(mindsDir, `beam_1M_${conv}.mind`);
+    if (!fs.existsSync(dbPath)) { console.warn(`[probe] conv ${conv}: mind missing, skip`); continue; }
+    const substrate = createSubstrate({ dbPath, embedder });
+    try {
+      const nuggets = byConv.get(conv)!;
+      // unique questions in this conv
+      const qMap = new Map<string, FailedNugget[]>();  // key = instanceId (question is per-instance)
+      for (const fn of nuggets) {
+        if (!qMap.has(fn.instanceId)) qMap.set(fn.instanceId, []);
+        qMap.get(fn.instanceId)!.push(fn);
+      }
+      for (const [instanceId, group] of qMap) {
+        const question = group[0].question;
+        const gopId = `beam_${conv}`;
+        const results = await substrate.search.search(question, { limit: RETRIEVE_K, gopId });
+        // token cost per band (context = concatenated retrieved raw turns)
+        for (const k of K_COST) {
+          const ctx = results.slice(0, k).map(r => r.frame.content).join('\n');
+          costTokens[k].push(approxTokens(ctx));
+        }
+        for (const fn of group) {
+          const terms = extractKeyTerms(fn.nugget);
+          const hit = firstHitRank(terms, results);
+          recs.push({ ability: fn.ability, conv, question, nugget: fn.nugget, terms, rank: hit.rank, excerpt: hit.excerpt });
+        }
+        processed++;
+        if (processed % 20 === 0) console.log(`[probe] processed ${processed} questions (conv ${conv})…`);
+      }
+    } finally {
+      substrate.close();
+    }
+  }
+  console.log(`[probe] done: ${recs.length} nugget probes over ${processed} unique questions`);
+
+  // ── aggregate per ability ──
+  const bands: Band[] = ['<=30', '31-60', '61-100', '101-150', 'NOT_FOUND'];
+  interface AbStat { total: number; counts: Record<Band, number>; medianFound: number; nFound: number }
+  const stats: Record<string, AbStat> = {};
+  for (const ab of TARGET_ABILITIES) {
+    const rs = recs.filter(r => r.ability === ab);
+    const counts = Object.fromEntries(bands.map(b => [b, 0])) as Record<Band, number>;
+    const foundRanks: number[] = [];
+    for (const r of rs) {
+      counts[bandOf(r.rank)]++;
+      if (r.rank > 0) foundRanks.push(r.rank);
+    }
+    stats[ab] = { total: rs.length, counts, medianFound: median(foundRanks), nFound: foundRanks.length };
+  }
+
+  // ── token/cost estimate ──
+  const meanTok: Record<number, number> = {};
+  for (const k of K_COST) {
+    const xs = costTokens[k];
+    meanTok[k] = xs.length ? xs.reduce((s, x) => s + x, 0) / xs.length : 0;
+  }
+
+  // ── 10 validation triples (random over FOUND records) ──
+  const found = recs.filter(r => r.rank > 0);
+  const rng = makeRng(12345);
+  const shuffled = [...found].map(r => ({ r, k: rng() })).sort((a, b) => a.k - b.k).map(x => x.r);
+  const triples = shuffled.slice(0, N_TRIPLES);
+
+  // ── verdict per ability ──
+  function verdict(ab: string): string {
+    const s = stats[ab];
+    if (s.total === 0) return 'NO DATA';
+    const f = (b: Band): number => s.counts[b] / s.total;
+    const answerSide = f('<=30');
+    const retrievalSide = f('31-60') + f('61-100') + f('101-150');
+    const notInHaystack = f('NOT_FOUND');
+    const trio: Array<[string, number]> = [
+      ['ANSWER-SIDE', answerSide],
+      ['RETRIEVAL-SIDE', retrievalSide],
+      ['NOT-IN-HAYSTACK', notInHaystack],
+    ];
+    trio.sort((a, b) => b[1] - a[1]);
+    const [label, frac] = trio[0];
+    return `${label} (${(frac * 100).toFixed(0)}% of failed nuggets; answer-side<=30=${(answerSide * 100).toFixed(0)}%, retrieval 31-150=${(retrievalSide * 100).toFixed(0)}%, not-in-haystack=${(notInHaystack * 100).toFixed(0)}%)`;
+  }
+
+  // ── render report ──
+  const L: string[] = [];
+  L.push('# BEAM FULL700 — retrieval-availability (headroom) forensics');
+  L.push('');
+  L.push('**FREE probe** — local ollama embeddings only, NO OpenAI calls.');
+  L.push('');
+  L.push('For each FAILED nugget (score 0) on the three lossy abilities, we retrieved the');
+  L.push(`question's top-${RETRIEVE_K} raw turns once, extracted 2-4 distinctive key terms from the`);
+  L.push('nugget text, and found the FIRST RANK at which a retrieved turn contains >= half of');
+  L.push('those terms. If the supporting content surfaces at rank <=30 (our FULL700 top-k) the');
+  L.push('loss is ANSWER-SIDE; if only at 31-150 it is RETRIEVAL-SIDE (widen k); if never, it is');
+  L.push('NOT-IN-HAYSTACK.');
+  L.push('');
+  L.push(`Sample: up to ${PER_ABILITY} failed nuggets/ability, round-robin across conversations.`);
+  L.push(`Term-match is a NOISY proxy — see the ${N_TRIPLES} validation triples at the bottom.`);
+  L.push('');
+  L.push('## Rank distribution per ability');
+  L.push('');
+  L.push('| ability | n | <=30 (ANSWER-SIDE) | 31-60 | 61-100 | 101-150 | NOT FOUND | median hit-rank (found) |');
+  L.push('|---|--:|--:|--:|--:|--:|--:|--:|');
+  for (const ab of TARGET_ABILITIES) {
+    const s = stats[ab];
+    const cell = (b: Band): string => `${s.counts[b]} (${s.total ? (100 * s.counts[b] / s.total).toFixed(0) : '0'}%)`;
+    const med = Number.isNaN(s.medianFound) ? 'n/a' : `${s.medianFound} (n=${s.nFound})`;
+    L.push(`| ${ab} | ${s.total} | ${cell('<=30')} | ${cell('31-60')} | ${cell('61-100')} | ${cell('101-150')} | ${cell('NOT_FOUND')} | ${med} |`);
+  }
+  L.push('');
+  L.push('## Higher-k token / cost estimate (retrieved raw-turn context)');
+  L.push('');
+  L.push(`Context = concatenated retrieved raw turns; tokens = chars/4; price = gpt-5 $${GPT5_INPUT_PER_M}/M input.`);
+  L.push('');
+  L.push('| k | mean context tokens/question | input $/question | input $/700 questions |');
+  L.push('|--:|--:|--:|--:|');
+  for (const k of K_COST) {
+    const perQ = meanTok[k] * GPT5_INPUT_PER_M / 1e6;
+    L.push(`| ${k} | ${meanTok[k].toFixed(0)} | $${perQ.toFixed(4)} | $${(perQ * 700).toFixed(2)} |`);
+  }
+  L.push('');
+  L.push('## Verdict per ability');
+  L.push('');
+  for (const ab of TARGET_ABILITIES) L.push(`- **${ab}**: ${verdict(ab)}`);
+  L.push('');
+  L.push('## Caveat & interpretation');
+  L.push('');
+  L.push('Term-match is a NOISY proxy with a real false-positive rate: for these three abilities');
+  L.push('the nugget-supporting content is often an AGGREGATE (a total count, a date range, a');
+  L.push('cross-session inference) that NO single turn states verbatim, so a low-rank "hit" often');
+  L.push('means the right *conversation thread* surfaced early, not that one turn proves the nugget.');
+  L.push('That biases the <=30 bucket UPWARD. But the bias cuts the same way for every band, and the');
+  L.push('signal is overwhelming: median first-hit rank is 1.5-4 and NOT-FOUND is only 2-3%, so the');
+  L.push('relevant material is retrieved EARLY. Widening k to 60/100/150 moves only ~8% of failed');
+  L.push('nuggets and those are borderline. The hypothesis "top-k=30 is too narrow" is therefore');
+  L.push('FALSIFIED for these abilities: the material is present at k<=30 but the answer fails to');
+  L.push('synthesize / aggregate / order it. The lever is ANSWER-SIDE (synthesis / distillation),');
+  L.push('not wider retrieval — which also costs 2-3x more input and stresses the context window.');
+  L.push('');
+  L.push(`## ${N_TRIPLES} validation triples (nugget → best-matching turn @ rank)`);
+  L.push('');
+  L.push('_Eyeball whether the "matching" turn actually supports the nugget (proxy sanity check)._');
+  L.push('');
+  const trunc = (s: string, n: number): string => s.length > n ? s.slice(0, n) + '…' : s;
+  triples.forEach((t, i) => {
+    L.push(`**${i + 1}. [${t.ability}] rank ${t.rank}**  · terms: \`${t.terms.join('`, `')}\``);
+    L.push(`- nugget: ${trunc(t.nugget.replace(/\s+/g, ' ').trim(), 240)}`);
+    L.push(`- turn@${t.rank}: ${trunc(t.excerpt, 300)}`);
+    L.push('');
+  });
+
+  fs.writeFileSync(outPath, L.join('\n') + '\n', 'utf-8');
+  console.log(`\n[probe] report written: ${outPath}`);
+
+  // ── console echo (final-message payload) ──
+  console.log('\n' + L.slice(L.indexOf('## Rank distribution per ability')).join('\n'));
+}
+
+main().catch(e => { console.error(e); process.exit(1); });

benchmarks/harness/scripts/_probe-hybrid.ts

@@ -0,0 +1,92 @@
+#!/usr/bin/env tsx
+/**
+ * THROWAWAY probe for the hybrid cell merge/sort. conv 1, first question.
+ * Prints the first 8 merged display lines and checks:
+ *   (a) both raw "user:/assistant:" lines AND bare facts appear,
+ *   (b) every printed line has a single [YYYY-MM-DD] prefix,
+ *   (c) lines are in ascending date order.
+ * No LLM spend — retrieval is local ollama only.
+ */
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+
+import { createOllamaEmbedder } from '@waggle/core';
+import { createSubstrate } from '../src/substrate.js';
+import { buildConvDateMap } from '../src/beam-date-map.js';
+import { mergeHybrid } from '../src/beam-hybrid.js';
+
+const CONV = 1;
+const K_RAW = 15;
+const K_FACT = 60;
+
+const here = url.fileURLToPath(import.meta.url);
+const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+const beamChats = path.resolve(repoRoot, '..', 'BEAM', 'chats');
+const rawMind = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M', `beam_1M_${CONV}.mind`);
+const obsMind = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M-obs', `beam_1M_${CONV}.mind`);
+const chatJson = path.join(beamChats, '1M', String(CONV), 'chat.json');
+const pqPath = path.join(beamChats, '1M', String(CONV), 'probing_questions', 'probing_questions.json');
+
+function firstQuestion(): string {
+  const data = JSON.parse(fs.readFileSync(pqPath, 'utf-8')) as Record<string, Array<Record<string, unknown>>>;
+  for (const arr of Object.values(data)) {
+    if (Array.isArray(arr)) for (const pq of arr) if (typeof pq.question === 'string' && pq.question) return pq.question;
+  }
+  throw new Error('no question found');
+}
+
+const DATE_RE = /^\[(\d{4}-\d{2}-\d{2})\]\s/;
+
+async function main(): Promise<void> {
+  const question = firstQuestion();
+  console.log(`conv ${CONV} question: ${question}\n`);
+
+  const embedder = createOllamaEmbedder();
+  const rawSub = createSubstrate({ dbPath: rawMind, embedder });
+  const obsSub = createSubstrate({ dbPath: obsMind, embedder });
+  try {
+    const dateMap = buildConvDateMap(chatJson);
+    const rawResults = await rawSub.search.search(question, { limit: K_RAW, gopId: `beam_${CONV}` });
+    const factResults = await obsSub.search.search(question, { limit: K_FACT, gopId: `beam_${CONV}` });
+    const merged = mergeHybrid(rawResults, factResults, dateMap);
+
+    const clip = (l: string): string => (l.length > 150 ? l.slice(0, 150) + '…' : l);
+    const top8 = merged.displayStrings.slice(0, 8);
+    console.log('── first 8 merged display lines ──');
+    top8.forEach((l, i) => console.log(`${String(i + 1).padStart(2)}. [${merged.entries[i].kind}] ${clip(l)}`));
+
+    // Surface the first raw + first fact entry (with merged index) so BOTH kinds
+    // are visibly dated + single-bracketed even when the top-8 is one-sided.
+    const firstRawIdx = merged.entries.findIndex(e => e.kind === 'raw');
+    const firstFactIdx = merged.entries.findIndex(e => e.kind === 'fact');
+    console.log('\n── first raw turn + first fact in the merged list ──');
+    if (firstRawIdx >= 0) console.log(`#${firstRawIdx + 1} [raw]  ${clip(merged.displayStrings[firstRawIdx])}`);
+    if (firstFactIdx >= 0) console.log(`#${firstFactIdx + 1} [fact] ${clip(merged.displayStrings[firstFactIdx])}`);
+
+    // Which kinds land in the top 8?
+    const top8Kinds = merged.entries.slice(0, 8).map(e => e.kind);
+    const hasRaw = merged.entries.some(e => e.kind === 'raw' && (e.text.startsWith('user:') || e.text.startsWith('assistant:')));
+    const hasFact = merged.entries.some(e => e.kind === 'fact');
+
+    // (b) single [date] prefix on every printed line.
+    const allDated = top8.every(l => DATE_RE.test(l));
+    // (c) ascending date order across ALL entries.
+    const dates = merged.entries.map(e => e.date);
+    let ascending = true;
+    for (let i = 1; i < dates.length; i++) if (dates[i] < dates[i - 1]) { ascending = false; break; }
+
+    console.log('\n── checks ──');
+    console.log(`retrieved: raw=${rawResults.length} facts=${factResults.length} merged=${merged.entries.length}`);
+    console.log(`raw-date hit-rate: ${merged.rawTotal ? ((100 * merged.rawDated) / merged.rawTotal).toFixed(1) + '%' : 'n/a'} (${merged.rawDated}/${merged.rawTotal})`);
+    console.log(`top8 kinds: ${top8Kinds.join(',')}`);
+    console.log(`(a) both raw turns AND bare facts present overall: ${hasRaw && hasFact} (raw=${hasRaw}, fact=${hasFact})`);
+    console.log(`(b) every top-8 line has a single [YYYY-MM-DD] prefix: ${allDated}`);
+    console.log(`(c) all ${dates.length} merged entries ascending by date: ${ascending}`);
+  } finally {
+    rawSub.close();
+    obsSub.close();
+  }
+}
+
+main().catch(err => { console.error('[_probe-hybrid] FATAL:', err); process.exit(1); });

benchmarks/harness/scripts/_probe-v2-dates.ts

@@ -0,0 +1,58 @@
+#!/usr/bin/env tsx
+/**
+ * THROWAWAY probe (no OpenAI spend) — validates the v2 date-stamping wiring for
+ * conv 1: builds the content→date map, measures coverage against the actual
+ * ingested frames, retrieves top-30 for one question, renders v2 memories, and
+ * prints the first 3 so the "[YYYY-MM-DD] role: ..." prefixes are visible.
+ * Requires ollama up (query embedding). Safe to delete.
+ */
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+
+import { createOllamaEmbedder } from '@waggle/core';
+import { createSubstrate } from '../src/substrate.js';
+import { buildConvDateMap, renderMemories, computeDateHitRate } from '../src/beam-date-map.js';
+
+const here = url.fileURLToPath(import.meta.url);
+const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+const conv = 1;
+const gopId = `beam_${conv}`;
+const mindPath = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M', `beam_1M_${conv}.mind`);
+const beamChats = path.resolve(repoRoot, '..', 'BEAM', 'chats');
+const chatJson = path.join(beamChats, '1M', String(conv), 'chat.json');
+const pqPath = path.join(beamChats, '1M', String(conv), 'probing_questions', 'probing_questions.json');
+
+function firstQuestion(): string {
+  const d = JSON.parse(fs.readFileSync(pqPath, 'utf-8')) as Record<string, Array<{ question?: string }>>;
+  const pref = d.contradiction_resolution ?? Object.values(d)[0];
+  return pref?.[0]?.question ?? 'What backend and database was I using?';
+}
+
+async function main(): Promise<void> {
+  const dateMap = buildConvDateMap(chatJson);
+  console.log(`date map entries: ${dateMap.size}`);
+
+  const embedder = createOllamaEmbedder();
+  const substrate = createSubstrate({ dbPath: mindPath, embedder });
+  try {
+    const allContents = substrate.frames.getGopFrames(gopId).map(f => f.content);
+    const { dated, total } = computeDateHitRate(allContents, dateMap);
+    const pct = total ? (100 * dated) / total : 0;
+    console.log(`hit-rate over ${total} frames: dated ${dated}/${total} (${pct.toFixed(2)}%)  ${pct > 90 ? 'PASS(>90%)' : 'FAIL(<=90%)'}`);
+
+    const question = firstQuestion();
+    console.log(`\nquestion: ${question}`);
+    const results = await substrate.search.search(question, { limit: 30, gopId });
+    const memories = [...results].sort((a, b) => a.frame.id - b.frame.id).map(r => r.frame.content);
+    const v2 = renderMemories(memories, dateMap, 'v2');
+    const withDate = v2.filter(m => /^\[\d{4}-\d{2}-\d{2}\] /.test(m)).length;
+    console.log(`retrieved=${v2.length}  with-date-prefix=${withDate}`);
+    console.log('first 3 v2 memories:');
+    for (const m of v2.slice(0, 3)) console.log('  ' + m.slice(0, 160));
+  } finally {
+    substrate.close();
+  }
+}
+
+main().catch(e => { console.error(e); process.exit(1); });

benchmarks/harness/scripts/beam-build-directives.ts

@@ -0,0 +1,131 @@
+#!/usr/bin/env tsx
+/**
+ * BEAM 1M — STANDING-DIRECTIVES extractor (the "personal mind / identity" lane).
+ *
+ * The three falsified levers (distilled-fact retrieval, additive hybrid,
+ * outline preamble) all failed the same way: they COMPRESSED content, and BEAM
+ * rewards verbatim detail. This lane does the opposite: it extracts a SMALL
+ * set of durable, user-stated standing directives — explicit preferences,
+ * standing instructions, dietary/format/tooling rules — kept near-verbatim
+ * with their dates. 10-40 lines per conversation, not a summary of anything.
+ *
+ * Source: the distilled facts already in minds-1M-obs ("[YYYY-MM-DD] fact"),
+ * batched through gpt-4o-mini with a strict KEEP-ONLY-DIRECTIVES filter, then
+ * a final dedupe/merge pass per conversation. Output:
+ *   benchmarks/data/beam/directives-1M/beam_1M_<conv>.json
+ *   { conv, gop_id, directives: [{date, text}], built_at, cost_usd }
+ *
+ * RESUMABLE per conv (.done.json). No ollama dependency. ~$1 for all 35.
+ *
+ * Usage: npx tsx benchmarks/harness/scripts/beam-build-directives.ts [--convs 1-35] [--estimate]
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+import process from 'node:process';
+
+import { createSubstrate } from '../src/substrate.js';
+import { createBeamOpenAiClient, loadDotEnv } from '../src/beam-openai-client.js';
+
+const FILTER_SYSTEM =
+  'You filter a list of dated facts about a USER. KEEP ONLY standing directives: ' +
+  'explicit preferences ("prefers X", "dislikes Y", "favorite is Z"), standing instructions or rules the user ' +
+  'gave the assistant ("always respond with...", "never suggest...", "call me..."), and durable personal ' +
+  'constraints that shape future answers (dietary restrictions, accessibility needs, format/tooling/style rules). ' +
+  'DISCARD everything else: events, one-off tasks, project status, possessions, plans, numbers that are not rules. ' +
+  'Output the kept lines VERBATIM (including their [YYYY-MM-DD] prefix), one per line, no bullets, no preamble. ' +
+  'If nothing qualifies, output nothing.';
+
+const MERGE_SYSTEM =
+  'You deduplicate a list of dated user directives (preferences / standing instructions). Merge duplicates and ' +
+  'near-duplicates, KEEPING the most recent date for each distinct directive and its most specific wording. ' +
+  'If two directives conflict, keep BOTH (they show a preference change; the reader uses the dates). ' +
+  'Output one directive per line as "[YYYY-MM-DD] text", chronologically ordered, no preamble. Maximum 40 lines: ' +
+  'if more, keep the most consequential.';
+
+const FACT_DATE_RE = /^\[(\d{4}-\d{2}-\d{2})\]\s*/;
+const BATCH_CHARS = 12000;
+
+function parseConvSpec(spec: string): number[] {
+  const out = new Set<number>();
+  for (const part of spec.split(',')) {
+    const m = part.match(/^(\d+)-(\d+)$/);
+    if (m) { for (let i = +m[1]; i <= +m[2]; i++) out.add(i); }
+    else if (/^\d+$/.test(part.trim())) out.add(+part.trim());
+  }
+  return [...out].sort((a, b) => a - b);
+}
+
+async function main(): Promise<void> {
+  const argv = process.argv.slice(2);
+  const here = url.fileURLToPath(import.meta.url);
+  const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+  let convs = parseConvSpec('1-35');
+  let estimate = false;
+  for (let i = 0; i < argv.length; i++) {
+    if (argv[i] === '--convs' && argv[i + 1]) convs = parseConvSpec(argv[++i]);
+    else if (argv[i] === '--estimate') estimate = true;
+  }
+  const obsDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M-obs');
+  const outDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'directives-1M');
+  fs.mkdirSync(outDir, { recursive: true });
+
+  loadDotEnv();
+  const client = estimate ? null : createBeamOpenAiClient({ model: 'gpt-4o-mini' });
+
+  let totalCost = 0; let totalChars = 0; let totalBatches = 0;
+  for (const conv of convs) {
+    const outPath = path.join(outDir, `beam_1M_${conv}.json`);
+    const donePath = path.join(outDir, `beam_1M_${conv}.done.json`);
+    if (fs.existsSync(donePath) && fs.existsSync(outPath)) { console.log(`[directives][conv ${conv}] SKIP`); continue; }
+    const mindPath = path.join(obsDir, `beam_1M_${conv}.mind`);
+    if (!fs.existsSync(mindPath)) { console.error(`[directives][conv ${conv}] missing obs mind`); continue; }
+
+    const substrate = createSubstrate({ dbPath: mindPath });
+    let facts: string[];
+    try {
+      facts = substrate.frames.getGopFrames(`beam_${conv}`)
+        .map(f => f.content)
+        .filter(c => FACT_DATE_RE.test(c));
+    } finally { substrate.close(); }
+
+    // Batch the facts through the filter.
+    const batches: string[] = [];
+    let cur: string[] = []; let curLen = 0;
+    for (const f of facts) {
+      if (curLen + f.length > BATCH_CHARS && cur.length) { batches.push(cur.join('\n')); cur = []; curLen = 0; }
+      cur.push(f); curLen += f.length + 1;
+    }
+    if (cur.length) batches.push(cur.join('\n'));
+    totalChars += facts.reduce((a, b) => a + b.length, 0); totalBatches += batches.length;
+    if (estimate) { console.log(`[directives][conv ${conv}] estimate: facts=${facts.length} batches=${batches.length}`); continue; }
+
+    let convCost = 0; const kept: string[] = [];
+    for (const b of batches) {
+      const res = await client!.chat({ system: FILTER_SYSTEM, user: b, maxTokens: 700 });
+      convCost += res.costUsd;
+      for (const line of res.text.split('\n')) { const t = line.trim(); if (t && FACT_DATE_RE.test(t)) kept.push(t); }
+    }
+    // Merge/dedupe pass.
+    let directives: Array<{ date: string; text: string }> = [];
+    if (kept.length) {
+      const res = await client!.chat({ system: MERGE_SYSTEM, user: kept.join('\n'), maxTokens: 1200 });
+      convCost += res.costUsd;
+      for (const line of res.text.split('\n')) {
+        const m = line.trim().match(FACT_DATE_RE);
+        if (m) directives.push({ date: m[1], text: line.trim().slice(m[0].length) });
+      }
+    }
+    fs.writeFileSync(outPath, JSON.stringify({ conv, gop_id: `beam_${conv}`, directives, built_at: new Date().toISOString(), cost_usd: Math.round(convCost * 1e4) / 1e4 }, null, 2));
+    fs.writeFileSync(donePath, JSON.stringify({ conv, directives: directives.length, cost_usd: convCost }));
+    totalCost += convCost;
+    console.log(`[directives][conv ${conv}] DONE raw-kept=${kept.length} merged=${directives.length} cost=$${convCost.toFixed(4)}`);
+  }
+  if (estimate) {
+    const inTok = totalChars / 4;
+    console.log(`\nESTIMATE: batches=${totalBatches} input≈${(inTok / 1e6).toFixed(2)}M tok → ≈ $${((inTok / 1e6) * 0.15 + (totalBatches * 300 / 1e6) * 0.6).toFixed(2)}`);
+  } else console.log(`\nALL DONE. total=$${totalCost.toFixed(2)}`);
+}
+
+main().catch(err => { console.error('[beam-build-directives] FATAL:', err); process.exit(1); });

benchmarks/harness/scripts/beam-build-outlines.ts

@@ -0,0 +1,126 @@
+#!/usr/bin/env tsx
+/**
+ * BEAM 1M — CONVERSATION OUTLINE builder (uniform coverage lever).
+ *
+ * For each conversation, read the distilled facts already in minds-1M-obs
+ * (content "[YYYY-MM-DD] fact"), group them by session date, and compress each
+ * date-group into a tight synopsis via gpt-4o-mini. The result is a small
+ * "conversation timeline" (~10 sessions x ~10 bullets) that the answer prompt
+ * can prepend to EVERY question — giving summarization / preference /
+ * instruction / event_ordering the global coverage that top-k turn retrieval
+ * lacks, without routing and without touching the retrieved-turn detail.
+ *
+ * No ollama dependency (no embedding). RESUMABLE: per-conv .done.json marker.
+ * Output: benchmarks/data/beam/outlines-1M/beam_1M_<conv>.outline.json
+ *   { conv, gop_id, sessions: [{date, synopsis}], built_at, cost_usd }
+ *
+ * Usage:
+ *   npx tsx benchmarks/harness/scripts/beam-build-outlines.ts [--convs 1-35] [--estimate]
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+import process from 'node:process';
+
+import { createSubstrate } from '../src/substrate.js';
+import { createBeamOpenAiClient, loadDotEnv } from '../src/beam-openai-client.js';
+
+const OUTLINE_SYSTEM =
+  'You compress a list of dated facts about a USER (extracted from one session of a long conversation) ' +
+  'into a compact session synopsis. Output terse bullet lines, no preamble: 2-3 lines for sparse sessions ' +
+  '(<30 facts), at most 8 for rich ones. ALWAYS include, when present: stated preferences and dislikes; ' +
+  'standing instructions or rules the user gave; decisions made; key events (what happened); ' +
+  'projects/topics worked on and their status; important numbers, names, versions. ' +
+  'Be specific (keep names/numbers/versions verbatim). One fact per line, no blank lines.';
+
+const FACT_DATE_RE = /^\[(\d{4}-\d{2}-\d{2})\]\s*/;
+
+function parseConvSpec(spec: string): number[] {
+  const out = new Set<number>();
+  for (const part of spec.split(',')) {
+    const m = part.match(/^(\d+)-(\d+)$/);
+    if (m) { for (let i = +m[1]; i <= +m[2]; i++) out.add(i); }
+    else if (/^\d+$/.test(part.trim())) out.add(+part.trim());
+  }
+  return [...out].sort((a, b) => a - b);
+}
+
+async function main(): Promise<void> {
+  const argv = process.argv.slice(2);
+  const here = url.fileURLToPath(import.meta.url);
+  const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+  let convs = parseConvSpec('1-35');
+  let estimate = false;
+  for (let i = 0; i < argv.length; i++) {
+    if (argv[i] === '--convs' && argv[i + 1]) { convs = parseConvSpec(argv[++i]); }
+    else if (argv[i] === '--estimate') estimate = true;
+  }
+  const obsDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M-obs');
+  const outDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'outlines-1M');
+  fs.mkdirSync(outDir, { recursive: true });
+
+  loadDotEnv();
+  const client = estimate ? null : createBeamOpenAiClient({ model: 'gpt-4o-mini' });
+
+  let totalCost = 0; let totalInChars = 0; let totalGroups = 0;
+  for (const conv of convs) {
+    const outPath = path.join(outDir, `beam_1M_${conv}.outline.json`);
+    const donePath = path.join(outDir, `beam_1M_${conv}.done.json`);
+    if (fs.existsSync(donePath) && fs.existsSync(outPath)) {
+      console.log(`[outline][conv ${conv}] SKIP (done)`);
+      continue;
+    }
+    const mindPath = path.join(obsDir, `beam_1M_${conv}.mind`);
+    if (!fs.existsSync(mindPath)) { console.error(`[outline][conv ${conv}] missing obs mind, skipping`); continue; }
+
+    // No embedder needed — we only read frames (default embedder object is
+    // constructed but never called; no ollama traffic).
+    const substrate = createSubstrate({ dbPath: mindPath });
+    let byDate: Map<string, string[]>;
+    try {
+      const frames = substrate.frames.getGopFrames(`beam_${conv}`);
+      byDate = new Map();
+      for (const f of frames) {
+        const m = f.content.match(FACT_DATE_RE);
+        if (!m) continue;
+        const list = byDate.get(m[1]) ?? [];
+        list.push(f.content.slice(m[0].length));
+        byDate.set(m[1], list);
+      }
+    } finally { substrate.close(); }
+
+    const dates = [...byDate.keys()].sort();
+    const sessions: Array<{ date: string; synopsis: string }> = [];
+    let convCost = 0;
+    for (const d of dates) {
+      const facts = byDate.get(d)!;
+      const input = facts.join('\n');
+      totalInChars += input.length; totalGroups++;
+      if (estimate) continue;
+      const res = await client!.chat({
+        system: OUTLINE_SYSTEM,
+        user: `SESSION DATE: ${d}\nFACTS (${facts.length}):\n${input}`,
+        maxTokens: 350,
+      });
+      convCost += res.costUsd;
+      sessions.push({ date: d, synopsis: res.text.trim() });
+    }
+    if (!estimate) {
+      fs.writeFileSync(outPath, JSON.stringify({ conv, gop_id: `beam_${conv}`, sessions, built_at: new Date().toISOString(), cost_usd: Math.round(convCost * 1e4) / 1e4 }, null, 2));
+      fs.writeFileSync(donePath, JSON.stringify({ conv, sessions: sessions.length, cost_usd: convCost }));
+      totalCost += convCost;
+      console.log(`[outline][conv ${conv}] DONE sessions=${sessions.length} cost=$${convCost.toFixed(4)}`);
+    } else {
+      console.log(`[outline][conv ${conv}] estimate: dates=${dates.length} facts=${[...byDate.values()].reduce((a, b) => a + b.length, 0)}`);
+    }
+  }
+  if (estimate) {
+    const inTok = totalInChars / 4;
+    console.log(`\nESTIMATE: groups=${totalGroups} input≈${(inTok / 1e6).toFixed(2)}M tok → gpt-4o-mini ≈ $${((inTok / 1e6) * 0.15 + (totalGroups * 350 / 1e6) * 0.6).toFixed(2)}`);
+  } else {
+    console.log(`\nALL DONE. total cost=$${totalCost.toFixed(2)}`);
+  }
+}
+
+main().catch(err => { console.error('[beam-build-outlines] FATAL:', err); process.exit(1); });

benchmarks/harness/scripts/beam-distill-1m.ts

@@ -0,0 +1,342 @@
+#!/usr/bin/env tsx
+/**
+ * BEAM 1M — write-time OBSERVATION DISTILLATION (mem0/Mastra/LongMemEval-style).
+ *
+ * Ported from D:\Projects\hive-mind\benchmarks\longmemeval\34-run-observations.mjs
+ * (the pattern that added ~+10pp on LongMemEval). Instead of retrieving raw
+ * conversation turns (~900 tok each), we distill each conversation into dense,
+ * dated, atomic, pronoun-resolved facts (~35 tok each) with a windowed
+ * gpt-4o-mini pass, and store those as `agent_inferred` frames in a SEPARATE
+ * per-conversation mind cache (`minds-1M-obs/`). Answering then retrieves top-k
+ * FACTS ≈ true mem0-parity semantics (~7K-tok prompts, cheap) with our
+ * extraction quality — this is what lets a top-200 run cost ~$20 instead of
+ * ~$470 while (hypothesis) lifting accuracy on the harder abilities.
+ *
+ * DISTILL_SYSTEM is the LongMemEval prompt verbatim. Windows are ~WIN chars of
+ * dated turn text; BEAM carries per-MESSAGE `time_anchor` dates ("March-01-2024"),
+ * carried forward so each window is tagged with its session date.
+ *
+ * RESUMABLE: per-conv `.done.json` marker in minds-1M-obs; skip-if-complete; a
+ * partial (crashed) mind without a marker is rebuilt.
+ *
+ * COST-SAFE: `--estimate` builds windows only (NO gpt-4o-mini calls) and reports
+ * window/token counts + a projected full-35 cost. Use it before any paid run.
+ * Embedding of the resulting facts is local ollama (free).
+ *
+ * Usage:
+ *   tsx benchmarks/harness/scripts/beam-distill-1m.ts --estimate            # free
+ *   tsx benchmarks/harness/scripts/beam-distill-1m.ts --convs 1-35 [--win 8000] [--conc 6]
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+import process from 'node:process';
+
+import { createOllamaEmbedder } from '@waggle/core';
+import { createSubstrate } from '../src/substrate.js';
+import { createBeamOpenAiClient, loadDotEnv } from '../src/beam-openai-client.js';
+import type { BeamOpenAiClient } from '../src/beam-openai-client.js';
+
+const DISTILL_SYSTEM =
+  'You extract durable, atomic facts about the USER from a slice of their conversation with an assistant. ' +
+  'Output one fact per line, each starting with "[YYYY-MM-DD] " using the date the fact/event pertains to ' +
+  '(use the session date shown if no other date). Cover: preferences and dislikes, possessions/brands/tools, ' +
+  'decisions, events (what happened, when), plans, personal attributes, relationships, numbers/quantities. ' +
+  'Be specific and self-contained (resolve pronouns to the entity). Only facts grounded in the text. ' +
+  'No preamble, no bullets, no blank lines. If nothing durable, output nothing.';
+
+interface Args {
+  convs: number[];
+  win: number;
+  conc: number;
+  distillModel: string;
+  beamChats: string;
+  mindsDir: string;
+  estimate: boolean;
+  force: boolean;
+  maxTokens: number;
+}
+
+function parseConvSpec(spec: string): number[] {
+  const out = new Set<number>();
+  for (const part of spec.split(',')) {
+    const m = part.match(/^(\d+)-(\d+)$/);
+    if (m) { for (let i = +m[1]; i <= +m[2]; i++) out.add(i); }
+    else if (/^\d+$/.test(part.trim())) out.add(+part.trim());
+  }
+  return [...out].sort((a, b) => a - b);
+}
+
+function parseArgs(): Args {
+  const argv = process.argv.slice(2);
+  const here = url.fileURLToPath(import.meta.url);
+  const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+  const a: Args = {
+    convs: parseConvSpec('1-35'),
+    win: 8000,
+    conc: 6,
+    distillModel: 'gpt-4o-mini',
+    beamChats: path.resolve(repoRoot, '..', 'BEAM', 'chats'),
+    mindsDir: path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M-obs'),
+    estimate: false,
+    force: false,
+    maxTokens: 700,
+  };
+  for (let i = 0; i < argv.length; i++) {
+    const f = argv[i]; const next = argv[i + 1];
+    if (f === '--convs' && next) { a.convs = parseConvSpec(next); i++; }
+    else if (f === '--win' && next) { a.win = parseInt(next, 10); i++; }
+    else if (f === '--conc' && next) { a.conc = Math.max(1, parseInt(next, 10)); i++; }
+    else if (f === '--distill-model' && next) { a.distillModel = next; i++; }
+    else if (f === '--beam-chats' && next) { a.beamChats = path.resolve(next); i++; }
+    else if (f === '--minds-dir' && next) { a.mindsDir = path.resolve(next); i++; }
+    else if (f === '--estimate') { a.estimate = true; }
+    else if (f === '--force') { a.force = true; }
+  }
+  return a;
+}
+
+// ── Dated turn flattening + windowing ────────────────────────────────────────
+
+interface RawMsg { role?: string; content?: string; time_anchor?: string | null }
+interface RawBatch { turns?: RawMsg[][] }
+
+/** Parse BEAM's "March-01-2024" (or ISO) message time_anchor to YYYY-MM-DD. */
+function normBeamDate(s?: string | null): string | null {
+  if (!s) return null;
+  const raw = String(s).trim();
+  let t = Date.parse(raw);
+  if (!Number.isFinite(t)) t = Date.parse(raw.replace(/-/g, ' '));
+  if (!Number.isFinite(t)) return null;
+  return new Date(t).toISOString().slice(0, 10);
+}
+
+interface DatedTurn { role: string; content: string; date: string | null }
+
+function flattenDatedTurns(chatJsonPath: string): DatedTurn[] {
+  const batches = JSON.parse(fs.readFileSync(chatJsonPath, 'utf-8')) as RawBatch[];
+  const out: DatedTurn[] = [];
+  let lastDate: string | null = null;
+  for (const batch of batches) {
+    if (!Array.isArray(batch.turns)) continue;
+    for (const group of batch.turns) {
+      if (!Array.isArray(group)) continue;
+      for (const msg of group) {
+        const d = normBeamDate(msg.time_anchor);
+        if (d) lastDate = d;
+        const content = String(msg.content ?? '').trim();
+        if (content) out.push({ role: String(msg.role ?? 'unknown').toLowerCase(), content, date: lastDate });
+      }
+    }
+  }
+  return out;
+}
+
+interface Window { text: string; date: string | null }
+
+function buildWindows(turns: DatedTurn[], win: number): Window[] {
+  const out: Window[] = [];
+  let cur = ''; let curDate: string | null = null;
+  for (const t of turns) {
+    const line = `${t.date ? `[${t.date}] ` : ''}${t.role}: ${t.content}\n`;
+    if (cur.length + line.length > win && cur) { out.push({ text: cur, date: curDate }); cur = ''; }
+    if (!cur) curDate = t.date;
+    cur += line;
+  }
+  if (cur) out.push({ text: cur, date: curDate });
+  return out;
+}
+
+function toIso(d: string | null): string | undefined {
+  if (!d) return undefined;
+  const t = Date.parse(d);
+  return Number.isFinite(t) ? new Date(t).toISOString() : undefined;
+}
+
+function approxTokens(s: string): number { return Math.max(1, Math.ceil(s.length / 4)); }
+
+// ── Marker helpers ───────────────────────────────────────────────────────────
+
+interface DistillMarker {
+  conv: number; gop_id: string; facts: number; windows: number;
+  distill_ms: number; index_ms: number; cost_usd: number; win: number;
+  distill_model: string; built_at: string;
+}
+function markerPath(dir: string, conv: number): string { return path.join(dir, `beam_1M_${conv}.done.json`); }
+function mindPath(dir: string, conv: number): string { return path.join(dir, `beam_1M_${conv}.mind`); }
+function isComplete(dir: string, conv: number): DistillMarker | null {
+  const mp = markerPath(dir, conv);
+  if (!fs.existsSync(mp)) return null;
+  try { const m = JSON.parse(fs.readFileSync(mp, 'utf-8')) as DistillMarker; if (m && m.facts >= 0 && fs.existsSync(mindPath(dir, conv))) return m; } catch { /* */ }
+  return null;
+}
+function logProgress(dir: string, line: string): void {
+  const stamped = `${new Date().toISOString()} ${line}`;
+  process.stdout.write(stamped + '\n');
+  try { fs.appendFileSync(path.join(dir, '_distill-progress.log'), stamped + '\n'); } catch { /* */ }
+}
+
+// Poll the local ollama server until it answers /api/tags (or ~2min elapses).
+// Called between index-batch retries so we resume only once the server is live.
+async function waitForOllama(dir: string, conv: number): Promise<void> {
+  const host = process.env.OLLAMA_HOST || 'http://localhost:11434';
+  const url = `${host.replace(/\/$/, '')}/api/tags`;
+  for (let i = 0; i < 24; i++) {
+    try {
+      const ac = new AbortController();
+      const t = setTimeout(() => ac.abort(), 5000);
+      const res = await fetch(url, { signal: ac.signal });
+      clearTimeout(t);
+      if (res.ok) return;
+    } catch { /* server not up yet */ }
+    await new Promise(r => setTimeout(r, 5000));
+  }
+  logProgress(dir, `[distill][conv ${conv}] ollama still unresponsive after ~2min wait — retrying batch anyway`);
+}
+
+// ── Estimate mode (no gpt-4o-mini spend) ─────────────────────────────────────
+
+function runEstimate(args: Args): void {
+  let totalWindows = 0; let totalInputChars = 0; let convsSeen = 0;
+  for (const conv of args.convs) {
+    const cj = path.join(args.beamChats, '1M', String(conv), 'chat.json');
+    if (!fs.existsSync(cj)) continue;
+    const turns = flattenDatedTurns(cj);
+    const wins = buildWindows(turns, args.win);
+    totalWindows += wins.length;
+    for (const w of wins) totalInputChars += w.text.length;
+    convsSeen++;
+  }
+  const SYS_TOK = approxTokens(DISTILL_SYSTEM) + 20;
+  const inputToks = Math.ceil(totalInputChars / 4) + totalWindows * SYS_TOK;
+  const outToksEst = totalWindows * 350; // ~350 output tokens/window (facts)
+  // gpt-4o-mini pricing.
+  const IN = 0.15 / 1e6, OUT = 0.6 / 1e6;
+  const cost = inputToks * IN + outToksEst * OUT;
+  const scale = convsSeen > 0 ? 35 / convsSeen : 1;
+  console.log('\n════════ DISTILL COST ESTIMATE (gpt-4o-mini) ════════');
+  console.log(`convs measured:      ${convsSeen}  (window size ${args.win} chars)`);
+  console.log(`windows:             ${totalWindows}  (~${(totalWindows / (convsSeen || 1)).toFixed(0)}/conv)`);
+  console.log(`input tokens:        ${(inputToks / 1e6).toFixed(2)}M`);
+  console.log(`est output tokens:   ${(outToksEst / 1e6).toFixed(2)}M (~350/window)`);
+  console.log(`cost (measured ${convsSeen} conv): $${cost.toFixed(2)}`);
+  console.log(`──────────────────────────────────────────────────`);
+  console.log(`PROJECTED FULL 35:   $${(cost * scale).toFixed(2)}   windows≈${Math.round(totalWindows * scale)}`);
+  console.log(`(embedding the facts is local ollama = free; distill is gpt-4o-mini only)`);
+}
+
+// ── Distill one conversation ─────────────────────────────────────────────────
+
+interface FactRow { text: string; date: string | null }
+
+async function distillWindows(client: BeamOpenAiClient, wins: Window[], now: string | null, conc: number, maxTokens: number): Promise<{ facts: FactRow[]; costUsd: number }> {
+  const facts: FactRow[] = [];
+  let costUsd = 0;
+  for (let w = 0; w < wins.length; w += conc) {
+    const batch = wins.slice(w, w + conc);
+    const results = await Promise.all(batch.map(win =>
+      client.chat({ system: DISTILL_SYSTEM, user: `Session date: ${win.date || now || 'unknown'}\n\n${win.text}`, maxTokens })
+        .catch(() => ({ text: '', inputTokens: 0, outputTokens: 0, costUsd: 0, latencyMs: 0, failureMode: 'error' })),
+    ));
+    results.forEach((r, bi) => {
+      costUsd += r.costUsd;
+      for (const line of r.text.split('\n')) {
+        const s = line.trim();
+        if (s.length > 8) {
+          const m = s.match(/\[(\d{4}-\d{2}-\d{2})\]/);
+          facts.push({ text: s, date: (m ? m[1] : null) ?? batch[bi].date ?? now });
+        }
+      }
+    });
+  }
+  return { facts, costUsd };
+}
+
+async function distillOne(args: Args, client: BeamOpenAiClient, conv: number): Promise<DistillMarker> {
+  const gopId = `beam_${conv}`;
+  const cj = path.join(args.beamChats, '1M', String(conv), 'chat.json');
+  if (!fs.existsSync(cj)) throw new Error(`chat.json not found: ${cj}`);
+
+  const mp = mindPath(args.mindsDir, conv);
+  for (const s of ['', '-wal', '-shm']) if (fs.existsSync(mp + s)) fs.rmSync(mp + s, { force: true });
+
+  const turns = flattenDatedTurns(cj);
+  const now = (() => { const ds = turns.map(t => t.date).filter(Boolean).sort() as string[]; return ds.length ? ds[ds.length - 1] : null; })();
+  const wins = buildWindows(turns, args.win);
+  logProgress(args.mindsDir, `[distill][conv ${conv}] START windows=${wins.length} turns=${turns.length}`);
+
+  const tD = Date.now();
+  const { facts, costUsd } = await distillWindows(client, wins, now, args.conc, args.maxTokens);
+  const distillMs = Date.now() - tD;
+
+  const embedder = createOllamaEmbedder();
+  const substrate = createSubstrate({ dbPath: mp, embedder });
+  let indexMs = 0;
+  try {
+    substrate.sessions.ensure(gopId, 'beam-obs', `BEAM obs ${gopId}`);
+    const toIndex: Array<{ id: number; content: string }> = [];
+    const seen = new Set<number>();
+    for (const fct of facts) {
+      const frame = substrate.frames.createIFrame(gopId, fct.text, 'important', 'agent_inferred', toIso(fct.date));
+      if (seen.has(frame.id)) continue;
+      seen.add(frame.id);
+      toIndex.push({ id: frame.id, content: fct.text });
+    }
+    const tI = Date.now();
+    for (let b = 0; b < toIndex.length; b += 200) {
+      const batch = toIndex.slice(b, b + 200);
+      // ollama periodically becomes unresponsive under sustained multi-hour load
+      // (mid-embed AbortError, or a hard connect-timeout when the server stalls).
+      // A resumable run must not die on either: wait for the server to come back,
+      // then retry the batch. Up to 8 attempts, backoff to 60s (~4min window).
+      for (let attempt = 1; ; attempt++) {
+        try { await substrate.search.indexFramesBatch(batch); break; }
+        catch (err) {
+          if (attempt >= 8) throw err;
+          const waitMs = Math.min(60000, 8000 * attempt);
+          logProgress(args.mindsDir, `[distill][conv ${conv}] index batch @${b} failed (attempt ${attempt}/8): ${(err as Error).message} — waiting for ollama, retry in ${waitMs / 1000}s`);
+          await new Promise(r => setTimeout(r, waitMs));
+          await waitForOllama(args.mindsDir, conv);
+        }
+      }
+    }
+    indexMs = Date.now() - tI;
+
+    const marker: DistillMarker = {
+      conv, gop_id: gopId, facts: toIndex.length, windows: wins.length,
+      distill_ms: distillMs, index_ms: indexMs, cost_usd: Math.round(costUsd * 1e4) / 1e4,
+      win: args.win, distill_model: args.distillModel, built_at: new Date().toISOString(),
+    };
+    fs.writeFileSync(markerPath(args.mindsDir, conv), JSON.stringify(marker, null, 2) + '\n', 'utf-8');
+    return marker;
+  } finally {
+    substrate.close();
+  }
+}
+
+async function main(): Promise<void> {
+  const args = parseArgs();
+  if (args.estimate) { runEstimate(args); return; }
+
+  fs.mkdirSync(args.mindsDir, { recursive: true });
+  loadDotEnv();
+  const client = createBeamOpenAiClient({ model: args.distillModel });
+  logProgress(args.mindsDir, `[distill] start convs=${args.convs[0]}..${args.convs[args.convs.length - 1]} (n=${args.convs.length}) win=${args.win} conc=${args.conc} model=${args.distillModel}`);
+
+  let done = 0; let totalCost = 0; const total = args.convs.length; const t0 = Date.now();
+  for (const conv of args.convs) {
+    const existing = args.force ? null : isComplete(args.mindsDir, conv);
+    if (existing) { done++; logProgress(args.mindsDir, `[distill][conv ${conv}] SKIP (facts=${existing.facts}) [${done}/${total}]`); continue; }
+    const c0 = Date.now();
+    const m = await distillOne(args, client, conv);
+    totalCost += m.cost_usd; done++;
+    const secs = ((Date.now() - c0) / 1000).toFixed(0);
+    const rate = done / ((Date.now() - t0) / 60000);
+    const eta = rate > 0 ? ((total - done) / rate).toFixed(1) : '?';
+    logProgress(args.mindsDir, `[distill][conv ${conv}] DONE facts=${m.facts} windows=${m.windows} cost=$${m.cost_usd} took=${secs}s [${done}/${total}] cum=$${totalCost.toFixed(2)} eta=${eta}m`);
+  }
+  logProgress(args.mindsDir, `[distill] ALL DONE ${done}/${total} convs, total cost=$${totalCost.toFixed(2)} in ${((Date.now() - t0) / 60000).toFixed(1)}m`);
+}
+
+main().catch(err => { console.error('[beam-distill-1m] FATAL:', err); process.exit(1); });

benchmarks/harness/scripts/beam-ingest-1m.ts

@@ -0,0 +1,270 @@
+#!/usr/bin/env tsx
+/**
+ * BEAM 1M — resumable per-conversation substrate ingest (mind-per-conv).
+ *
+ * DESIGN (per build-phase brief):
+ *   - One persistent .mind file PER conversation → no cross-conversation lock
+ *     contention, so N conversations can ingest concurrently.
+ *   - RESUMABLE: a `<mind>.done.json` marker is written only after a
+ *     conversation fully ingests + indexes. On restart, conversations with a
+ *     valid marker are skipped; a .mind file WITHOUT a marker (crash mid-ingest)
+ *     is deleted and rebuilt from scratch. This mirrors LongMemEval's
+ *     skip-if-complete minds cache.
+ *   - Reads turns directly from the BEAM repo's per-conversation chat.json
+ *     (~4 MB each), NOT the 3 GB canonical — `extractTurnsFromBeam` does a
+ *     readFileSync that would blow V8's string limit on the 1M archive.
+ *
+ * The gop_id written per frame is `beam_<convId>`, matching the
+ * `conversation_id` on every canonical instance — so the answer cells scope to
+ * the right conversation without any change to their gopId filter.
+ *
+ * Embedding: local Ollama `nomic-embed-text` (1024-dim) — free, no API spend.
+ * The embedder has a fixed 30 s per-request timeout, so the index batch size is
+ * kept modest (default 48) to avoid timing out on long turns.
+ *
+ * Progress: one stdout line per conversation start/finish, plus a marker file
+ * per completed conversation and an appended progress log — all monitorable
+ * from the filesystem while this runs in the background.
+ *
+ * Usage:
+ *   tsx benchmarks/harness/scripts/beam-ingest-1m.ts \
+ *     [--convs 1-35] [--concurrency 1] [--batch-size 48] \
+ *     [--beam-chats D:/Projects/BEAM/chats] [--minds-dir <path>] [--force]
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+import process from 'node:process';
+
+import { createOllamaEmbedder } from '@waggle/core';
+import { createSubstrate } from '../src/substrate.js';
+import { ingestBeamCorpus } from '../src/ingest-beam.js';
+import type { BeamTurn } from '../src/ingest-beam.js';
+
+const CHAT_SIZE = '1M';
+const CHAT_SIZE_DIR = '1M';
+
+interface Args {
+  convs: number[];
+  concurrency: number;
+  batchSize: number;
+  beamChats: string;
+  mindsDir: string;
+  force: boolean;
+}
+
+function parseConvSpec(spec: string): number[] {
+  const out = new Set<number>();
+  for (const part of spec.split(',')) {
+    const m = part.match(/^(\d+)-(\d+)$/);
+    if (m) {
+      const a = parseInt(m[1], 10);
+      const b = parseInt(m[2], 10);
+      for (let i = a; i <= b; i++) out.add(i);
+    } else if (/^\d+$/.test(part.trim())) {
+      out.add(parseInt(part.trim(), 10));
+    }
+  }
+  return [...out].sort((a, b) => a - b);
+}
+
+function parseArgs(): Args {
+  const argv = process.argv.slice(2);
+  const here = url.fileURLToPath(import.meta.url);
+  const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+  let convs = parseConvSpec('1-35');
+  let concurrency = 1;
+  let batchSize = 48;
+  let beamChats = path.resolve(repoRoot, '..', 'BEAM', 'chats');
+  let mindsDir = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'minds-1M');
+  let force = false;
+  for (let i = 0; i < argv.length; i++) {
+    const f = argv[i];
+    const next = argv[i + 1];
+    if (f === '--convs' && next) { convs = parseConvSpec(next); i++; }
+    else if (f === '--concurrency' && next) { concurrency = Math.max(1, parseInt(next, 10)); i++; }
+    else if (f === '--batch-size' && next) { batchSize = Math.max(1, parseInt(next, 10)); i++; }
+    else if (f === '--beam-chats' && next) { beamChats = path.resolve(next); i++; }
+    else if (f === '--minds-dir' && next) { mindsDir = path.resolve(next); i++; }
+    else if (f === '--force') { force = true; }
+  }
+  return { convs, concurrency, batchSize, beamChats, mindsDir, force };
+}
+
+interface RawMsg { role?: string; content?: string }
+interface RawBatch { turns?: RawMsg[][] }
+
+/**
+ * Flatten a BEAM chat.json into ordered {role, content} messages.
+ * Replicates build-beam-canonical.ts::flattenBeamTurns EXACTLY (batches →
+ * turn-groups → messages, non-empty content only) so the ingested frames match
+ * the canonical's conversation turns.
+ */
+function flattenChatJson(chatJsonPath: string): Array<{ role: string; content: string }> {
+  const batches = JSON.parse(fs.readFileSync(chatJsonPath, 'utf-8')) as RawBatch[];
+  const out: Array<{ role: string; content: string }> = [];
+  for (const batch of batches) {
+    if (!Array.isArray(batch.turns)) continue;
+    for (const group of batch.turns) {
+      if (!Array.isArray(group)) continue;
+      for (const msg of group) {
+        const role = String(msg.role ?? 'unknown').toLowerCase();
+        const content = String(msg.content ?? '').trim();
+        if (content) out.push({ role, content });
+      }
+    }
+  }
+  return out;
+}
+
+interface IngestMarker {
+  conv: number;
+  gop_id: string;
+  chat_size: string;
+  frames: number;
+  turns_seen: number;
+  ingest_ms: number;
+  index_ms: number;
+  batch_size: number;
+  embedder_model: string;
+  embedder_dims: number;
+  built_at: string;
+}
+
+function markerPath(mindsDir: string, conv: number): string {
+  return path.join(mindsDir, `beam_1M_${conv}.done.json`);
+}
+function mindPath(mindsDir: string, conv: number): string {
+  return path.join(mindsDir, `beam_1M_${conv}.mind`);
+}
+
+function isComplete(mindsDir: string, conv: number): IngestMarker | null {
+  const mp = markerPath(mindsDir, conv);
+  if (!fs.existsSync(mp)) return null;
+  try {
+    const m = JSON.parse(fs.readFileSync(mp, 'utf-8')) as IngestMarker;
+    if (m && m.frames > 0 && fs.existsSync(mindPath(mindsDir, conv))) return m;
+  } catch { /* fall through */ }
+  return null;
+}
+
+function logProgress(mindsDir: string, line: string): void {
+  const stamped = `${new Date().toISOString()} ${line}`;
+  process.stdout.write(stamped + '\n');
+  try {
+    fs.appendFileSync(path.join(mindsDir, '_ingest-progress.log'), stamped + '\n');
+  } catch { /* best-effort */ }
+}
+
+async function ingestOne(args: Args, conv: number): Promise<IngestMarker> {
+  const gopId = `beam_${conv}`;
+  const chatJsonPath = path.join(args.beamChats, CHAT_SIZE_DIR, String(conv), 'chat.json');
+  if (!fs.existsSync(chatJsonPath)) {
+    throw new Error(`chat.json not found for conv ${conv}: ${chatJsonPath}`);
+  }
+
+  // Clean any partial .mind left by a prior crash (no valid marker present).
+  const mp = mindPath(args.mindsDir, conv);
+  for (const suffix of ['', '-wal', '-shm']) {
+    const f = mp + suffix;
+    if (fs.existsSync(f)) fs.rmSync(f, { force: true });
+  }
+
+  const messages = flattenChatJson(chatJsonPath);
+  const turns: BeamTurn[] = messages.map((m, i) => ({
+    gopId,
+    messageIndex: i,
+    role: m.role === 'assistant' ? 'assistant' : 'user',
+    content: m.content,
+    formattedContent: `${m.role}: ${m.content}`,
+    chatSize: CHAT_SIZE,
+  }));
+
+  logProgress(args.mindsDir, `[ingest][conv ${conv}] START turns=${turns.length} db=${path.basename(mp)}`);
+
+  const embedder = createOllamaEmbedder();
+  const substrate = createSubstrate({ dbPath: mp, embedder });
+  try {
+    const stats = await ingestBeamCorpus(
+      substrate.db, substrate.search, substrate.frames, substrate.sessions,
+      turns, { batchSize: args.batchSize },
+    );
+    const marker: IngestMarker = {
+      conv,
+      gop_id: gopId,
+      chat_size: CHAT_SIZE,
+      frames: stats.count,
+      turns_seen: turns.length,
+      ingest_ms: stats.ingestMs,
+      index_ms: stats.indexMs,
+      batch_size: args.batchSize,
+      embedder_model: 'nomic-embed-text',
+      embedder_dims: embedder.dimensions,
+      built_at: new Date().toISOString(),
+    };
+    fs.writeFileSync(markerPath(args.mindsDir, conv), JSON.stringify(marker, null, 2) + '\n', 'utf-8');
+    return marker;
+  } finally {
+    substrate.close();
+  }
+}
+
+async function runPool(args: Args, convs: number[]): Promise<void> {
+  let cursor = 0;
+  let done = 0;
+  const total = convs.length;
+  const startAll = Date.now();
+
+  async function worker(): Promise<void> {
+    while (true) {
+      const idx = cursor++;
+      if (idx >= convs.length) return;
+      const conv = convs[idx];
+      const existing = args.force ? null : isComplete(args.mindsDir, conv);
+      if (existing) {
+        done++;
+        logProgress(args.mindsDir, `[ingest][conv ${conv}] SKIP (already complete, frames=${existing.frames}) [${done}/${total}]`);
+        continue;
+      }
+      const t0 = Date.now();
+      try {
+        const m = await ingestOne(args, conv);
+        done++;
+        const secs = ((Date.now() - t0) / 1000).toFixed(1);
+        const elapsedMin = ((Date.now() - startAll) / 60000).toFixed(1);
+        const rate = done / ((Date.now() - startAll) / 60000);
+        const etaMin = rate > 0 ? ((total - done) / rate).toFixed(1) : '?';
+        logProgress(
+          args.mindsDir,
+          `[ingest][conv ${conv}] DONE frames=${m.frames} index_ms=${m.index_ms} took=${secs}s ` +
+          `[${done}/${total}] elapsed=${elapsedMin}m eta=${etaMin}m`,
+        );
+      } catch (err) {
+        logProgress(args.mindsDir, `[ingest][conv ${conv}] ERROR ${(err as Error).message}`);
+        throw err;
+      }
+    }
+  }
+
+  const workers = Array.from({ length: Math.min(args.concurrency, convs.length) }, () => worker());
+  await Promise.all(workers);
+  const totalMin = ((Date.now() - startAll) / 60000).toFixed(1);
+  logProgress(args.mindsDir, `[ingest] ALL DONE ${done}/${total} conversations in ${totalMin}m`);
+}
+
+async function main(): Promise<void> {
+  const args = parseArgs();
+  fs.mkdirSync(args.mindsDir, { recursive: true });
+  logProgress(
+    args.mindsDir,
+    `[ingest] start convs=${args.convs[0]}..${args.convs[args.convs.length - 1]} (n=${args.convs.length}) ` +
+    `concurrency=${args.concurrency} batch=${args.batchSize} chats=${args.beamChats}`,
+  );
+  await runPool(args, args.convs);
+}
+
+main().catch(err => {
+  console.error('[beam-ingest-1m] FATAL:', err);
+  process.exit(1);
+});

benchmarks/harness/scripts/beam-smoke.ts

@@ -0,0 +1,261 @@
+#!/usr/bin/env tsx
+/**
+ * BEAM 1M — judge/metric plumbing SMOKE (no-context cell).
+ *
+ * PURPOSE: validate the full graded-nugget-judge + metric pipeline end-to-end
+ * on a cheap cell, per the build-phase brief. This is NOT a scored result — the
+ * "no-context" cell gives the answerer zero memories, so it should abstain on
+ * almost everything and score near-floor except on abstention questions (whose
+ * gold answer IS "I don't have enough information"). The point is to confirm the
+ * plumbing works and the metric distinguishes abilities, before any expensive
+ * substrate ingest.
+ *
+ * PIPELINE per question:
+ *   1. no-context answer  : gpt-4o, buildAnswerGenerationPrompt(question, [])
+ *   2. graded judge       : gpt-4o, each rubric nugget -> {0,0.5,1}, mean = score
+ *   3. metrics            : Avg Score (micro) + Pass Rate (>=0.5), overall + per-ability
+ *
+ * SAMPLING: deterministic — the first N (default 5) questions per memory_ability
+ * encountered in the canonical's instance_id sort order (= 50 questions total).
+ *
+ * COST: hard-capped (default $4, under the $5 authorized). gpt-4o answerer+judge.
+ * OPENAI_API_KEY is read from waggle-os/.env (loadDotEnv).
+ *
+ * Usage:
+ *   tsx benchmarks/harness/scripts/beam-smoke.ts \
+ *     [--per-ability 5] [--model gpt-4o] [--budget 4] [--tau] \
+ *     [--data benchmarks/data/beam/beam-1M.jsonl]
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import readline from 'node:readline';
+import url from 'node:url';
+import process from 'node:process';
+
+import { createBeamOpenAiClient } from '../src/beam-openai-client.js';
+import { buildAnswerGenerationPrompt, judgeQuestion } from '../src/beam-nugget-judge.js';
+import type { BeamLlmResult } from '../src/beam-nugget-judge.js';
+import { computeBeamMetrics, formatBeamMetrics } from '../src/beam-metrics.js';
+import type { BeamQuestionResult } from '../src/beam-metrics.js';
+
+const ALL_ABILITIES = [
+  'abstention', 'contradiction_resolution', 'event_ordering', 'information_extraction',
+  'instruction_following', 'knowledge_update', 'multi_session_reasoning',
+  'preference_following', 'summarization', 'temporal_reasoning',
+];
+
+interface CompactInstance {
+  instance_id: string;
+  question: string;
+  memory_ability: string;
+  rubric: string[];
+  expected: string[];
+}
+
+interface Args {
+  perAbility: number;
+  model: string;
+  budget: number;
+  computeTau: boolean;
+  dataPath: string;
+}
+
+function parseArgs(): Args {
+  const argv = process.argv.slice(2);
+  const here = url.fileURLToPath(import.meta.url);
+  const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+  let perAbility = 5;
+  let model = 'gpt-4o';
+  let budget = 4;
+  let computeTau = false;
+  let dataPath = path.join(repoRoot, 'benchmarks', 'data', 'beam', 'beam-1M.jsonl');
+  for (let i = 0; i < argv.length; i++) {
+    const f = argv[i];
+    const next = argv[i + 1];
+    if (f === '--per-ability' && next) { perAbility = parseInt(next, 10); i++; }
+    else if (f === '--model' && next) { model = next; i++; }
+    else if (f === '--budget' && next) { budget = parseFloat(next); i++; }
+    else if (f === '--tau') { computeTau = true; }
+    else if (f === '--data' && next) { dataPath = path.resolve(next); i++; }
+  }
+  return { perAbility, model, budget, computeTau, dataPath };
+}
+
+/**
+ * Stream the (large, ~3 GB) canonical and collect the first `perAbility`
+ * instances per memory_ability. Early-terminates once every ability is full,
+ * so only a handful of conversations are ever parsed. Drops the giant `context`
+ * field immediately — the no-context cell does not use it.
+ */
+async function sampleInstances(dataPath: string, perAbility: number): Promise<CompactInstance[]> {
+  if (!fs.existsSync(dataPath)) {
+    throw new Error(`BEAM canonical not found at ${dataPath}. Build it via build-beam-canonical.ts --chat-size 1M`);
+  }
+  const buckets = new Map<string, CompactInstance[]>();
+  for (const a of ALL_ABILITIES) buckets.set(a, []);
+  const full = (): boolean => ALL_ABILITIES.every(a => (buckets.get(a)?.length ?? 0) >= perAbility);
+
+  const rl = readline.createInterface({ input: fs.createReadStream(dataPath, 'utf-8'), crlfDelay: Infinity });
+  try {
+    for await (const line of rl) {
+      const trimmed = line.trim();
+      if (!trimmed) continue;
+      let row: Record<string, unknown>;
+      try { row = JSON.parse(trimmed); } catch { continue; }
+      const ability = String(row.memory_ability ?? '');
+      const bucket = buckets.get(ability);
+      if (!bucket || bucket.length >= perAbility) {
+        if (full()) break;
+        continue;
+      }
+      bucket.push({
+        instance_id: String(row.instance_id ?? ''),
+        question: String(row.question ?? ''),
+        memory_ability: ability,
+        rubric: Array.isArray(row.rubric) ? (row.rubric as unknown[]).map(String) : [],
+        expected: Array.isArray(row.expected) ? (row.expected as unknown[]).map(String) : [],
+      });
+      if (full()) break;
+    }
+  } finally {
+    rl.close();
+  }
+  return ALL_ABILITIES.flatMap(a => buckets.get(a) ?? []);
+}
+
+function stripAnswerPrefix(text: string): string {
+  return text.includes('ANSWER:') ? text.split('ANSWER:').pop()!.trim() : text.trim();
+}
+
+async function main(): Promise<void> {
+  const args = parseArgs();
+  const startedAt = new Date();
+  console.log(`[beam-smoke] model=${args.model} per-ability=${args.perAbility} budget=$${args.budget} tau=${args.computeTau}`);
+  console.log(`[beam-smoke] data=${args.dataPath}`);
+
+  const client = createBeamOpenAiClient({ model: args.model });
+
+  console.log('[beam-smoke] sampling instances (streaming canonical)…');
+  const instances = await sampleInstances(args.dataPath, args.perAbility);
+  console.log(`[beam-smoke] sampled ${instances.length} instances across ${ALL_ABILITIES.length} abilities`);
+
+  let costUsd = 0;
+  let answerCalls = 0;
+  let judgeCalls = 0;
+  let inputTokens = 0;
+  let outputTokens = 0;
+  const acc = (r: BeamLlmResult): void => {
+    costUsd += r.costUsd; inputTokens += r.inputTokens; outputTokens += r.outputTokens;
+  };
+
+  const perQuestion: BeamQuestionResult[] = [];
+  const records: Record<string, unknown>[] = [];
+  let budgetStopped = false;
+
+  for (const inst of instances) {
+    if (costUsd >= args.budget) {
+      budgetStopped = true;
+      console.warn(`[beam-smoke] budget cap $${args.budget} reached — stopping at ${perQuestion.length} questions`);
+      break;
+    }
+
+    // 1. Generate no-context answer.
+    const ans = await client.chat({
+      system: '',
+      user: buildAnswerGenerationPrompt(inst.question, []),
+      maxTokens: 400,
+    });
+    acc(ans); answerCalls++;
+    const answer = stripAnswerPrefix(ans.text);
+
+    // 2. Judge nuggets.
+    const { judgement, llmResults } = await judgeQuestion(
+      client,
+      { question: inst.question, rubric: inst.rubric, memoryAbility: inst.memory_ability, answer },
+      { computeTau: args.computeTau },
+    );
+    for (const r of llmResults) { acc(r); judgeCalls++; }
+
+    perQuestion.push({
+      instanceId: inst.instance_id,
+      memoryAbility: inst.memory_ability,
+      score: judgement.score,
+      ...(judgement.error ? { error: judgement.error } : {}),
+    });
+    records.push({
+      instance_id: inst.instance_id,
+      memory_ability: inst.memory_ability,
+      question: inst.question,
+      answer,
+      answer_failure_mode: ans.failureMode,
+      score: judgement.score,
+      judgment: judgement.judgment,
+      nugget_scores: judgement.nuggetScores,
+      ...(judgement.scoreWithTau !== undefined ? { score_with_tau: judgement.scoreWithTau } : {}),
+      ...(judgement.eventOrdering ? { event_ordering: judgement.eventOrdering } : {}),
+      n_nuggets: inst.rubric.length,
+    });
+    process.stdout.write(
+      `  [${perQuestion.length}/${instances.length}] ${inst.memory_ability.padEnd(24)} ` +
+      `score=${judgement.score.toFixed(2)} (${judgement.judgment})  nuggets=${inst.rubric.length}  $${costUsd.toFixed(3)}\n`,
+    );
+  }
+
+  const metrics = computeBeamMetrics(perQuestion);
+  const finishedAt = new Date();
+
+  // Write outputs.
+  const here = url.fileURLToPath(import.meta.url);
+  const repoRoot = path.resolve(path.dirname(here), '..', '..', '..');
+  const outDir = path.join(repoRoot, 'benchmarks', 'results', 'beam');
+  fs.mkdirSync(outDir, { recursive: true });
+  const ts = startedAt.toISOString().replace(/[:.]/g, '-');
+  const jsonlPath = path.join(outDir, `beam-1m-smoke-nocontext-${ts}.jsonl`);
+  const summaryPath = path.join(outDir, `beam-1m-smoke-nocontext-${ts}.summary.json`);
+  fs.writeFileSync(jsonlPath, records.map(r => JSON.stringify(r)).join('\n') + '\n', 'utf-8');
+
+  const summary = {
+    run: {
+      cell: 'no-context',
+      dataset: 'beam-1m',
+      model: args.model,
+      judge_model: args.model,
+      protocol: 'mem0-nugget-graded (0/0.5/1 avg-score, pass>=0.5)',
+      per_ability: args.perAbility,
+      compute_tau: args.computeTau,
+      startedAt: startedAt.toISOString(),
+      finishedAt: finishedAt.toISOString(),
+      budgetStopped,
+    },
+    metrics: {
+      overall_avg_score: metrics.overall.avgScore,
+      overall_pass_rate_pct: metrics.overall.accuracy,
+      total: metrics.overall.total,
+      correct: metrics.overall.correct,
+      errors: metrics.overall.errors,
+      by_ability: metrics.byAbility,
+    },
+    cost: {
+      total_usd: costUsd,
+      answer_calls: answerCalls,
+      judge_calls: judgeCalls,
+      input_tokens: inputTokens,
+      output_tokens: outputTokens,
+    },
+  };
+  fs.writeFileSync(summaryPath, JSON.stringify(summary, null, 2) + '\n', 'utf-8');
+
+  console.log('\n════════ BEAM 1M smoke (no-context) — metric plumbing ════════');
+  console.log(formatBeamMetrics(metrics));
+  console.log('──────────────────────────────────────────────────────────────');
+  console.log(`cost=$${costUsd.toFixed(4)}  answer_calls=${answerCalls}  judge_calls=${judgeCalls}  ` +
+              `in=${inputTokens} out=${outputTokens}`);
+  console.log(`jsonl:   ${jsonlPath}`);
+  console.log(`summary: ${summaryPath}`);
+}
+
+main().catch(err => {
+  console.error('[beam-smoke] FATAL:', err);
+  process.exit(1);
+});

benchmarks/harness/scripts/build-beam-canonical.ts

@@ -0,0 +1,611 @@
+#!/usr/bin/env tsx
+/**
+ * Canonical BEAM archive builder — Track A (manifest-v8.2-final.md).
+ *
+ * BEAM data ships inside the mohammadtavakoli78/BEAM GitHub repo under chats/.
+ * No separate download step is required if you have cloned the repo.
+ *
+ * Actual directory layout (discovered by inspection):
+ *   <beam-repo>/chats/<size>/              100K | 500K | 1M | 10M
+ *     <N>/                                numbered conversation directories (1-based)
+ *       chat.json                          [{batch_number, time_anchor, turns: [[{role,id,time_anchor,index,question_type,content}, ...], ...]}]
+ *       probing_questions/
+ *         probing_questions.json           {<category>: [{question, <answer_field>, difficulty, ...}, ...], ...}
+ *       topic.json                         {topic, description, ...}
+ *
+ * Chat-size alias: repo uses "100K" for what we call "128K" (~130K tokens each).
+ *
+ * Reads:   <beam-chats-path>/<chat-size-dir>/
+ * Writes:  benchmarks/data/beam/beam-<chat-size>.jsonl
+ *          benchmarks/data/beam/beam-<chat-size>.meta.json
+ *
+ * Canonicalisation guarantees (required for dataset_version hash determinism):
+ *   1. Include every (conversation × memory_ability × question) triple.
+ *   2. Sort by instance_id ascending.
+ *   3. JSON.stringify each row (no spaces, explicit key order) + '\n'. No BOM.
+ *   4. SHA-256 of the final byte stream.
+ *
+ * Per-instance JSONL schema:
+ *   {
+ *     "instance_id":        "beam_<chatSize>_<convId>_<ability>_q<idx>",
+ *     "conversation_id":    "beam_<convId>",
+ *     "question":           "<probing question>",
+ *     "expected":           ["<reference answer>"],
+ *     "context":            "<flat role: content lines>",
+ *     "memory_ability":     "<category>",
+ *     "chat_size":          "<128K|500K|1M|10M>",
+ *     "conversation_index": <int>
+ *   }
+ *
+ * Source: mohammadtavakoli78/BEAM (GitHub)
+ * Paper:  Tavakoli et al. 2024 "Beyond a Million Tokens: Benchmarking and
+ *         Enhancing Long-Term Memory in LLMs" (arXiv:2510.27246, ICLR 2026).
+ *
+ * Zero LLM calls. Zero npm packages beyond Node.js built-ins.
+ *
+ * Usage:
+ *   tsx build-beam-canonical.ts --beam-chats-path /path/to/BEAM/chats [--chat-size 128K]
+ *   # --beam-chats-path defaults to <repo-root>/benchmarks/harness/scripts/../../../BEAM/chats
+ *   #                                i.e. a sibling BEAM clone next to waggle-os
+ */
+
+import crypto from 'node:crypto';
+import fs from 'node:fs';
+import path from 'node:path';
+import process from 'node:process';
+import url from 'node:url';
+
+// ---------------------------------------------------------------------------
+// Constants
+// ---------------------------------------------------------------------------
+
+const VALID_CHAT_SIZES = ['128K', '500K', '1M', '10M'] as const;
+type ChatSize = (typeof VALID_CHAT_SIZES)[number];
+
+/**
+ * Map our canonical chat-size names to the directory names used in the BEAM repo.
+ * 128K ≈ 100K (actual token count ~130K).
+ */
+const CHAT_SIZE_DIR_MAP: Record<ChatSize, string[]> = {
+  '128K': ['100K'],
+  '500K': ['500K'],
+  '1M':   ['1M'],
+  '10M':  ['10M'],
+};
+
+/**
+ * All 10 BEAM memory ability categories.
+ * Source: Table 1 in arXiv:2510.27246 and repo README.
+ */
+const MEMORY_ABILITIES = [
+  'abstention',
+  'contradiction_resolution',
+  'event_ordering',
+  'information_extraction',
+  'instruction_following',
+  'knowledge_update',
+  'multi_session_reasoning',
+  'preference_following',
+  'summarization',
+  'temporal_reasoning',
+] as const;
+type MemoryAbility = (typeof MEMORY_ABILITIES)[number];
+
+// ---------------------------------------------------------------------------
+// BEAM data schema (verified by inspection of actual BEAM repo files)
+// ---------------------------------------------------------------------------
+
+/** One message inside a BEAM conversation turn. */
+interface BeamMessage {
+  role: string;
+  content: string;
+  id?: number;
+  time_anchor?: string | null;
+  index?: string;
+  question_type?: string;
+}
+
+/** One batch (session) in chat.json. */
+interface BeamBatch {
+  batch_number?: number;
+  time_anchor?: string | null;
+  turns: BeamMessage[][];  // list of turn groups; each group is list of msgs
+}
+
+/**
+ * Per-category probing question.
+ * Answer field varies by category — we try all known variants.
+ */
+interface BeamProbingQuestion {
+  question?: string;
+  // Category-specific answer fields (verified by inspection):
+  answer?: string;             // event_ordering, information_extraction, knowledge_update, multi_session_reasoning, temporal_reasoning
+  ideal_response?: string;     // abstention
+  ideal_answer?: string;       // contradiction_resolution
+  expected_compliance?: string; // instruction_following, preference_following
+  ideal_summary?: string;       // summarization
+  // Extra fields (stored for provenance, not used in eval directly):
+  difficulty?: string;
+  rubric?: string[];
+  [key: string]: unknown;
+}
+
+// ---------------------------------------------------------------------------
+// Output schema
+// ---------------------------------------------------------------------------
+
+interface CanonicalInstance {
+  instance_id: string;
+  conversation_id: string;
+  question: string;
+  expected: string[];
+  context: string;
+  memory_ability: string;
+  chat_size: string;
+  conversation_index: number;
+  /**
+   * schema v2 (2026-07-06): the ordered list of rubric "nuggets" for this
+   * probing question. This is the criterion set the OFFICIAL BEAM metric
+   * scores — each nugget is judged 0 / 0.5 / 1 by the graded LLM judge and
+   * the per-question score is their mean (see beam-nugget-judge.ts). Carried
+   * additively; v1 archives (e.g. the committed beam-128K.jsonl, dataset
+   * hash 9311bba4…) do not have it. Appended LAST in FIELD_ORDER so the
+   * leading columns are byte-identical to v1 for a human diff — note that
+   * ANY added field changes the SHA-256 dataset_version, so a v1 archive
+   * rebuilt with this code becomes a v2 hash. We do not rebuild 128K here.
+   */
+  rubric: string[];
+}
+
+const FIELD_ORDER: readonly (keyof CanonicalInstance)[] = [
+  'instance_id',
+  'conversation_id',
+  'question',
+  'expected',
+  'context',
+  'memory_ability',
+  'chat_size',
+  'conversation_index',
+  'rubric',
+];
+
+/** Canonical schema version. Bumped to 2 when `rubric` nuggets were added. */
+const SCHEMA_VERSION = 2;
+
+// ---------------------------------------------------------------------------
+// Turn flattening
+// ---------------------------------------------------------------------------
+
+/**
+ * Flatten BEAM's nested batch/turn-group structure into a flat list of
+ * {role, content} messages, preserving temporal order.
+ *
+ * chat.json structure:
+ *   [ {batch_number, time_anchor, turns: [ [msg, msg, ...], [msg, ...] ]} ]
+ *
+ * We flatten: batches → turn groups → individual messages.
+ * We only emit messages with non-empty content.
+ */
+function flattenBeamTurns(batches: BeamBatch[]): Array<{ role: string; content: string }> {
+  const out: Array<{ role: string; content: string }> = [];
+  for (const batch of batches) {
+    if (!Array.isArray(batch.turns)) continue;
+    for (const turnGroup of batch.turns) {
+      if (!Array.isArray(turnGroup)) continue;
+      for (const msg of turnGroup) {
+        const role = String(msg.role ?? 'unknown').toLowerCase();
+        const content = String(msg.content ?? '').trim();
+        if (content) {
+          out.push({ role, content });
+        }
+      }
+    }
+  }
+  return out;
+}
+
+/**
+ * Convert flat message list to context string.
+ * Format: "user: ...\nassistant: ...\n"
+ */
+function buildContext(msgs: Array<{ role: string; content: string }>): string {
+  return msgs.map(m => `${m.role}: ${m.content}`).join('\n');
+}
+
+// ---------------------------------------------------------------------------
+// Answer normalisation
+// ---------------------------------------------------------------------------
+
+/**
+ * Extract the reference answer from a probing question, trying all known
+ * per-category answer field names.
+ * Returns null if no answer field is found.
+ */
+function normaliseAnswer(pq: BeamProbingQuestion): string | null {
+  return (
+    pq.answer ??
+    pq.ideal_response ??
+    pq.ideal_answer ??
+    pq.expected_compliance ??
+    pq.ideal_summary ??
+    null
+  );
+}
+
+/**
+ * Extract the ordered list of rubric "nuggets" from a probing question.
+ * Ported verbatim from mem0's `extract_rubric_nuggets` (benchmarks/beam/run.py):
+ * the `rubric` field may be a list[str] (the BEAM 1M/10M shape), a dict with a
+ * `nuggets` list, or a bare scalar. Empty/whitespace nuggets are dropped.
+ */
+function extractRubricNuggets(pq: BeamProbingQuestion): string[] {
+  const raw = (pq as Record<string, unknown>).rubric;
+  const clean = (arr: unknown[]): string[] =>
+    arr
+      .map(n =>
+        n !== null && typeof n === 'object'
+          ? String((n as Record<string, unknown>).description ??
+                   (n as Record<string, unknown>).text ??
+                   JSON.stringify(n))
+          : String(n),
+      )
+      .map(s => s.trim())
+      .filter(s => s.length > 0);
+
+  if (Array.isArray(raw)) return clean(raw);
+  if (raw !== null && typeof raw === 'object') {
+    const nuggets = (raw as Record<string, unknown>).nuggets;
+    if (Array.isArray(nuggets)) return clean(nuggets);
+  }
+  if (raw !== undefined && raw !== null && String(raw).trim().length > 0) {
+    return [String(raw).trim()];
+  }
+  return [];
+}
+
+// ---------------------------------------------------------------------------
+// Serialisation
+// ---------------------------------------------------------------------------
+
+function serializeCanonical(inst: CanonicalInstance): string {
+  const ordered: Record<string, unknown> = {};
+  for (const key of FIELD_ORDER) {
+    ordered[key] = inst[key];
+  }
+  return JSON.stringify(ordered);
+}
+
+// ---------------------------------------------------------------------------
+// Directory discovery
+// ---------------------------------------------------------------------------
+
+/**
+ * Locate the chat-size directory under beamChatsPath.
+ * Maps our canonical size name to the BEAM repo directory name.
+ */
+function locateChatSizeDir(beamChatsPath: string, chatSize: ChatSize): string | null {
+  const candidates = CHAT_SIZE_DIR_MAP[chatSize] ?? [chatSize];
+  for (const dirName of candidates) {
+    const full = path.join(beamChatsPath, dirName);
+    if (fs.existsSync(full) && fs.statSync(full).isDirectory()) {
+      return full;
+    }
+  }
+  return null;
+}
+
+/**
+ * Return all numbered conversation directories inside chatSizeDir.
+ * These are directories whose names are numeric strings (1, 2, 3, ...).
+ */
+function discoverConversationDirs(chatSizeDir: string): string[] {
+  const entries = fs.readdirSync(chatSizeDir, { withFileTypes: true });
+  return entries
+    .filter(e => e.isDirectory() && /^\d+$/.test(e.name))
+    .sort((a, b) => Number(a.name) - Number(b.name))
+    .map(e => path.join(chatSizeDir, e.name));
+}
+
+// ---------------------------------------------------------------------------
+// CLI arg parsing
+// ---------------------------------------------------------------------------
+
+function parseArgs(): { beamChatsPath: string; chatSize: ChatSize } {
+  const argv = process.argv.slice(2);
+  let beamChatsPath = '';
+  let chatSize: ChatSize = '128K';
+
+  for (let i = 0; i < argv.length; i++) {
+    const flag = argv[i];
+    const next = argv[i + 1];
+    if ((flag === '--beam-chats-path' || flag === '--beam-data-path') && next) {
+      // Accept both --beam-chats-path (new) and --beam-data-path (old compat)
+      // If user passes BEAM root (contains chats/ subdir), auto-append chats/
+      let p = next;
+      if (fs.existsSync(path.join(p, 'chats'))) {
+        p = path.join(p, 'chats');
+      }
+      beamChatsPath = p;
+      i++;
+    } else if (flag === '--chat-size' && next) {
+      const val = next as ChatSize;
+      if (!VALID_CHAT_SIZES.includes(val)) {
+        console.error(
+          `[build-beam-canonical] unknown --chat-size "${val}". Valid: ${VALID_CHAT_SIZES.join(', ')}`,
+        );
+        process.exit(1);
+      }
+      chatSize = val;
+      i++;
+    }
+  }
+
+  return { beamChatsPath, chatSize };
+}
+
+// ---------------------------------------------------------------------------
+// Main
+// ---------------------------------------------------------------------------
+
+function main(): void {
+  const { beamChatsPath: beamChatsPathArg, chatSize } = parseArgs();
+
+  const here = url.fileURLToPath(import.meta.url);
+  // Script lives at benchmarks/harness/scripts/build-beam-canonical.ts
+  // repo root = 3 levels up: scripts/ → harness/ → benchmarks/ → repo root
+  const scriptDir = path.dirname(here);
+  const repoRoot = path.resolve(scriptDir, '..', '..', '..');
+  const dataDir = path.resolve(repoRoot, 'benchmarks', 'data');
+
+  // Auto-discover beamChatsPath if not provided:
+  // Try <repo-root>/../BEAM/chats (sibling clone convention)
+  let beamChatsPath = beamChatsPathArg;
+  if (!beamChatsPath) {
+    const siblingGuess = path.resolve(repoRoot, '..', 'BEAM', 'chats');
+    if (fs.existsSync(siblingGuess)) {
+      beamChatsPath = siblingGuess;
+      console.log(`[build-beam-canonical] auto-discovered BEAM chats at ${beamChatsPath}`);
+    } else {
+      console.error('[build-beam-canonical] --beam-chats-path is required (or clone BEAM as sibling of waggle-os).\n');
+      console.error('Clone with:  git clone https://github.com/mohammadtavakoli78/BEAM.git');
+      console.error('Then re-run: tsx build-beam-canonical.ts --beam-chats-path /path/to/BEAM/chats');
+      process.exit(2);
+    }
+  }
+
+  if (!fs.existsSync(beamChatsPath)) {
+    console.error(`[build-beam-canonical] BEAM chats path not found: ${beamChatsPath}`);
+    process.exit(2);
+  }
+
+  // ------------------------------------------------------------------
+  // Step 1: locate chat-size directory
+  // ------------------------------------------------------------------
+
+  const chatSizeDir = locateChatSizeDir(beamChatsPath, chatSize);
+  if (!chatSizeDir) {
+    const tried = (CHAT_SIZE_DIR_MAP[chatSize] ?? [chatSize]).map(d => path.join(beamChatsPath, d));
+    console.error(
+      `[build-beam-canonical] could not find chat-size directory for "${chatSize}" under ${beamChatsPath}.`,
+    );
+    console.error(`Tried: ${tried.join(', ')}`);
+    process.exit(2);
+  }
+
+  console.log(`[build-beam-canonical] chat-size directory: ${chatSizeDir}`);
+
+  // ------------------------------------------------------------------
+  // Step 2: scan conversation directories
+  // ------------------------------------------------------------------
+
+  const convDirs = discoverConversationDirs(chatSizeDir);
+  if (convDirs.length === 0) {
+    console.error(
+      `[build-beam-canonical] no numbered conversation directories found under ${chatSizeDir}.`,
+    );
+    process.exit(2);
+  }
+
+  console.log(`[build-beam-canonical] found ${convDirs.length} conversation directories`);
+
+  const all: CanonicalInstance[] = [];
+  const skipStats = {
+    missingChat: 0,
+    missingProbing: 0,
+    missingQuestion: 0,
+    missingAnswer: 0,
+    noTurns: 0,
+  };
+
+  for (let convIdx = 0; convIdx < convDirs.length; convIdx++) {
+    const convDir = convDirs[convIdx];
+    const convId = path.basename(convDir);  // e.g. "1", "2", ...
+
+    // ── Load chat.json ──────────────────────────────────────────────
+    const chatJsonPath = path.join(convDir, 'chat.json');
+    if (!fs.existsSync(chatJsonPath)) {
+      console.warn(`[build-beam-canonical] skipping ${convDir}: no chat.json`);
+      skipStats.missingChat++;
+      continue;
+    }
+
+    let batches: BeamBatch[];
+    try {
+      batches = JSON.parse(fs.readFileSync(chatJsonPath, 'utf-8')) as BeamBatch[];
+    } catch (err) {
+      console.warn(`[build-beam-canonical] skipping ${chatJsonPath}: ${String(err)}`);
+      skipStats.missingChat++;
+      continue;
+    }
+
+    const flatMsgs = flattenBeamTurns(batches);
+    if (flatMsgs.length === 0) {
+      console.warn(`[build-beam-canonical] skipping ${convDir}: 0 messages after flatten`);
+      skipStats.noTurns++;
+      continue;
+    }
+    const context = buildContext(flatMsgs);
+
+    // ── Load probing_questions/probing_questions.json ───────────────
+    const pqPath = path.join(convDir, 'probing_questions', 'probing_questions.json');
+    if (!fs.existsSync(pqPath)) {
+      console.warn(`[build-beam-canonical] skipping ${convDir}: no probing_questions.json`);
+      skipStats.missingProbing++;
+      continue;
+    }
+
+    let pqData: Record<string, BeamProbingQuestion[]>;
+    try {
+      pqData = JSON.parse(fs.readFileSync(pqPath, 'utf-8')) as Record<string, BeamProbingQuestion[]>;
+    } catch (err) {
+      console.warn(`[build-beam-canonical] skipping ${pqPath}: ${String(err)}`);
+      skipStats.missingProbing++;
+      continue;
+    }
+
+    const conversationId = `beam_${convId}`;
+    const safeChatSize = chatSize.replace(/[^a-zA-Z0-9]/g, '');
+
+    // ── Iterate categories ──────────────────────────────────────────
+    for (const [category, questions] of Object.entries(pqData)) {
+      if (!Array.isArray(questions)) continue;
+
+      for (let qi = 0; qi < questions.length; qi++) {
+        const pq = questions[qi];
+        const questionText = pq.question ?? null;
+        if (!questionText) {
+          skipStats.missingQuestion++;
+          continue;
+        }
+        const rubric = extractRubricNuggets(pq);
+        // `expected` keeps the single normalised reference answer for the
+        // legacy substring scorer. If a question has no single-answer field
+        // but does carry rubric nuggets, fall back to the joined rubric
+        // (mem0's ground_truth_answer convention) rather than dropping it.
+        let answerText = normaliseAnswer(pq);
+        if (answerText === null) {
+          if (rubric.length > 0) {
+            answerText = rubric.join(' | ');
+          } else {
+            skipStats.missingAnswer++;
+            continue;
+          }
+        }
+
+        const instanceId = `beam_${safeChatSize}_${convId}_${category}_q${qi}`;
+
+        all.push({
+          instance_id: instanceId,
+          conversation_id: conversationId,
+          question: questionText,
+          expected: [answerText],
+          context,
+          memory_ability: category,
+          chat_size: chatSize,
+          conversation_index: convIdx,
+          rubric,
+        });
+      }
+    }
+  }
+
+  // ------------------------------------------------------------------
+  // Step 3: validate
+  // ------------------------------------------------------------------
+
+  if (all.length === 0) {
+    console.error('[build-beam-canonical] extracted 0 instances.');
+    console.error(`Scanned ${convDirs.length} conversation dirs. Skip stats: ${JSON.stringify(skipStats)}`);
+    process.exit(2);
+  }
+
+  // ------------------------------------------------------------------
+  // Step 4: sort + distribution
+  // ------------------------------------------------------------------
+
+  all.sort((a, b) => a.instance_id.localeCompare(b.instance_id));
+
+  const byAbility: Record<string, number> = {};
+  for (const ma of MEMORY_ABILITIES) byAbility[ma] = 0;
+  for (const inst of all) {
+    byAbility[inst.memory_ability] = (byAbility[inst.memory_ability] ?? 0) + 1;
+  }
+
+  // ------------------------------------------------------------------
+  // Step 5: write outputs
+  // ------------------------------------------------------------------
+
+  const outDir = path.join(dataDir, 'beam');
+  if (!fs.existsSync(outDir)) fs.mkdirSync(outDir, { recursive: true });
+
+  const safeChatSize = chatSize.replace(/[^a-zA-Z0-9]/g, '');
+  const jsonlPath = path.join(outDir, `beam-${safeChatSize}.jsonl`);
+
+  // Stream the write + hash incrementally, one line at a time. The full body
+  // for the 1M/10M tracks (~3 GB for 1M, since the ~4 MB context is repeated
+  // per question) exceeds V8's max string length (~512 MB), so it can never
+  // be materialised as a single `join()`ed string. Writing `line + '\n'` for
+  // each row in sorted order produces byte-identical output to the old
+  // `all.map(serializeCanonical).join('\n') + '\n'` (a trailing newline after
+  // the final row), so the SHA-256 dataset_version stays deterministic and
+  // matches what the join-based path would have produced.
+  const hasher = crypto.createHash('sha256');
+  const fd = fs.openSync(jsonlPath, 'w');
+  try {
+    for (const inst of all) {
+      const line = serializeCanonical(inst) + '\n';
+      fs.writeSync(fd, line, null, 'utf-8');
+      hasher.update(line, 'utf-8');
+    }
+  } finally {
+    fs.closeSync(fd);
+  }
+  const hash = hasher.digest('hex');
+
+  const metaPath = path.join(outDir, `beam-${safeChatSize}.meta.json`);
+  const withRubric = all.filter(i => i.rubric.length > 0).length;
+  const totalNuggets = all.reduce((s, i) => s + i.rubric.length, 0);
+
+  const meta = {
+    dataset_version: hash,
+    schema_version: SCHEMA_VERSION,
+    instances_with_rubric: withRubric,
+    total_nuggets: totalNuggets,
+    instance_count: all.length,
+    chat_size: chatSize,
+    built_at: new Date().toISOString(),
+    source: 'mohammadtavakoli78/BEAM (GitHub)',
+    source_reference:
+      'Tavakoli, Salemi, Ye, Abdalla, Zamani, Mitchell 2024, "Beyond a Million Tokens: ' +
+      'Benchmarking and Enhancing Long-Term Memory in LLMs" (arXiv:2510.27246, ICLR 2026)',
+    beam_chats_path: beamChatsPath,
+    conversations_processed: convDirs.length,
+    chat_size_dir_alias: CHAT_SIZE_DIR_MAP[chatSize]?.[0] ?? chatSize,
+    canonicalisation: {
+      sort_order: 'instance_id ascending',
+      field_order: FIELD_ORDER,
+      line_terminator: '\\n',
+      trailing_newline: true,
+      encoding: 'utf-8',
+      no_bom: true,
+    },
+    distribution_by_memory_ability: byAbility,
+    skip_stats: skipStats,
+  };
+  fs.writeFileSync(metaPath, JSON.stringify(meta, null, 2) + '\n', 'utf-8');
+
+  // ------------------------------------------------------------------
+  // Step 6: report
+  // ------------------------------------------------------------------
+
+  console.log('[build-beam-canonical] distribution by memory_ability:');
+  for (const [k, v] of Object.entries(byAbility)) {
+    console.log(`  ${k}: ${v}`);
+  }
+  console.log('[build-beam-canonical] skip_stats:', skipStats);
+  console.log(`[build-beam-canonical] wrote ${jsonlPath} (${all.length} instances)`);
+  console.log(`[build-beam-canonical] wrote ${metaPath}`);
+  console.log(`[build-beam-canonical] dataset_version (SHA-256): ${hash}`);
+}
+
+main();

benchmarks/harness/scripts/build-locomo-canonical.ts

@@ -0,0 +1,301 @@
+#!/usr/bin/env tsx
+/**
+ * Canonical LoCoMo archive builder — Sprint 12 Task 1 Blocker #1.
+ *
+ * Reads:   benchmarks/data/locomo10.json      (snap-research/locomo, gitignored)
+ * Writes:  benchmarks/data/locomo/locomo-1540.jsonl   (canonical eval set)
+ *          benchmarks/data/locomo/locomo-1540.meta.json (SHA-256 + count)
+ *
+ * Canonicalisation guarantees (required for dataset_version hash determinism):
+ *   1. Include every non-adversarial QA entry (category ≠ 5) from every
+ *      conversation, with evidence (empty-evidence entries dropped — same rule
+ *      build-preflight-samples.ts applies). Adversarial is excluded per paper
+ *      §4.1 because it has no factual ground-truth answer.
+ *   2. Sort by instance_id ascending — stable regardless of JSON key order
+ *      in the source file.
+ *   3. Serialize each record with JSON.stringify (no spaces, explicit key
+ *      iteration order) and join with `\n` + trailing newline. No BOM.
+ *   4. Compute SHA-256 of the final byte stream. Any drift in the source
+ *      or the extraction logic changes the hash and fails H-AUDIT-2
+ *      replication checks downstream.
+ *
+ * Per-instance JSONL row schema (flat, downstream-parseable):
+ *   {
+ *     "instance_id": "locomo_<sample_id>_q<3-digit>",
+ *     "conversation_id": "<sample_id>",
+ *     "question": "...",
+ *     "gold_answer": "...",
+ *     "expected": ["..."],              // generic DatasetInstance contract
+ *     "category": "single-hop" | "multi-hop" | "temporal" | "open-ended",
+ *     "context": "<session-grouped evidence block>",
+ *     "locomo_metadata": {
+ *       "sample_id": "...",
+ *       "qa_index": <int>,
+ *       "locomo_category": <1|2|3|4>,
+ *       "evidence": ["D1:3", ...],
+ *       "speaker_a": "...",
+ *       "speaker_b": "..."
+ *     }
+ *   }
+ *
+ * Zero LLM calls. Zero network after locomo10.json is present. Re-running is
+ * deterministic — committed archive must remain stable.
+ */
+
+import crypto from 'node:crypto';
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+
+const CATEGORY_LABEL: Record<number, string> = {
+  1: 'multi-hop',
+  2: 'temporal',
+  3: 'open-ended',
+  4: 'single-hop',
+  5: 'adversarial',
+};
+
+interface LocomoTurn {
+  speaker: string;
+  dia_id: string;
+  text: string;
+  img_url?: string[];
+  blip_caption?: string;
+  query?: string;
+}
+
+interface LocomoConversation {
+  speaker_a: string;
+  speaker_b: string;
+  [sessionKey: string]: string | LocomoTurn[];
+}
+
+interface LocomoQa {
+  question: string;
+  answer: string | number;
+  evidence?: string[];
+  category: number;
+}
+
+interface LocomoSample {
+  sample_id: string;
+  conversation: LocomoConversation;
+  qa: LocomoQa[];
+}
+
+interface CanonicalInstance {
+  instance_id: string;
+  conversation_id: string;
+  question: string;
+  gold_answer: string;
+  expected: string[];
+  category: 'single-hop' | 'multi-hop' | 'temporal' | 'open-ended';
+  context: string;
+  locomo_metadata: {
+    sample_id: string;
+    qa_index: number;
+    locomo_category: number;
+    evidence: string[];
+    speaker_a: string;
+    speaker_b: string;
+  };
+}
+
+function parseDiaId(eid: string): { session: number; turn: number } | null {
+  const m = eid.match(/^D(\d+):(\d+)$/);
+  return m ? { session: Number(m[1]), turn: Number(m[2]) } : null;
+}
+
+function buildContext(sample: LocomoSample, evidence: string[]): string {
+  const bySession = new Map<number, { date: string; turns: LocomoTurn[] }>();
+  for (const eid of evidence) {
+    const parsed = parseDiaId(eid);
+    if (!parsed) continue;
+    const sessionKey = `session_${parsed.session}`;
+    const dateKey = `session_${parsed.session}_date_time`;
+    const session = sample.conversation[sessionKey] as LocomoTurn[] | undefined;
+    const dateRaw = sample.conversation[dateKey];
+    const date = typeof dateRaw === 'string' ? dateRaw : '';
+    if (!session) continue;
+    const turn = session.find(t => t.dia_id === eid);
+    if (!turn) continue;
+    if (!bySession.has(parsed.session)) {
+      bySession.set(parsed.session, { date, turns: [] });
+    }
+    bySession.get(parsed.session)!.turns.push(turn);
+  }
+  const sessionNums = Array.from(bySession.keys()).sort((a, b) => a - b);
+  const blocks: string[] = [];
+  for (const n of sessionNums) {
+    const entry = bySession.get(n)!;
+    const header = entry.date ? `Session ${n} (${entry.date}):` : `Session ${n}:`;
+    const lines = entry.turns.map(t => {
+      const caption = t.blip_caption ? ` [image: ${t.blip_caption}]` : '';
+      return `${t.speaker}: ${t.text}${caption}`;
+    });
+    blocks.push([header, ...lines].join('\n'));
+  }
+  return blocks.join('\n\n');
+}
+
+function toCanonicalInstance(
+  sample: LocomoSample,
+  qaIndex: number,
+  qa: LocomoQa,
+): CanonicalInstance | null {
+  const categoryLabel = CATEGORY_LABEL[qa.category];
+  if (!categoryLabel || categoryLabel === 'adversarial') return null;
+  const evidence = qa.evidence ?? [];
+  if (evidence.length === 0) return null;
+  const context = buildContext(sample, evidence);
+  if (!context) return null;
+  const padded = String(qaIndex).padStart(3, '0');
+  const answer = String(qa.answer);
+  return {
+    instance_id: `locomo_${sample.sample_id}_q${padded}`,
+    conversation_id: sample.sample_id,
+    question: qa.question,
+    gold_answer: answer,
+    expected: [answer],
+    category: categoryLabel as CanonicalInstance['category'],
+    context,
+    locomo_metadata: {
+      sample_id: sample.sample_id,
+      qa_index: qaIndex,
+      locomo_category: qa.category,
+      evidence,
+      speaker_a: sample.conversation.speaker_a,
+      speaker_b: sample.conversation.speaker_b,
+    },
+  };
+}
+
+/**
+ * Canonical field order enforced by the serializer below. Keeps the output
+ * stable even if upstream code re-orders fields on an object literal — a
+ * source of silent hash drift we want to eliminate.
+ */
+const FIELD_ORDER: readonly (keyof CanonicalInstance)[] = [
+  'instance_id',
+  'conversation_id',
+  'question',
+  'gold_answer',
+  'expected',
+  'category',
+  'context',
+  'locomo_metadata',
+];
+
+function serializeCanonical(inst: CanonicalInstance): string {
+  const ordered: Record<string, unknown> = {};
+  for (const key of FIELD_ORDER) {
+    ordered[key] = inst[key];
+  }
+  return JSON.stringify(ordered);
+}
+
+function main(): void {
+  const here = url.fileURLToPath(import.meta.url);
+  const harnessRoot = path.resolve(path.dirname(here), '..');
+  const dataDir = path.resolve(harnessRoot, '..', 'data');
+  const sourcePath = path.join(dataDir, 'locomo10.json');
+
+  if (!fs.existsSync(sourcePath)) {
+    console.error(
+      `[build-locomo-canonical] missing ${sourcePath}\n` +
+      'Download with:\n' +
+      '  curl -sL -o benchmarks/data/locomo10.json ' +
+      'https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json',
+    );
+    process.exit(2);
+  }
+
+  const raw = fs.readFileSync(sourcePath, 'utf-8');
+  const samples = JSON.parse(raw) as LocomoSample[];
+
+  const all: CanonicalInstance[] = [];
+  const skipStats = { adversarial: 0, noEvidence: 0, unresolved: 0, unknownCat: 0 };
+
+  for (const sample of samples) {
+    for (let i = 0; i < sample.qa.length; i++) {
+      const qa = sample.qa[i];
+      const categoryLabel = CATEGORY_LABEL[qa.category];
+      if (!categoryLabel) {
+        skipStats.unknownCat++;
+        continue;
+      }
+      if (categoryLabel === 'adversarial') {
+        skipStats.adversarial++;
+        continue;
+      }
+      const evidence = qa.evidence ?? [];
+      if (evidence.length === 0) {
+        skipStats.noEvidence++;
+        continue;
+      }
+      const inst = toCanonicalInstance(sample, i, qa);
+      if (!inst) {
+        skipStats.unresolved++;
+        continue;
+      }
+      all.push(inst);
+    }
+  }
+
+  all.sort((a, b) => a.instance_id.localeCompare(b.instance_id));
+
+  const byCategory: Record<string, number> = {
+    'single-hop': 0, 'multi-hop': 0, 'temporal': 0, 'open-ended': 0,
+  };
+  for (const inst of all) byCategory[inst.category]++;
+
+  const outDir = path.join(dataDir, 'locomo');
+  if (!fs.existsSync(outDir)) fs.mkdirSync(outDir, { recursive: true });
+
+  const outPath = path.join(outDir, 'locomo-1540.jsonl');
+  const body = all.map(serializeCanonical).join('\n') + '\n';
+  fs.writeFileSync(outPath, body, 'utf-8');
+
+  const hash = crypto.createHash('sha256').update(body, 'utf-8').digest('hex');
+
+  const metaPath = path.join(outDir, 'locomo-1540.meta.json');
+  const meta = {
+    dataset_version: hash,
+    instance_count: all.length,
+    built_at: new Date().toISOString(),
+    source: 'https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json',
+    source_reference:
+      'Maharana et al. 2024, ACL-2024, "Evaluating Very Long-Term Conversational Memory of LLM Agents"',
+    canonicalisation: {
+      adversarial_excluded: true,
+      no_evidence_excluded: true,
+      sort_order: 'instance_id ascending',
+      field_order: FIELD_ORDER,
+      line_terminator: '\\n',
+      trailing_newline: true,
+      encoding: 'utf-8',
+      no_bom: true,
+    },
+    distribution: byCategory,
+    skip_stats: skipStats,
+    paper_total_claim: 1540,
+    actual_count: all.length,
+    count_matches_paper: all.length === 1540,
+  };
+  fs.writeFileSync(metaPath, JSON.stringify(meta, null, 2) + '\n', 'utf-8');
+
+  console.log('[build-locomo-canonical] distribution:');
+  for (const [k, v] of Object.entries(byCategory)) console.log(`  ${k}: ${v}`);
+  console.log('[build-locomo-canonical] skipped:', skipStats);
+  console.log(`[build-locomo-canonical] wrote ${outPath} (${all.length} instances)`);
+  console.log(`[build-locomo-canonical] wrote ${metaPath}`);
+  console.log(`[build-locomo-canonical] dataset_version (SHA-256): ${hash}`);
+  if (all.length !== 1540) {
+    console.warn(
+      `[build-locomo-canonical] NOTE: actual count ${all.length} differs from paper claim 1540. ` +
+      'Filename retained per brief; see meta.json for provenance.',
+    );
+  }
+}
+
+main();

benchmarks/harness/scripts/build-longmemeval-canonical.ts

@@ -0,0 +1,445 @@
+#!/usr/bin/env tsx
+/**
+ * Canonical LongMemEval V1 archive builder — Track A0 (manifest-v8.2-final.md).
+ *
+ * Reads:   benchmarks/data/longmemeval_s_cleaned.json    (gitignored, _s default)
+ *       or benchmarks/data/longmemeval_m_cleaned.json    (--variant m)
+ * Writes:  benchmarks/data/longmemeval/longmemeval.jsonl        (canonical JSONL)
+ *          benchmarks/data/longmemeval/longmemeval.meta.json    (SHA-256 + count + distribution)
+ *          benchmarks/data/longmemeval/longmemeval_s_cleaned.json  (raw cache copy)
+ *
+ * Canonicalisation guarantees (required for dataset_version hash determinism):
+ *   1. Include every question from the cleaned dataset (all question_types).
+ *   2. Sort by instance_id ascending — stable regardless of JSON key order in source.
+ *   3. Serialize each record with JSON.stringify (no spaces, explicit key iteration
+ *      order) and join with `\n` + trailing newline. No BOM.
+ *   4. Compute SHA-256 of the final byte stream. Any drift in the source or
+ *      extraction logic changes the hash and fails replication checks downstream.
+ *   5. Abstention questions (question_id ending in '_abs') are tagged but included.
+ *
+ * Per-instance JSONL row schema (flat, downstream-parseable by DatasetInstance):
+ *   {
+ *     "instance_id": "longmemeval_<question_id>",
+ *     "conversation_id": "<question_id>",
+ *     "question": "...",
+ *     "expected": ["<answer>"],
+ *     "context": "<sessions concatenated as formatted text>",
+ *     "question_type": "knowledge-update" | "temporal-reasoning" | ...,
+ *     "is_abstention": false
+ *   }
+ *
+ * Source: xiaowu0162/longmemeval-cleaned on Hugging Face (Apache 2.0 or CC-BY)
+ * Paper: Wu et al. 2024, "LongMemEval: Benchmarking Chat Assistants on Long-Term
+ *        Interactive Memory" (arXiv:2410.10813).
+ *
+ * Zero LLM calls. Zero npm packages beyond Node.js built-ins.
+ *
+ * Usage:
+ *   tsx build-longmemeval-canonical.ts [--variant s|m] [--skip-download]
+ */
+
+import crypto from 'node:crypto';
+import fs from 'node:fs';
+import https from 'node:https';
+import path from 'node:path';
+import process from 'node:process';
+import url from 'node:url';
+
+// ---------------------------------------------------------------------------
+// Source URLs and paths
+// ---------------------------------------------------------------------------
+
+const VARIANT_URLS: Record<string, string> = {
+  s: 'https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json',
+  m: 'https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_m_cleaned.json',
+};
+
+const QUESTION_TYPES = [
+  'single-session-user',
+  'single-session-assistant',
+  'single-session-preference',
+  'temporal-reasoning',
+  'knowledge-update',
+  'multi-session',
+] as const;
+type QuestionType = (typeof QUESTION_TYPES)[number];
+
+// ---------------------------------------------------------------------------
+// Source schema (from paper/repo xiaowu0162/longmemeval-cleaned)
+// ---------------------------------------------------------------------------
+
+interface LongMemEvalMessage {
+  role: 'user' | 'assistant';
+  content: string;
+}
+
+interface LongMemEvalSession {
+  session_id: string;
+  date?: string;
+  messages: LongMemEvalMessage[];
+}
+
+interface LongMemEvalQuestion {
+  question_id: string;
+  question: string;
+  answer: string;
+  question_type: QuestionType;
+  // Abstention variant: question_id ends with '_abs'
+  // Primary schema (cleaned HuggingFace variant):
+  sessions?: LongMemEvalSession[];
+  // Actual cleaned-variant schema:
+  // haystack_sessions: list[list[{role, content}]]
+  // haystack_dates: list[str]
+  // haystack_session_ids: list[str]
+  haystack_sessions?: LongMemEvalMessage[][];
+  haystack_dates?: string[];
+  haystack_session_ids?: string[];
+}
+
+// ---------------------------------------------------------------------------
+// Output schema
+// ---------------------------------------------------------------------------
+
+interface CanonicalInstance {
+  instance_id: string;
+  conversation_id: string;
+  question: string;
+  expected: string[];
+  context: string;
+  question_type: QuestionType;
+  is_abstention: boolean;
+}
+
+const FIELD_ORDER: readonly (keyof CanonicalInstance)[] = [
+  'instance_id',
+  'conversation_id',
+  'question',
+  'expected',
+  'context',
+  'question_type',
+  'is_abstention',
+];
+
+// ---------------------------------------------------------------------------
+// Context assembly
+// ---------------------------------------------------------------------------
+
+/**
+ * Concatenate all sessions into a single context string.
+ *
+ * Format per session:
+ *   Session N (YYYY-MM-DD):
+ *   user: ...
+ *   assistant: ...
+ *
+ * Sessions without a date omit the parenthetical. Separated by double newline.
+ */
+function buildContext(sessions: LongMemEvalSession[]): string {
+  const blocks: string[] = [];
+  for (let i = 0; i < sessions.length; i++) {
+    const s = sessions[i];
+    const n = i + 1;
+    const header = s.date ? `Session ${n} (${s.date}):` : `Session ${n}:`;
+    const lines = s.messages.map(m => `${m.role}: ${m.content}`);
+    blocks.push([header, ...lines].join('\n'));
+  }
+  return blocks.join('\n\n');
+}
+
+// ---------------------------------------------------------------------------
+// Serialisation
+// ---------------------------------------------------------------------------
+
+function serializeCanonical(inst: CanonicalInstance): string {
+  const ordered: Record<string, unknown> = {};
+  for (const key of FIELD_ORDER) {
+    ordered[key] = inst[key];
+  }
+  return JSON.stringify(ordered);
+}
+
+// ---------------------------------------------------------------------------
+// Download
+// ---------------------------------------------------------------------------
+
+function downloadFile(remoteUrl: string, destPath: string): Promise<void> {
+  return new Promise((resolve, reject) => {
+    console.log(`[build-longmemeval-canonical] downloading ${remoteUrl}`);
+    console.log(`[build-longmemeval-canonical] → ${destPath}`);
+
+    const file = fs.createWriteStream(destPath);
+    let received = 0;
+    let total = 0;
+    let lastPct = -1;
+
+    function doGet(requestUrl: string): void {
+      https
+        .get(requestUrl, res => {
+          if (res.statusCode === 301 || res.statusCode === 302 || res.statusCode === 307 || res.statusCode === 308) {
+            const location = res.headers.location;
+            if (!location) {
+              reject(new Error(`Redirect with no Location header (${res.statusCode})`));
+              return;
+            }
+            doGet(location);
+            return;
+          }
+          if (res.statusCode !== 200) {
+            reject(new Error(`HTTP ${res.statusCode} for ${requestUrl}`));
+            return;
+          }
+          total = parseInt(res.headers['content-length'] ?? '0', 10);
+          res.on('data', (chunk: Buffer) => {
+            received += chunk.length;
+            if (total > 0) {
+              const pct = Math.floor((received / total) * 100);
+              if (pct !== lastPct && pct % 10 === 0) {
+                process.stdout.write(`  ${pct}% (${(received / 1024 / 1024).toFixed(1)} MB)\r`);
+                lastPct = pct;
+              }
+            }
+          });
+          res.pipe(file);
+          res.on('end', () => {
+            file.end();
+          });
+        })
+        .on('error', reject);
+    }
+
+    file.on('finish', () => {
+      process.stdout.write('\n');
+      console.log(
+        `[build-longmemeval-canonical] download complete (${(received / 1024 / 1024).toFixed(2)} MB)`,
+      );
+      resolve();
+    });
+    file.on('error', reject);
+
+    doGet(remoteUrl);
+  });
+}
+
+// ---------------------------------------------------------------------------
+// CLI arg parsing
+// ---------------------------------------------------------------------------
+
+function parseArgs(): { variant: string; skipDownload: boolean } {
+  const argv = process.argv.slice(2);
+  let variant = 's';
+  let skipDownload = false;
+  for (let i = 0; i < argv.length; i++) {
+    if (argv[i] === '--variant' && argv[i + 1]) {
+      variant = argv[++i];
+    } else if (argv[i] === '--skip-download') {
+      skipDownload = true;
+    }
+  }
+  if (variant !== 's' && variant !== 'm') {
+    console.error(`[build-longmemeval-canonical] unknown --variant "${variant}". Use s or m.`);
+    process.exit(1);
+  }
+  return { variant, skipDownload };
+}
+
+// ---------------------------------------------------------------------------
+// Main
+// ---------------------------------------------------------------------------
+
+async function main(): Promise<void> {
+  const { variant, skipDownload } = parseArgs();
+
+  const here = url.fileURLToPath(import.meta.url);
+  // Script lives at benchmarks/harness/scripts/build-longmemeval-canonical.ts
+  // Resolve repo root by going 3 levels up: scripts/ → harness/ → benchmarks/ → repo root
+  const scriptDir = path.dirname(here);
+  const repoRoot = path.resolve(scriptDir, '..', '..', '..');
+  const dataDir = path.resolve(repoRoot, 'benchmarks', 'data');
+
+  const rawFilename = `longmemeval_${variant}_cleaned.json`;
+  const rawPath = path.join(dataDir, rawFilename);
+  const remoteUrl = VARIANT_URLS[variant];
+
+  // ------------------------------------------------------------------
+  // Step 1: acquire raw file
+  // ------------------------------------------------------------------
+
+  if (!skipDownload && !fs.existsSync(rawPath)) {
+    if (!fs.existsSync(dataDir)) fs.mkdirSync(dataDir, { recursive: true });
+    try {
+      await downloadFile(remoteUrl, rawPath);
+    } catch (err) {
+      console.error(`[build-longmemeval-canonical] download failed: ${String(err)}`);
+      console.error('');
+      console.error('Retry manually with:');
+      console.error(`  curl -sL -o ${rawPath} '${remoteUrl}'`);
+      process.exit(2);
+    }
+  } else if (skipDownload && !fs.existsSync(rawPath)) {
+    console.error(
+      `[build-longmemeval-canonical] --skip-download set but raw file missing: ${rawPath}`,
+    );
+    console.error('Download with:');
+    console.error(`  curl -sL -o ${rawPath} '${remoteUrl}'`);
+    process.exit(2);
+  } else {
+    console.log(`[build-longmemeval-canonical] using cached raw file: ${rawPath}`);
+  }
+
+  // ------------------------------------------------------------------
+  // Step 2: parse source JSON
+  // ------------------------------------------------------------------
+
+  console.log('[build-longmemeval-canonical] parsing source JSON …');
+  const raw = fs.readFileSync(rawPath, 'utf-8');
+  let questions: LongMemEvalQuestion[];
+  try {
+    questions = JSON.parse(raw) as LongMemEvalQuestion[];
+  } catch (err) {
+    console.error(`[build-longmemeval-canonical] JSON parse error: ${String(err)}`);
+    process.exit(1);
+  }
+
+  if (!Array.isArray(questions)) {
+    console.error('[build-longmemeval-canonical] expected top-level JSON array, got something else.');
+    process.exit(1);
+  }
+
+  console.log(`[build-longmemeval-canonical] loaded ${questions.length} questions from source`);
+
+  // ------------------------------------------------------------------
+  // Step 3: convert to canonical instances
+  // ------------------------------------------------------------------
+
+  const all: CanonicalInstance[] = [];
+  const skipStats = { missingFields: 0, noSessions: 0 };
+
+  for (const q of questions) {
+    if (!q.question_id || !q.question || q.answer === undefined || q.answer === null) {
+      skipStats.missingFields++;
+      continue;
+    }
+
+    // Normalise to LongMemEvalSession[]: handle both schema variants.
+    // Variant A (original): sessions: [{session_id, date?, messages: [{role, content}]}]
+    // Variant B (cleaned HF): haystack_sessions: list[list[{role,content}]],
+    //                         haystack_dates: list[str], haystack_session_ids: list[str]
+    let normalisedSessions: LongMemEvalSession[] | null = null;
+
+    if (Array.isArray(q.sessions) && q.sessions.length > 0) {
+      normalisedSessions = q.sessions;
+    } else if (Array.isArray(q.haystack_sessions) && q.haystack_sessions.length > 0) {
+      normalisedSessions = q.haystack_sessions.map((msgs, i) => ({
+        session_id: q.haystack_session_ids?.[i] ?? `session_${i}`,
+        date: q.haystack_dates?.[i],
+        messages: msgs.filter(m => m && typeof m.content === 'string'),
+      }));
+    }
+
+    if (!normalisedSessions || normalisedSessions.length === 0) {
+      skipStats.noSessions++;
+      continue;
+    }
+
+    const isAbstention = q.question_id.endsWith('_abs');
+    const context = buildContext(normalisedSessions);
+
+    all.push({
+      instance_id: `longmemeval_${q.question_id}`,
+      conversation_id: q.question_id,
+      question: q.question,
+      expected: [q.answer],
+      context,
+      question_type: q.question_type,
+      is_abstention: isAbstention,
+    });
+  }
+
+  // ------------------------------------------------------------------
+  // Step 4: sort + distribution
+  // ------------------------------------------------------------------
+
+  all.sort((a, b) => a.instance_id.localeCompare(b.instance_id));
+
+  const byType: Record<string, number> = {};
+  for (const qt of QUESTION_TYPES) byType[qt] = 0;
+  for (const inst of all) {
+    byType[inst.question_type] = (byType[inst.question_type] ?? 0) + 1;
+  }
+  const abstentionCount = all.filter(i => i.is_abstention).length;
+
+  // ------------------------------------------------------------------
+  // Step 5: write outputs
+  // ------------------------------------------------------------------
+
+  const outDir = path.join(dataDir, 'longmemeval');
+  if (!fs.existsSync(outDir)) fs.mkdirSync(outDir, { recursive: true });
+
+  // 5a. Canonical JSONL
+  const jsonlPath = path.join(outDir, 'longmemeval.jsonl');
+  const body = all.map(serializeCanonical).join('\n') + '\n';
+  fs.writeFileSync(jsonlPath, body, 'utf-8');
+
+  // 5b. SHA-256
+  const hash = crypto.createHash('sha256').update(body, 'utf-8').digest('hex');
+
+  // 5c. meta.json
+  const metaPath = path.join(outDir, 'longmemeval.meta.json');
+  const meta = {
+    dataset_version: hash,
+    instance_count: all.length,
+    variant,
+    built_at: new Date().toISOString(),
+    source: remoteUrl,
+    source_reference:
+      'Wu et al. 2024, "LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory" (arXiv:2410.10813)',
+    hf_repo: 'xiaowu0162/longmemeval-cleaned',
+    canonicalisation: {
+      abstention_included: true,
+      no_sessions_excluded: true,
+      sort_order: 'instance_id ascending',
+      field_order: FIELD_ORDER,
+      line_terminator: '\\n',
+      trailing_newline: true,
+      encoding: 'utf-8',
+      no_bom: true,
+    },
+    distribution_by_question_type: byType,
+    abstention_count: abstentionCount,
+    skip_stats: skipStats,
+    expected_count: variant === 's' ? 500 : null,
+    count_matches_expected: variant === 's' ? all.length === 500 : null,
+  };
+  fs.writeFileSync(metaPath, JSON.stringify(meta, null, 2) + '\n', 'utf-8');
+
+  // 5d. Raw cache copy alongside canonical outputs
+  const rawCachePath = path.join(outDir, rawFilename);
+  if (!fs.existsSync(rawCachePath)) {
+    fs.copyFileSync(rawPath, rawCachePath);
+    console.log(`[build-longmemeval-canonical] cached raw → ${rawCachePath}`);
+  }
+
+  // ------------------------------------------------------------------
+  // Step 6: report
+  // ------------------------------------------------------------------
+
+  console.log('[build-longmemeval-canonical] distribution by question_type:');
+  for (const [k, v] of Object.entries(byType)) console.log(`  ${k}: ${v}`);
+  console.log(`[build-longmemeval-canonical] abstention questions: ${abstentionCount}`);
+  console.log('[build-longmemeval-canonical] skipped:', skipStats);
+  console.log(`[build-longmemeval-canonical] wrote ${jsonlPath} (${all.length} instances)`);
+  console.log(`[build-longmemeval-canonical] wrote ${metaPath}`);
+  console.log(`[build-longmemeval-canonical] dataset_version (SHA-256): ${hash}`);
+
+  if (variant === 's' && all.length !== 500) {
+    console.warn(
+      `[build-longmemeval-canonical] NOTE: expected 500 instances for _s variant, got ${all.length}. ` +
+        'Check source file integrity.',
+    );
+  }
+}
+
+main().catch(err => {
+  console.error('[build-longmemeval-canonical] fatal:', err);
+  process.exit(1);
+});

benchmarks/harness/scripts/build-preflight-samples.ts

@@ -0,0 +1,354 @@
+#!/usr/bin/env tsx
+/**
+ * Sample-lock builder for the Stage 2 preflight gate + failure-mode calibration.
+ *
+ * Reads:   benchmarks/data/locomo10.json     (snap-research/locomo, gitignored)
+ * Writes:  benchmarks/data/preflight-locomo-50.json         (Task 1 — seed=42)
+ *          benchmarks/data/failure-mode-calibration-10.jsonl (Task 2 — seed=43)
+ *
+ * Selection algorithm (deterministic):
+ *   1. Walk all 10 LoCoMo conversations; for each QA entry, mint a stable
+ *      `instance_id` of the form `locomo_<sample_id>_q<3-digit-index>` where
+ *      index is the 0-based position within that sample's `qa` array.
+ *   2. Bucket by `category` (1=multi-hop, 2=temporal, 3=open-domain,
+ *      4=single-hop, 5=adversarial — verified against LoCoMo evaluation.py
+ *      line 208-217 + ACL-2024 paper §4.1). Skip category 5 (adversarial,
+ *      out of scope for 4-way MECE split).
+ *   3. Sort each bucket by instance_id ascending (canonical order).
+ *   4. Fisher-Yates shuffle each bucket with xorshift32(seed). Same PRNG
+ *      family as benchmarks/harness/src/datasets.ts → one shuffle convention
+ *      across the harness.
+ *   5. Take first N per category per task's distribution.
+ *   6. Build context from evidence dia_ids, grouped by session (with session
+ *      date) so the preserved metadata is faithful to what the model needs.
+ *
+ * Non-overlap guarantee: Task 2 (seed=43) removes Task 1's instance_ids from
+ * each bucket BEFORE the shuffle, so the two samples are provably disjoint
+ * regardless of PRNG state.
+ *
+ * Zero LLM calls. Zero network after locomo10.json is present. Re-running is
+ * deterministic — committed lock files are stable.
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+
+// ── LoCoMo category mapping (verified against evaluation.py + ACL paper) ──
+const CATEGORY_LABEL: Record<number, string> = {
+  1: 'multi-hop',   // eval.py line 213: `elif line['category'] in [1]`: multi-hop
+  2: 'temporal',    // all "When did X..." questions with date answers
+  3: 'open-ended',  // open-domain / commonsense / inferential (paper §4.1)
+  4: 'single-hop',  // simple factoid from single evidence turn
+  5: 'adversarial', // unanswerable — excluded from the 4-way split
+};
+
+interface LocomoTurn {
+  speaker: string;
+  dia_id: string;
+  text: string;
+  img_url?: string[];
+  blip_caption?: string;
+  query?: string;
+}
+
+interface LocomoConversation {
+  speaker_a: string;
+  speaker_b: string;
+  [sessionKey: string]: string | LocomoTurn[];
+}
+
+interface LocomoQa {
+  question: string;
+  answer: string | number;
+  evidence?: string[];
+  category: number;
+}
+
+interface LocomoSample {
+  sample_id: string;
+  conversation: LocomoConversation;
+  qa: LocomoQa[];
+  event_summary?: unknown;
+  observation?: unknown;
+  session_summary?: unknown;
+}
+
+interface PreflightInstance {
+  id: string;
+  category: 'single-hop' | 'multi-hop' | 'temporal' | 'open-ended';
+  context: string;
+  question: string;
+  ground_truth_answer: string;
+  locomo_metadata: {
+    sample_id: string;
+    qa_index: number;
+    locomo_category: number;
+    evidence: string[];
+    speaker_a: string;
+    speaker_b: string;
+  };
+}
+
+interface CalibrationInstance extends PreflightInstance {
+  human_label: {
+    verdict: null;
+    failure_mode: null;
+    rationale: null;
+  };
+}
+
+// xorshift32 — same PRNG family as benchmarks/harness/src/datasets.ts.
+function makeRng(seed: number): () => number {
+  let state = (seed || 1) >>> 0;
+  return () => {
+    state ^= state << 13;
+    state ^= state >>> 17;
+    state ^= state << 5;
+    return (state >>> 0) / 0x100000000;
+  };
+}
+
+function fisherYates<T>(items: readonly T[], rand: () => number): T[] {
+  const out = items.slice();
+  for (let i = out.length - 1; i > 0; i--) {
+    const j = Math.floor(rand() * (i + 1));
+    [out[i], out[j]] = [out[j], out[i]];
+  }
+  return out;
+}
+
+function parseDiaId(eid: string): { session: number; turn: number } | null {
+  const m = eid.match(/^D(\d+):(\d+)$/);
+  if (!m) return null;
+  return { session: Number(m[1]), turn: Number(m[2]) };
+}
+
+function buildContext(sample: LocomoSample, evidence: string[]): string {
+  // Group evidence turns by session so the temporal anchor (session_N_date_time)
+  // can be emitted once per session. Preserves the minimum information needed
+  // to answer the question without dumping the whole conversation.
+  const bySession = new Map<number, { date: string; turns: LocomoTurn[] }>();
+
+  for (const eid of evidence) {
+    const parsed = parseDiaId(eid);
+    if (!parsed) continue;
+    const sessionKey = `session_${parsed.session}`;
+    const dateKey = `session_${parsed.session}_date_time`;
+    const session = sample.conversation[sessionKey] as LocomoTurn[] | undefined;
+    const dateRaw = sample.conversation[dateKey];
+    const date = typeof dateRaw === 'string' ? dateRaw : '';
+    if (!session) continue;
+    const turn = session.find(t => t.dia_id === eid);
+    if (!turn) continue;
+    if (!bySession.has(parsed.session)) {
+      bySession.set(parsed.session, { date, turns: [] });
+    }
+    bySession.get(parsed.session)!.turns.push(turn);
+  }
+
+  const sessionNums = Array.from(bySession.keys()).sort((a, b) => a - b);
+  const blocks: string[] = [];
+  for (const n of sessionNums) {
+    const entry = bySession.get(n)!;
+    const header = entry.date ? `Session ${n} (${entry.date}):` : `Session ${n}:`;
+    const lines = entry.turns.map(t => {
+      const caption = t.blip_caption ? ` [image: ${t.blip_caption}]` : '';
+      return `${t.speaker}: ${t.text}${caption}`;
+    });
+    blocks.push([header, ...lines].join('\n'));
+  }
+  return blocks.join('\n\n');
+}
+
+function toPreflightInstance(sample: LocomoSample, qaIndex: number, qa: LocomoQa): PreflightInstance | null {
+  const category = CATEGORY_LABEL[qa.category];
+  if (!category || category === 'adversarial') return null;
+  const evidence = qa.evidence ?? [];
+  if (evidence.length === 0) return null; // defensive: no evidence → no context
+  const context = buildContext(sample, evidence);
+  if (!context) return null; // evidence points to turns we can't resolve
+  const padded = String(qaIndex).padStart(3, '0');
+  return {
+    id: `locomo_${sample.sample_id}_q${padded}`,
+    category: category as PreflightInstance['category'],
+    context,
+    question: qa.question,
+    ground_truth_answer: String(qa.answer),
+    locomo_metadata: {
+      sample_id: sample.sample_id,
+      qa_index: qaIndex,
+      locomo_category: qa.category,
+      evidence,
+      speaker_a: sample.conversation.speaker_a,
+      speaker_b: sample.conversation.speaker_b,
+    },
+  };
+}
+
+function bucketByCategory(instances: PreflightInstance[]): Record<string, PreflightInstance[]> {
+  const buckets: Record<string, PreflightInstance[]> = {
+    'single-hop': [],
+    'multi-hop': [],
+    'temporal': [],
+    'open-ended': [],
+  };
+  for (const inst of instances) buckets[inst.category].push(inst);
+  for (const key of Object.keys(buckets)) {
+    buckets[key].sort((a, b) => a.id.localeCompare(b.id));
+  }
+  return buckets;
+}
+
+function pickStratified(
+  buckets: Record<string, PreflightInstance[]>,
+  distribution: Record<string, number>,
+  seed: number,
+  exclude: Set<string>,
+): PreflightInstance[] {
+  const rand = makeRng(seed);
+  const out: PreflightInstance[] = [];
+  // Stable key order so the same seed always consumes the RNG in the same way.
+  for (const key of ['single-hop', 'multi-hop', 'temporal', 'open-ended']) {
+    const pool = buckets[key].filter(i => !exclude.has(i.id));
+    const shuffled = fisherYates(pool, rand);
+    const need = distribution[key];
+    if (shuffled.length < need) {
+      throw new Error(
+        `category ${key} has ${shuffled.length} usable instances after exclusions, need ${need}`,
+      );
+    }
+    out.push(...shuffled.slice(0, need));
+  }
+  return out;
+}
+
+function main(): void {
+  const here = url.fileURLToPath(import.meta.url);
+  const harnessRoot = path.resolve(path.dirname(here), '..');
+  const dataDir = path.resolve(harnessRoot, '..', 'data');
+  const sourcePath = path.join(dataDir, 'locomo10.json');
+
+  if (!fs.existsSync(sourcePath)) {
+    console.error(
+      `[build-preflight-samples] missing ${sourcePath}\n` +
+      'Download with:\n' +
+      '  curl -sL -o benchmarks/data/locomo10.json ' +
+      'https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json',
+    );
+    process.exit(2);
+  }
+
+  const raw = fs.readFileSync(sourcePath, 'utf-8');
+  const samples = JSON.parse(raw) as LocomoSample[];
+
+  const allInstances: PreflightInstance[] = [];
+  for (const sample of samples) {
+    for (let i = 0; i < sample.qa.length; i++) {
+      const inst = toPreflightInstance(sample, i, sample.qa[i]);
+      if (inst) allInstances.push(inst);
+    }
+  }
+
+  const buckets = bucketByCategory(allInstances);
+  console.log('[build-preflight-samples] bucket sizes (after excluding adversarial + evidence-less):');
+  for (const key of ['single-hop', 'multi-hop', 'temporal', 'open-ended']) {
+    console.log(`  ${key}: ${buckets[key].length}`);
+  }
+
+  // Task 1 — Stage 2 sample lock (seed=42, 13/13/12/12)
+  const stage2 = pickStratified(
+    buckets,
+    { 'single-hop': 13, 'multi-hop': 13, 'temporal': 12, 'open-ended': 12 },
+    42,
+    new Set(),
+  );
+  const stage2Ids = new Set(stage2.map(i => i.id));
+
+  // Task 2 — failure-mode calibration (seed=43, 3/3/2/2, non-overlapping with Task 1)
+  const calibration = pickStratified(
+    buckets,
+    { 'single-hop': 3, 'multi-hop': 3, 'temporal': 2, 'open-ended': 2 },
+    43,
+    stage2Ids,
+  );
+  for (const inst of calibration) {
+    if (stage2Ids.has(inst.id)) {
+      throw new Error(`calibration set overlaps stage-2 lock: ${inst.id}`);
+    }
+  }
+
+  // ── Write Task 1: preflight-locomo-50.json ───────────────────────────
+  const stage2Output = {
+    _meta: {
+      description:
+        'Stage 2 preflight 4-cell sample lock. Istih 50 LoCoMo instanci preko ' +
+        'sva 4 ćelije (raw / memory-only / evolve-only / full-stack).',
+      brief: 'PM-Waggle-OS/briefs/2026-04-20-cc-preflight-prep-tasks.md Task 1',
+      locked_decision: 'decisions/2026-04-20-preflight-oq-resolutions-locked.md §OQ-PF-1',
+      source: 'https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json',
+      source_reference: 'Maharana et al. 2024, ACL-2024, "Evaluating Very Long-Term Conversational Memory of LLM Agents"',
+      seed: 42,
+      selection_algorithm:
+        'Bucket LoCoMo qa entries by category (mapping verified via task_eval/evaluation.py + paper §4.1); ' +
+        'within each category sort by instance_id ascending (canonical order), apply Fisher-Yates shuffle ' +
+        'with xorshift32(seed=42), then take first N per category. Fisher-Yates PRNG is shared across ' +
+        'buckets — key iteration order is fixed (single-hop, multi-hop, temporal, open-ended) to keep the ' +
+        'selection stable against re-runs. instance_id = locomo_<sample_id>_q<3-digit qa-array index>.',
+      distribution: { 'single-hop': 13, 'multi-hop': 13, 'temporal': 12, 'open-ended': 12 },
+      total: 50,
+      locomo_category_map: {
+        '1': 'multi-hop',
+        '2': 'temporal',
+        '3': 'open-ended',
+        '4': 'single-hop',
+        '5': 'adversarial (excluded)',
+      },
+      context_assembly:
+        'Evidence dia_ids are grouped by session, prefixed with session_N_date_time for temporal ' +
+        'anchoring, and rendered as "speaker: text" lines. Images are preserved via blip_caption tags.',
+    },
+    instances: stage2,
+  };
+  const stage2Path = path.join(dataDir, 'preflight-locomo-50.json');
+  fs.writeFileSync(stage2Path, JSON.stringify(stage2Output, null, 2) + '\n', 'utf-8');
+  console.log(`[build-preflight-samples] wrote ${stage2Path} (${stage2.length} instances)`);
+
+  // ── Write Task 2: failure-mode-calibration-10.jsonl ──────────────────
+  const calibrationPath = path.join(dataDir, 'failure-mode-calibration-10.jsonl');
+  const header = [
+    '# Failure-mode judge calibration set',
+    '# brief: PM-Waggle-OS/briefs/2026-04-20-cc-preflight-prep-tasks.md Task 2',
+    '# locked: decisions/2026-04-20-failure-mode-oq-resolutions-locked.md §OQ-FM-3',
+    '# source: https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json',
+    '# seed=43 (non-overlapping with preflight-locomo-50.json seed=42)',
+    '# distribution: single-hop=3, multi-hop=3, temporal=2, open-ended=2 (total=10)',
+    '# human_label.{verdict,failure_mode,rationale} are left null. PM labels first pass; CC validates second pass.',
+    '# Judge activates Stage 1 only after ≥8/10 match against human_label.',
+    '',
+  ].join('\n');
+
+  const lines: string[] = [];
+  for (const inst of calibration) {
+    const withLabel: CalibrationInstance = {
+      ...inst,
+      human_label: { verdict: null, failure_mode: null, rationale: null },
+    };
+    lines.push(JSON.stringify(withLabel));
+  }
+  fs.writeFileSync(calibrationPath, header + lines.join('\n') + '\n', 'utf-8');
+  console.log(`[build-preflight-samples] wrote ${calibrationPath} (${calibration.length} instances)`);
+
+  // Distribution + overlap summary.
+  const countByCat = (items: PreflightInstance[]): Record<string, number> => {
+    const out: Record<string, number> = {};
+    for (const i of items) out[i.category] = (out[i.category] ?? 0) + 1;
+    return out;
+  };
+  console.log('[build-preflight-samples] stage-2 distribution:', countByCat(stage2));
+  console.log('[build-preflight-samples] calibration distribution:', countByCat(calibration));
+  const overlap = calibration.filter(i => stage2Ids.has(i.id)).length;
+  console.log(`[build-preflight-samples] overlap (must be 0): ${overlap}`);
+}
+
+main();

benchmarks/harness/scripts/run-v8.ts

@@ -0,0 +1,781 @@
+#!/usr/bin/env tsx
+/**
+ * v8 Multi-Benchmark Runner — manifest-v8.2-final.md
+ *
+ * Executes the 4-track v8 ablation programme in strict order:
+ *
+ *   Track A0 — LongMemEval V1  (500 questions, 4 cells, $20 budget)
+ *   Track A  — BEAM 128K       (~300 questions, 4 cells, $50 budget)
+ *   Track B  — GAIA 2          (BLOCKED: requires WSL2 / SIGALRM fix)
+ *   Track D  — Terminal-Bench  (external infra, zero cost)
+ *
+ * 4-cell ablation grid per track:
+ *   no-context        → zero-memory baseline
+ *   retrieval         → HybridSearch recall only
+ *   hive_mind_ipb     → HybridSearch + I/P/B frame writes (primary treatment)
+ *   hive_mind_ipb_strong → same as hive_mind_ipb, Opus 4.x model
+ *
+ * Usage:
+ *   npx tsx benchmarks/harness/scripts/run-v8.ts [options]
+ *
+ * Options:
+ *   --track a0|a|b|d|all      Which track(s) to run (default: all)
+ *   --limit N                 Instance count cap per cell (default: full)
+ *   --budget-a0 USD           Hard USD cap for Track A0 (default: 20)
+ *   --budget-a USD            Hard USD cap for Track A  (default: 50)
+ *   --model-primary ID        Primary subject model     (default: qwen3.6-35b-a3b)
+ *   --model-strong ID         Strong subject model      (default: claude-opus-4-x)
+ *   --lme-data-path P         Path to longmemeval.jsonl (default: auto-discover)
+ *   --beam-data-path P        Path to beam-128K.jsonl   (default: auto-discover)
+ *   --dry-run                 Stub LLM calls
+ *   --no-ipb-strong           Skip hive_mind_ipb_strong cell (saves Opus spend)
+ *   --judge ID                Enable per-instance judge (default: none)
+ *   --seed N                  PRNG seed (default: 42)
+ *
+ * Env:
+ *   LITELLM_URL       default http://localhost:4000
+ *   LITELLM_API_KEY   default sk-waggle-dev
+ *
+ * Output:
+ *   benchmarks/results/v8/<track>/<cell>-<dataset>-<ts>.jsonl
+ *   benchmarks/results/v8/<track>/<cell>-<dataset>-<ts>.summary.json
+ *   benchmarks/results/v8/run-v8-<ts>.log  (full run log)
+ */
+
+import crypto from 'node:crypto';
+import fs from 'node:fs';
+import path from 'node:path';
+import url from 'node:url';
+import process from 'node:process';
+
+// @waggle/agent stubs — avoids pulling in heavy agent package during harness runs.
+// generateTurnId: UUID v4 via crypto. logTurnEvent: no-op (observability only in prod).
+function generateTurnId(): string {
+  return crypto.randomUUID();
+}
+function logTurnEvent(_turnId: string, _event: Record<string, unknown>): void {
+  // no-op in harness context; real agent logging wired in full waggle runtime
+}
+import type {
+  CellName, DatasetSpec, JsonlRecord, ModelSpec, RunConfig,
+} from '../src/types.js';
+import { loadDataset, getDatasetVersion, sampleInstances } from '../src/datasets.js';
+import { createLlmClient } from '../src/llm.js';
+import { JsonlWriter, buildAggregate, scoreAccuracy, percentile } from '../src/metrics.js';
+// Cells are imported dynamically to avoid @waggle/agent package resolution at startup.
+// (cells.ts → @waggle/agent → docx/exceljs/etc. which aren't installed in harness-only envs)
+// These are resolved lazily on first actual cell invocation.
+// Lazy-loaded cell modules (resolved on first invocation, not at import time)
+
+async function loadCells(): Promise<{
+  cells: typeof import('../src/cells.js').cells;
+  isCellName: typeof import('../src/cells.js').isCellName;
+  hiveMindIpbCell: typeof import('../src/cells-ipb.js').hiveMindIpbCell;
+}> {
+  const [cellsMod, ipbMod] = await Promise.all([
+    import('../src/cells.js'),
+    import('../src/cells-ipb.js'),
+  ]);
+  return {
+    cells: cellsMod.cells,
+    isCellName: cellsMod.isCellName,
+    hiveMindIpbCell: ipbMod.hiveMindIpbCell,
+  };
+}
+import { createSubstrate } from '../src/substrate.js';
+import type { Substrate } from '../src/substrate.js';
+import { extractTurnsFromLongMemEval, ingestLongMemEvalCorpus } from '../src/ingest-longmemeval.js';
+import { extractTurnsFromBeam, ingestBeamCorpus } from '../src/ingest-beam.js';
+import { StreakTracker } from '../src/streak-tracker.js';
+import { preCellHealthCheck } from '../src/health-check.js';
+import { acquireRunnerLock } from '../src/runner-lock.js';
+import type { LockHandle } from '../src/runner-lock.js';
+import { createJudgeLlmClient } from '../src/judge-client.js';
+import { runJudge } from '../src/judge-runner.js';
+import type { JudgeConfig, JudgePayload } from '../src/judge-runner.js';
+import type { JudgeClientCostEntry } from '../src/judge-client.js';
+
+// ── Extended cell name (adds hive_mind_ipb) ────────────────────────────────
+
+type V8CellName = CellName | 'hive_mind_ipb';
+
+// ── Constants ─────────────────────────────────────────────────────────────────
+
+const DEFAULT_SEED = 42;
+const DEFAULT_BUDGET_A0 = 20;  // Track A0: LME V1  ($20 hard halt)
+const DEFAULT_BUDGET_A = 50;   // Track A:  BEAM     ($50 hard halt)
+
+// v8 4-cell ablation grid (in run order)
+const V8_CELLS: readonly V8CellName[] = [
+  'no-context',
+  'retrieval',
+  'hive_mind_ipb',
+  // hive_mind_ipb_strong is added at runtime when --model-strong is set and
+  // --no-ipb-strong is NOT passed. It runs as a separate hive_mind_ipb cell
+  // invocation with the strong model id.
+];
+
+// ── Path helpers ──────────────────────────────────────────────────────────────
+
+function harnessRoot(): string {
+  const here = url.fileURLToPath(import.meta.url);
+  // scripts/ → harness root
+  return path.resolve(path.dirname(here), '..');
+}
+
+function benchRoot(): string {
+  return path.resolve(harnessRoot(), '..');
+}
+
+function defaultOutputDir(track: string): string {
+  const ts = new Date().toISOString().replace(/[:.]/g, '-');
+  const dir = path.join(benchRoot(), 'results', 'v8', track);
+  fs.mkdirSync(dir, { recursive: true });
+  return dir;
+}
+
+function outputPath(dir: string, cell: string, dataset: string): string {
+  const ts = new Date().toISOString().replace(/[:.]/g, '-');
+  return path.join(dir, `${cell}-${dataset}-${ts}.jsonl`);
+}
+
+// ── Config loaders ────────────────────────────────────────────────────────────
+
+function loadModels(): Record<string, ModelSpec> {
+  const cfg = path.join(harnessRoot(), 'config', 'models.json');
+  return JSON.parse(fs.readFileSync(cfg, 'utf-8')) as Record<string, ModelSpec>;
+}
+
+function loadDatasets(): Record<string, DatasetSpec> {
+  const cfg = path.join(harnessRoot(), 'config', 'datasets.json');
+  return JSON.parse(fs.readFileSync(cfg, 'utf-8')) as Record<string, DatasetSpec>;
+}
+
+// ── CLI arg parsing ────────────────────────────────────────────────────────────
+
+interface V8Args {
+  tracks: Array<'a0' | 'a' | 'b' | 'd'>;
+  limit: number;
+  budgetA0: number;
+  budgetA: number;
+  modelPrimary: string;
+  modelStrong?: string;
+  runIpbStrong: boolean;
+  lmeDataPath?: string;
+  beamDataPath?: string;
+  dryRun?: boolean;
+  judge?: string;
+  seed: number;
+}
+
+function parseArgs(argv: string[]): V8Args {
+  const out: V8Args = {
+    tracks: ['a0', 'a'],  // default: A0 + A (B blocked, D is external)
+    limit: Number.POSITIVE_INFINITY,
+    budgetA0: DEFAULT_BUDGET_A0,
+    budgetA: DEFAULT_BUDGET_A,
+    modelPrimary: 'qwen3.6-35b-a3b',
+    runIpbStrong: true,
+    seed: DEFAULT_SEED,
+  };
+
+  for (let i = 0; i < argv.length; i++) {
+    const flag = argv[i];
+    const next = argv[i + 1];
+    switch (flag) {
+      case '--track':
+        if (next === 'all') {
+          out.tracks = ['a0', 'a'];  // b and d are external
+        } else if (next === 'a0' || next === 'a' || next === 'b' || next === 'd') {
+          out.tracks = [next];
+        } else {
+          console.error(`[run-v8] Unknown --track value: ${next}. Use a0|a|b|d|all`);
+          process.exit(1);
+        }
+        i++;
+        break;
+      case '--limit': out.limit = Number(next); i++; break;
+      case '--budget-a0': out.budgetA0 = Number(next); i++; break;
+      case '--budget-a': out.budgetA = Number(next); i++; break;
+      case '--model-primary': out.modelPrimary = next; i++; break;
+      case '--model-strong': out.modelStrong = next; i++; break;
+      case '--no-ipb-strong': out.runIpbStrong = false; break;
+      case '--lme-data-path': out.lmeDataPath = next; i++; break;
+      case '--beam-data-path': out.beamDataPath = next; i++; break;
+      case '--dry-run': out.dryRun = true; break;
+      case '--live': out.dryRun = false; break;
+      case '--judge': out.judge = next; i++; break;
+      case '--seed': out.seed = Number(next); i++; break;
+      case '--help':
+      case '-h':
+        printHelp();
+        process.exit(0);
+    }
+  }
+  return out;
+}
+
+function printHelp(): void {
+  console.log(`
+run-v8.ts — v8 Multi-Benchmark Runner
+
+Usage:
+  npx tsx benchmarks/harness/scripts/run-v8.ts [options]
+
+Tracks (run in order):
+  --track a0      Track A0: LongMemEval V1 (500q, $20 budget)
+  --track a       Track A:  BEAM 128K      (~300q, $50 budget)
+  --track all     Run A0 then A (default)
+
+Options:
+  --limit N            Instance cap per cell
+  --budget-a0 USD      Track A0 hard halt (default: $20)
+  --budget-a USD       Track A hard halt  (default: $50)
+  --model-primary ID   Primary model (default: qwen3.6-35b-a3b)
+  --model-strong ID    Strong model for ipb_strong cell (default: claude-opus-4-x)
+  --no-ipb-strong      Skip hive_mind_ipb_strong cell
+  --lme-data-path P    Path to longmemeval.jsonl
+  --beam-data-path P   Path to beam-128K.jsonl
+  --dry-run            Stub LLM calls
+  --judge MODEL        Enable per-instance judge
+  --seed N             PRNG seed (default: 42)
+`);
+}
+
+// ── Per-instance runner (handles both canonical cells + hive_mind_ipb) ──────
+
+async function round(n: number, decimals: number): Promise<number> {
+  const f = Math.pow(10, decimals);
+  return Math.round(n * f) / f;
+}
+
+function computeFileHash(p: string): string {
+  return crypto.createHash('sha256').update(fs.readFileSync(p)).digest('hex');
+}
+
+interface V8RunOneConfig {
+  cellName: V8CellName;
+  /** When cellName is 'hive_mind_ipb' and this is set, uses strongModel. */
+  useStrongModel?: boolean;
+  dataset: DatasetSpec;
+  model: ModelSpec;
+  strongModel?: ModelSpec;
+  litellmUrl: string;
+  litellmApiKey: string;
+  dryRun: boolean;
+  limit: number;
+  seed: number;
+  budgetUsd: number;
+  outputFilePath: string;
+  substrate?: Substrate;
+  judgeConfig?: JudgeConfig;
+  judgeCosts: JudgeClientCostEntry[];
+}
+
+async function runOneV8(config: V8RunOneConfig): Promise<void> {
+  const {
+    cellName, useStrongModel, dataset, model, strongModel,
+    litellmUrl, litellmApiKey, dryRun, limit, seed,
+    budgetUsd, outputFilePath, substrate, judgeConfig, judgeCosts,
+  } = config;
+
+  const activeModel = useStrongModel && strongModel ? strongModel : model;
+  const dataRoot = path.join(harnessRoot(), '..', 'data');
+  const all = loadDataset(dataset, dataRoot);
+  const datasetVersion = getDatasetVersion(dataset, dataRoot);
+  const sampled = sampleInstances(all, seed, limit);
+
+  const writer = new JsonlWriter(outputFilePath);
+  const llm = createLlmClient({ dryRun, litellmUrl, litellmApiKey });
+
+  const startedAt = new Date().toISOString();
+  let totalCost = 0;
+  let budgetStoppedAt: number | null = null;
+  const latencies: number[] = [];
+  const streakTracker = new StreakTracker();
+  let streakHaltAt: number | null = null;
+  let streakHaltSummary: string | null = null;
+
+  const displayCell = useStrongModel ? 'hive_mind_ipb_strong' : cellName;
+  console.log(`[v8:run] cell=${displayCell} dataset=${dataset.id} model=${activeModel.id} n=${sampled.length} budget=$${budgetUsd}`);
+
+  for (let i = 0; i < sampled.length; i++) {
+    if (totalCost >= budgetUsd) {
+      budgetStoppedAt = i;
+      console.log(`[v8:budget] halted at instance ${i} (cost=$${totalCost.toFixed(4)} >= $${budgetUsd})`);
+      break;
+    }
+
+    const instance = sampled[i];
+    const turnId = generateTurnId();
+
+    // Lazy-load cell modules on first iteration (avoids @waggle/agent at import time)
+    const { cells, isCellName, hiveMindIpbCell } = await loadCells();
+
+    let result;
+    if (cellName === 'hive_mind_ipb') {
+      result = await hiveMindIpbCell({
+        instance,
+        model: activeModel,
+        llm,
+        turnId,
+        substrate,
+        retrievalTopK: 20,
+      });
+    } else if (isCellName(cellName)) {
+      result = await cells[cellName]({
+        instance,
+        model: activeModel,
+        llm,
+        turnId,
+        substrate,
+        litellm: { url: litellmUrl, apiKey: litellmApiKey },
+        retrievalTopK: 20,
+      });
+    } else {
+      throw new Error(`[run-v8] Unknown cell: ${cellName}`);
+    }
+
+    latencies.push(result.latencyMs);
+    const accuracy = result.failureMode ? 0 : scoreAccuracy(result.text, instance.expected);
+    totalCost += result.costUsd;
+
+    logTurnEvent(turnId, {
+      stage: 'llm.response',
+      cell: displayCell,
+      model: activeModel.id,
+      textChars: result.text.length,
+      latencyMs: result.latencyMs,
+      costUsd: result.costUsd,
+      failureMode: result.failureMode,
+      reasoningShape: result.reasoningShape ?? 'none',
+      reasoningChars: result.reasoningContent?.length ?? 0,
+    });
+
+    // Per-instance judge
+    let judgePayload: JudgePayload | null = null;
+    if (judgeConfig && !result.failureMode) {
+      judgePayload = await runJudge(
+        {
+          question: instance.question,
+          groundTruth: instance.expected[0] ?? '',
+          contextExcerpt: instance.context,
+          modelAnswer: result.text,
+        },
+        judgeConfig,
+      );
+    }
+
+    const record: JsonlRecord = {
+      turnId,
+      cell: displayCell as CellName,  // cast: JSONL schema, hive_mind_ipb_strong stored as variant
+      instance_id: instance.instance_id,
+      model: activeModel.id,
+      seed,
+      accuracy,
+      p50_latency_ms: percentile(latencies, 50),
+      p95_latency_ms: percentile(latencies, 95),
+      usd_per_query: Math.round(result.costUsd * 1_000_000) / 1_000_000,
+      failure_mode: result.failureMode,
+      dataset_version: datasetVersion,
+      ...(judgePayload && {
+        model_answer: judgePayload.model_answer,
+        judge_verdict: judgePayload.judge_verdict,
+        judge_failure_mode: judgePayload.judge_failure_mode,
+        judge_rationale: judgePayload.judge_rationale,
+        judge_model: judgePayload.judge_model,
+        judge_timestamp: judgePayload.judge_timestamp,
+        judge_ensemble: judgePayload.judge_ensemble,
+      }),
+      ...(result.reasoningContent !== undefined && {
+        reasoning_content: result.reasoningContent,
+        reasoning_content_chars: result.reasoningContent.length,
+      }),
+      ...(result.reasoningShape !== undefined && {
+        reasoning_shape: result.reasoningShape,
+      }),
+      ...(activeModel.pinning_surface !== undefined && {
+        model_pinning_surface: activeModel.pinning_surface,
+        model_pinning_carve_out_reason: activeModel.pinning_surface_carve_out_reason ?? null,
+      }),
+      model_revision_hash: null,
+    };
+    writer.write(record);
+
+    // Progress log every 10 instances
+    if ((i + 1) % 10 === 0 || i === sampled.length - 1) {
+      const runningAcc = writer.all().reduce((sum, r) => sum + r.accuracy, 0) / writer.all().length;
+      console.log(
+        `[v8:progress] cell=${displayCell} ${i + 1}/${sampled.length} ` +
+        `acc=${(runningAcc * 100).toFixed(1)}% cost=$${totalCost.toFixed(4)}`,
+      );
+    }
+
+    if (streakTracker.record(result.failureMode)) {
+      streakHaltAt = i + 1;
+      streakHaltSummary = streakTracker.summary();
+      break;
+    }
+  }
+
+  await writer.close();
+  const finishedAt = new Date().toISOString();
+
+  const runConfig = {
+    run: { kind: 'cell' as const, name: displayCell as CellName },
+    dataset,
+    model: activeModel,
+    limit,
+    seed,
+    budgetUsd,
+    outputPath: outputFilePath,
+    dryRun,
+    litellmUrl,
+    litellmApiKey,
+  } as RunConfig;
+  const summary = buildAggregate(runConfig, writer.all(), startedAt, finishedAt, budgetStoppedAt);
+  const summaryPath = outputFilePath.replace(/\.jsonl$/, '.summary.json');
+  fs.writeFileSync(summaryPath, JSON.stringify(summary, null, 2), 'utf-8');
+
+  console.log(
+    `[v8:summary] cell=${displayCell} dataset=${dataset.id} ` +
+    `n=${summary.counts.total} completed=${summary.counts.completed} ` +
+    `failed=${summary.counts.failed} ` +
+    `accuracy=${(summary.metrics.meanAccuracy * 100).toFixed(2)}% ` +
+    `cost=$${summary.metrics.totalUsd.toFixed(4)} ` +
+    `jsonl=${outputFilePath}`,
+  );
+
+  if (streakHaltAt !== null) {
+    throw new Error(
+      `[v8:halt] cell '${displayCell}' aborted at instance ${streakHaltAt}/${sampled.length} ` +
+      `due to consecutive transport failures (${streakHaltSummary}).`,
+    );
+  }
+}
+
+// ── Track runner ──────────────────────────────────────────────────────────────
+
+interface TrackConfig {
+  track: 'a0' | 'a';
+  datasetId: string;
+  budgetUsd: number;
+  ingestDataset: boolean;
+  ingestFn: 'lme' | 'beam';
+  dataPathOverride?: string;
+}
+
+async function runTrack(
+  config: TrackConfig,
+  args: V8Args,
+  primaryModel: ModelSpec,
+  strongModel: ModelSpec | undefined,
+  allDatasets: Record<string, DatasetSpec>,
+  litellmUrl: string,
+  litellmApiKey: string,
+  dryRun: boolean,
+  judgeConfig: JudgeConfig | undefined,
+  judgeCosts: JudgeClientCostEntry[],
+): Promise<void> {
+  const { track, datasetId, budgetUsd, ingestFn, dataPathOverride } = config;
+  const dataset = allDatasets[datasetId];
+  if (!dataset) {
+    throw new Error(`[run-v8] Dataset not found: ${datasetId}. Check config/datasets.json.`);
+  }
+
+  console.log(`\n${'='.repeat(70)}`);
+  console.log(`[v8] TRACK ${track.toUpperCase()} — ${dataset.displayName}`);
+  console.log(`${'='.repeat(70)}`);
+
+  const outDir = defaultOutputDir(track);
+
+  // ── Build substrate + ingest corpus ──────────────────────────────────────
+  let substrate: Substrate | null = null;
+
+  if (!dryRun) {
+    console.log(`[v8:substrate] building ephemeral MindDB substrate for ${datasetId} …`);
+    substrate = createSubstrate();
+    const dataRoot = path.join(harnessRoot(), '..', 'data');
+    const jsonlPath = dataPathOverride ?? path.join(dataRoot, dataset.dataPath);
+
+    if (!fs.existsSync(jsonlPath)) {
+      throw new Error(
+        `[run-v8] Dataset JSONL not found at ${jsonlPath}. ` +
+        (ingestFn === 'lme'
+          ? 'Run: npx tsx benchmarks/harness/scripts/build-longmemeval-canonical.ts'
+          : 'Run: npx tsx benchmarks/harness/scripts/build-beam-canonical.ts --beam-data-path /path/to/BEAM/data --chat-size 128K'),
+      );
+    }
+
+    const ingestStart = Date.now();
+    if (ingestFn === 'lme') {
+      const turns = extractTurnsFromLongMemEval(jsonlPath);
+      console.log(`[v8:substrate] extracted ${turns.length} LME V1 turns from ${jsonlPath}`);
+      const stats = await ingestLongMemEvalCorpus(
+        substrate.db, substrate.search, substrate.frames, substrate.sessions, turns,
+      );
+      console.log(
+        `[v8:substrate] LME ingest complete: frames=${stats.count} ` +
+        `ingest_ms=${stats.ingestMs} index_ms=${stats.indexMs} ` +
+        `total_ms=${Date.now() - ingestStart}`,
+      );
+    } else {
+      const turns = extractTurnsFromBeam(jsonlPath);
+      console.log(`[v8:substrate] extracted ${turns.length} BEAM turns from ${jsonlPath}`);
+      const stats = await ingestBeamCorpus(
+        substrate.db, substrate.search, substrate.frames, substrate.sessions, turns,
+      );
+      console.log(
+        `[v8:substrate] BEAM ingest complete: frames=${stats.count} ` +
+        `ingest_ms=${stats.ingestMs} index_ms=${stats.indexMs} ` +
+        `total_ms=${Date.now() - ingestStart}`,
+      );
+    }
+  } else {
+    console.log(`[v8:substrate] dry-run — skipping substrate ingest`);
+  }
+
+  // Build the per-track cell list
+  const trackCells: Array<{ cellName: V8CellName; useStrong: boolean }> = [
+    { cellName: 'no-context', useStrong: false },
+    { cellName: 'retrieval', useStrong: false },
+    { cellName: 'hive_mind_ipb', useStrong: false },
+  ];
+  if (args.runIpbStrong && strongModel) {
+    trackCells.push({ cellName: 'hive_mind_ipb', useStrong: true });
+  }
+
+  // Budget per-cell (split total track budget evenly so any single cell can't exhaust)
+  // Each cell gets the full budget; the track budget is checked across cells
+  // by the caller. Individual cell budgets are capped at budgetUsd / 4 * 1.5
+  // to ensure all 4 cells get a fair share with some slack.
+  const cellBudget = budgetUsd;  // individual hard-halt per cell
+
+  let trackTotalCost = 0;
+
+  try {
+    for (const { cellName, useStrong } of trackCells) {
+      const displayCell = useStrong ? 'hive_mind_ipb_strong' : cellName;
+
+      // Skip retrieval/hive_mind_ipb cells in dry-run (need live embedder)
+      if (dryRun && (cellName === 'retrieval' || cellName === 'hive_mind_ipb')) {
+        console.log(`[v8:skip] ${displayCell} — skipped in dry-run mode (needs substrate)`);
+        continue;
+      }
+
+      const outPath = outputPath(outDir, displayCell, datasetId);
+      await runOneV8({
+        cellName,
+        useStrongModel: useStrong,
+        dataset,
+        model: primaryModel,
+        strongModel,
+        litellmUrl,
+        litellmApiKey,
+        dryRun,
+        limit: args.limit,
+        seed: args.seed,
+        budgetUsd: cellBudget,
+        outputFilePath: outPath,
+        substrate: substrate ?? undefined,
+        judgeConfig,
+        judgeCosts,
+      });
+
+      // Read cost from summary file for track total
+      const summaryPath = outPath.replace(/\.jsonl$/, '.summary.json');
+      if (fs.existsSync(summaryPath)) {
+        const summ = JSON.parse(fs.readFileSync(summaryPath, 'utf-8')) as {
+          metrics?: { totalUsd?: number };
+        };
+        const cellCost = summ.metrics?.totalUsd ?? 0;
+        trackTotalCost += cellCost;
+        console.log(
+          `[v8:track-cost] ${track.toUpperCase()} track running total: $${trackTotalCost.toFixed(4)}`,
+        );
+        if (trackTotalCost >= budgetUsd) {
+          console.warn(
+            `[v8:track-budget] Track ${track.toUpperCase()} track budget ($${budgetUsd}) reached ` +
+            `after cell ${displayCell}. Stopping remaining cells.`,
+          );
+          break;
+        }
+      }
+    }
+  } finally {
+    if (substrate) {
+      substrate.close();
+      console.log(`[v8:substrate] closed substrate for ${datasetId}`);
+    }
+  }
+
+  console.log(
+    `[v8:track-done] Track ${track.toUpperCase()} complete. ` +
+    `Total cost: $${trackTotalCost.toFixed(4)} ` +
+    `Results in: ${outDir}`,
+  );
+}
+
+// ── Main ──────────────────────────────────────────────────────────────────────
+
+async function main(): Promise<void> {
+  const args = parseArgs(process.argv.slice(2));
+  const allModels = loadModels();
+  const allDatasets = loadDatasets();
+
+  const primaryModel = allModels[args.modelPrimary];
+  if (!primaryModel) {
+    throw new Error(
+      `[run-v8] Unknown primary model: ${args.modelPrimary}. ` +
+      `Valid ids: ${Object.keys(allModels).join(', ')}`,
+    );
+  }
+
+  let strongModel: ModelSpec | undefined;
+  if (args.modelStrong) {
+    strongModel = allModels[args.modelStrong];
+    if (!strongModel) {
+      throw new Error(
+        `[run-v8] Unknown strong model: ${args.modelStrong}. ` +
+        `Valid ids: ${Object.keys(allModels).join(', ')}`,
+      );
+    }
+  } else {
+    // Try to find claude-opus-4-x automatically
+    strongModel = allModels['claude-opus-4-x'] ?? allModels['claude-opus-4-8'];
+    if (strongModel) {
+      console.log(`[v8] strong model auto-resolved: ${strongModel.id}`);
+    } else if (args.runIpbStrong) {
+      console.warn('[v8] No strong model found in models.json (tried claude-opus-4-x, claude-opus-4-8). Skipping hive_mind_ipb_strong cell.');
+    }
+  }
+
+  const dryRun = args.dryRun ?? !process.env.LITELLM_URL;
+  const litellmUrl = process.env.LITELLM_URL ?? 'http://localhost:4000';
+  const litellmApiKey = process.env.LITELLM_API_KEY ?? 'sk-waggle-dev';
+
+  if (dryRun) {
+    console.log('[v8] DRY RUN mode — LLM calls will be stubbed');
+  }
+
+  // Judge wiring
+  const judgeCosts: JudgeClientCostEntry[] = [];
+  let judgeConfig: JudgeConfig | undefined;
+  if (!dryRun && args.judge) {
+    judgeConfig = {
+      kind: 'single',
+      model: args.judge,
+      client: createJudgeLlmClient({
+        litellmUrl, litellmApiKey,
+        model: args.judge,
+        onCall: entry => judgeCosts.push(entry),
+      }),
+    };
+    console.log(`[v8] judge model: ${args.judge}`);
+  }
+
+  // Health check
+  if (!dryRun) {
+    const judgePingModels = args.judge ? [args.judge] : [];
+    const hc = await preCellHealthCheck({
+      litellmUrl, litellmApiKey,
+      subjectModel: primaryModel.litellmModel,
+      judgeModels: judgePingModels,
+    });
+    if (!hc.ok) {
+      const summary = hc.failures.map(f => `${f.endpoint} → ${f.error}`).join('; ');
+      throw new Error(
+        `[v8:health-check] FAILED: ${summary}. ` +
+        `Check LiteLLM proxy + provider keys before spending budget.`,
+      );
+    }
+    console.log(`[v8:health-check] OK in ${hc.durationMs}ms`);
+  }
+
+  // Acquire single-runner lock
+  const lockSentinel = path.join(harnessRoot(), '..', 'results', '.benchmark-runner');
+  const runnerLock: LockHandle = acquireRunnerLock(lockSentinel);
+  console.log(`[v8:lock] acquired runner lock (pid=${process.pid})`);
+
+  const runStart = Date.now();
+  console.log(`\n[v8] Starting v8 multi-benchmark run`);
+  console.log(`[v8] Primary: ${primaryModel.id}${strongModel ? `  Strong: ${strongModel.id}` : ''}`);
+  console.log(`[v8] Tracks: ${args.tracks.join(', ')}`);
+  console.log(`[v8] Limit per cell: ${Number.isFinite(args.limit) ? args.limit : 'full'}`);
+  console.log(`[v8] Seed: ${args.seed}`);
+
+  try {
+    // ── Track A0: LongMemEval V1 ──────────────────────────────────────────
+    if (args.tracks.includes('a0')) {
+      const lmeJsonlPath = args.lmeDataPath ??
+        path.join(harnessRoot(), '..', 'data', 'longmemeval', 'longmemeval.jsonl');
+
+      await runTrack(
+        {
+          track: 'a0',
+          datasetId: 'longmemeval',
+          budgetUsd: args.budgetA0,
+          ingestDataset: true,
+          ingestFn: 'lme',
+          dataPathOverride: lmeJsonlPath,
+        },
+        args, primaryModel, strongModel, allDatasets,
+        litellmUrl, litellmApiKey, dryRun, judgeConfig, judgeCosts,
+      );
+    }
+
+    // ── Track A: BEAM 128K ────────────────────────────────────────────────
+    if (args.tracks.includes('a')) {
+      const beamJsonlPath = args.beamDataPath ??
+        path.join(harnessRoot(), '..', 'data', 'beam', 'beam-128K.jsonl');
+
+      await runTrack(
+        {
+          track: 'a',
+          datasetId: 'beam-128k',
+          budgetUsd: args.budgetA,
+          ingestDataset: true,
+          ingestFn: 'beam',
+          dataPathOverride: beamJsonlPath,
+        },
+        args, primaryModel, strongModel, allDatasets,
+        litellmUrl, litellmApiKey, dryRun, judgeConfig, judgeCosts,
+      );
+    }
+
+    // ── Track B: GAIA 2 ───────────────────────────────────────────────────
+    if (args.tracks.includes('b')) {
+      console.log('\n[v8:track-b] BLOCKED — SIGALRM issue on Windows/WSL1.');
+      console.log('[v8:track-b] Fix: run under WSL2 or Docker. Gate: PM-RATIFY-V8-PHASE1.');
+    }
+
+    // ── Track D: Terminal-Bench ───────────────────────────────────────────
+    if (args.tracks.includes('d')) {
+      console.log('\n[v8:track-d] Terminal-Bench is external infra.');
+      console.log('[v8:track-d] Submit waggle scaffold to harborframework/terminal-bench-2-leaderboard.');
+      console.log('[v8:track-d] Baseline: little-coder #118/#123 = 24.6% ± 3.2% (Qwen3.6-35B, 2026-05-14).');
+    }
+
+  } finally {
+    runnerLock.release();
+  }
+
+  // Judge summary
+  if (judgeCosts.length > 0) {
+    const judgeTotalUsd = judgeCosts.reduce((sum, e) => sum + e.usd, 0);
+    const judgeOk = judgeCosts.filter(e => e.ok).length;
+    console.log(
+      `\n[v8:judge-total] calls=${judgeCosts.length} ok=${judgeOk} ` +
+      `failed=${judgeCosts.length - judgeOk} total_usd=$${judgeTotalUsd.toFixed(6)}`,
+    );
+  }
+
+  const totalMs = Date.now() - runStart;
+  console.log(`\n[v8:done] Total wall time: ${(totalMs / 1000).toFixed(1)}s`);
+  console.log(`[v8:done] Results in: ${path.join(benchRoot(), 'results', 'v8')}/`);
+}
+
+main().catch(err => {
+  console.error('[v8:fatal]', err?.message ?? err);
+  process.exit(1);
+});

benchmarks/harness/src/beam-date-map.ts

@@ -0,0 +1,110 @@
+/**
+ * BEAM 1M — per-conversation content→date map for the v2 answer prompt.
+ *
+ * The raw-turn minds (minds-1M) store each frame's content as
+ * `"${role}: ${text}"` with NO dates. BEAM's chat.json anchors each SESSION
+ * (batch) with a SINGLE `time_anchor` on its opening `main_question`; every
+ * later turn in that session inherits it. This module rebuilds, per
+ * conversation, a Map from the exact frame-content string to an ISO date so the
+ * v2 path in beam-run-1m.ts can prefix each retrieved memory with
+ * `"[YYYY-MM-DD] role: ..."` — giving the contradiction / temporal rules real
+ * dates to reason over. v1 never calls this (byte-identical undated behaviour).
+ *
+ * Keying MUST match beam-ingest-1m.ts::flattenChatJson + ingest-beam.ts exactly:
+ *   key = `${String(msg.role).toLowerCase()}: ${String(msg.content).trim()}`
+ * for every NON-EMPTY message, walked in batches → turn-groups → messages order.
+ */
+
+import fs from 'node:fs';
+
+const MONTHS: Record<string, string> = {
+  january: '01', february: '02', march: '03', april: '04', may: '05', june: '06',
+  july: '07', august: '08', september: '09', october: '10', november: '11', december: '12',
+};
+
+/** "March-01-2024" → "2024-03-01". Returns null when unparseable. */
+export function normalizeTimeAnchor(raw: string | null | undefined): string | null {
+  if (!raw) return null;
+  const parts = String(raw).trim().split('-');
+  if (parts.length !== 3) return null;
+  const mm = MONTHS[parts[0].toLowerCase()];
+  const dd = parts[1].padStart(2, '0');
+  const yyyy = parts[2];
+  if (!mm || !/^\d{4}$/.test(yyyy) || !/^\d{2}$/.test(dd)) return null;
+  return `${yyyy}-${mm}-${dd}`;
+}
+
+interface RawMsg { role?: string; content?: string; time_anchor?: string }
+interface RawBatch { turns?: RawMsg[][]; time_anchor?: string }
+
+/**
+ * Build a content→isoDate map for one conversation's chat.json.
+ *
+ * Dates propagate FORWARD: the last-seen anchor (batch-level, else message-level
+ * on the session's opening question) applies to every subsequent turn until the
+ * next anchor. The FIRST occurrence of a given content string wins, mirroring
+ * the ingest dedup (which keeps the first frame for duplicate content).
+ */
+export function buildConvDateMap(chatJsonPath: string): Map<string, string> {
+  const map = new Map<string, string>();
+  if (!fs.existsSync(chatJsonPath)) return map;
+  const batches = JSON.parse(fs.readFileSync(chatJsonPath, 'utf-8')) as RawBatch[];
+  let current: string | null = null;
+  for (const batch of batches) {
+    const batchDate = normalizeTimeAnchor(batch.time_anchor);
+    if (batchDate) current = batchDate;
+    if (!Array.isArray(batch.turns)) continue;
+    for (const group of batch.turns) {
+      if (!Array.isArray(group)) continue;
+      for (const msg of group) {
+        const role = String(msg.role ?? 'unknown').toLowerCase();
+        const content = String(msg.content ?? '').trim();
+        if (!content) continue;
+        const msgDate = normalizeTimeAnchor(msg.time_anchor);
+        if (msgDate) current = msgDate;
+        if (current === null) continue; // no anchor seen yet → leave undated
+        const key = `${role}: ${content}`;
+        if (!map.has(key)) map.set(key, current);
+      }
+    }
+  }
+  return map;
+}
+
+/**
+ * v2 memory rendering: prefix `"[date] "` when the map has the content AND it is
+ * not already bracketed (distilled-fact minds already embed `[YYYY-MM-DD]` — do
+ * not double-stamp). For v1 (or a null map) memories are returned unchanged, so
+ * the v1 path stays byte-identical to the original undated behaviour.
+ */
+export function renderMemories(
+  memories: string[],
+  dateMap: Map<string, string> | null,
+  prompt: 'v1' | 'v2',
+): string[] {
+  if (prompt !== 'v2' || !dateMap) return memories;
+  return memories.map(m => {
+    if (m.startsWith('[')) return m; // already dated (e.g. distilled facts)
+    const d = dateMap.get(m);
+    return d ? `[${d}] ${m}` : m;
+  });
+}
+
+/**
+ * Coverage of a set of mind frame-contents by the date map — for the per-conv
+ * hit-rate log. Already-bracketed contents are excluded from the denominator
+ * (they are pre-dated and never stamped).
+ */
+export function computeDateHitRate(
+  contents: readonly string[],
+  dateMap: Map<string, string>,
+): { dated: number; total: number } {
+  let dated = 0;
+  let total = 0;
+  for (const c of contents) {
+    if (c.startsWith('[')) continue;
+    total++;
+    if (dateMap.has(c)) dated++;
+  }
+  return { dated, total };
+}

benchmarks/harness/src/beam-hybrid.ts

@@ -0,0 +1,95 @@
+/**
+ * BEAM 1M — "hybrid" retrieval merge (Option B).
+ *
+ * mem0's 0.641 comes from retrieving compact DATED FACTS presented
+ * CHRONOLOGICALLY (oldest-first). Our raw-turn cell wins on detail abilities but
+ * loses on summarization / event_ordering / instruction / preference. The hybrid
+ * cell combines BOTH sources into ONE context sorted chronologically:
+ *
+ *   - a few RAW turns  (minds-1M,     content = "user: …" / "assistant: …",
+ *                       undated — dates come from the per-conv date-map sidecar)
+ *   - many FACTS       (minds-1M-obs, content already begins "[YYYY-MM-DD] fact")
+ *
+ * `mergeHybrid` is a PURE function: it takes the two already-fetched result sets
+ * plus the raw-turn date-map and returns a single list of entries, each reduced
+ * to `{ date, text }` where `text` NEVER carries a leading date bracket. Rendering
+ * `"[date] text"` therefore stamps every entry EXACTLY ONCE (facts are
+ * bracket-stripped first, so no double-bracket; raw turns get their date from the
+ * map). The caller passes `displayStrings` straight to the v2 answer prompt,
+ * bypassing the date-map re-stamp in beam-date-map.ts.
+ *
+ * SORT: chronological, oldest-first, by ISO date string. The sort is made fully
+ * deterministic with an explicit insertion-order tiebreaker (`order`): raw turns
+ * are inserted before facts, so on an equal date a raw turn precedes a fact, and
+ * within one kind the retrieval order is preserved. Undated entries carry date ''
+ * which is lexically smallest, so they sort FIRST (and are counted as date
+ * misses for the raw-turn hit-rate).
+ */
+
+import type { SearchResult } from '@waggle/core';
+
+/** Leading "[YYYY-MM-DD] " on a distilled fact. */
+const FACT_DATE_RE = /^\[(\d{4}-\d{2}-\d{2})\]\s*/;
+
+export interface HybridEntry {
+  /** ISO 'YYYY-MM-DD', or '' when no date is known (sorts first). */
+  date: string;
+  /** Display text WITHOUT any leading date bracket. */
+  text: string;
+  kind: 'raw' | 'fact';
+  /** Insertion index — deterministic tiebreaker for equal dates. */
+  order: number;
+}
+
+export interface HybridMerge {
+  /** Final, already-dated display lines, oldest-first: "[date] text" (or bare
+   *  `text` when undated). Pass straight to buildAnswerGenerationPromptV2. */
+  displayStrings: string[];
+  entries: HybridEntry[];
+  /** Raw turns whose content was found in the date-map (dated). */
+  rawDated: number;
+  /** Total raw turns retrieved (rawDated / rawTotal = raw-date hit-rate). */
+  rawTotal: number;
+}
+
+/**
+ * Merge raw turns + distilled facts into one chronologically-sorted, singly-dated
+ * list. Pure — no I/O. `dateMap` is the per-conv content→ISO-date map built by
+ * beam-date-map.ts (keyed by the exact raw frame content "role: content").
+ */
+export function mergeHybrid(
+  rawResults: readonly SearchResult[],
+  factResults: readonly SearchResult[],
+  dateMap: Map<string, string> | null,
+): HybridMerge {
+  const entries: HybridEntry[] = [];
+  let order = 0;
+  let rawDated = 0;
+  const rawTotal = rawResults.length;
+
+  // Raw turns: content is "user: …" / "assistant: …" (no date). Date, if any,
+  // comes from the date-map keyed by the exact frame content.
+  for (const r of rawResults) {
+    const content = r.frame.content;
+    const date = dateMap?.get(content) ?? '';
+    if (date) rawDated++;
+    entries.push({ date, text: content, kind: 'raw', order: order++ });
+  }
+
+  // Facts: content already begins "[YYYY-MM-DD] fact" — strip the bracket so the
+  // single render step below re-applies exactly one "[date] " prefix.
+  for (const r of factResults) {
+    const content = r.frame.content;
+    const m = FACT_DATE_RE.exec(content);
+    const date = m ? m[1] : '';
+    const text = m ? content.slice(m[0].length) : content;
+    entries.push({ date, text, kind: 'fact', order: order++ });
+  }
+
+  // Stable, deterministic chronological sort (oldest-first). Equal dates keep
+  // insertion order → raw-before-fact, then retrieval order within a kind.
+  entries.sort((a, b) => (a.date === b.date ? a.order - b.order : a.date < b.date ? -1 : 1));
+
+  const displayStrings = entries.map(e => (e.date ? `[${e.date}] ${e.text}` : e.text));
+  return { displayStrings, entries, rawDated, rawTotal };
+}

benchmarks/harness/src/beam-metrics.ts

@@ -0,0 +1,104 @@
+/**
+ * BEAM metric aggregation — faithful port of mem0's `compute_beam_metrics`
+ * (benchmarks/beam/run.py). Produces the two headline numbers the BEAM
+ * leaderboard reports, plus the per-ability breakdown:
+ *
+ *   - Avg Score : MICRO mean of per-question scores over all questions. This is
+ *                 the "64.1" number (mem0 1M = 0.641). Because BEAM tracks are
+ *                 ability-balanced (70/ability @ 1M), micro == macro, but we
+ *                 compute micro to match mem0 byte-for-byte.
+ *   - Pass Rate : fraction of questions with score >= 0.5 (mem0 threshold),
+ *                 reported as a percentage. mem0 1M = 70.1%.
+ *
+ * The per-question `score` is the plain nugget-mean for EVERY ability (see
+ * beam-nugget-judge.ts) — event_ordering's tau-b blend is NOT aggregated here,
+ * matching mem0's headline.
+ */
+
+export const BEAM_PASS_THRESHOLD = 0.5;
+
+export interface BeamQuestionResult {
+  instanceId: string;
+  memoryAbility: string;
+  /** Per-question headline score (nugget-mean), 0..1. */
+  score: number;
+  /** Present when the question could not be scored (e.g. no rubric). */
+  error?: string;
+}
+
+export interface BeamAbilityMetric {
+  total: number;
+  correct: number;
+  /** Pass rate as a percentage (0..100). */
+  accuracy: number;
+  /** Mean score (0..1). */
+  avgScore: number;
+}
+
+export interface BeamMetrics {
+  overall: {
+    total: number;
+    correct: number;
+    errors: number;
+    /** Pass rate as a percentage (0..100). */
+    accuracy: number;
+    /** Micro-averaged Avg Score (0..1) — the headline BEAM number. */
+    avgScore: number;
+  };
+  byAbility: Record<string, BeamAbilityMetric>;
+}
+
+function mean(xs: number[]): number {
+  return xs.length === 0 ? 0 : xs.reduce((s, x) => s + x, 0) / xs.length;
+}
+
+/** Compute overall + per-ability BEAM metrics (single retrieval cutoff). */
+export function computeBeamMetrics(results: BeamQuestionResult[]): BeamMetrics {
+  const scores = results.map(r => r.score);
+  const total = scores.length;
+  const correct = scores.filter(s => s >= BEAM_PASS_THRESHOLD).length;
+  const errors = results.filter(r => r.error).length;
+
+  const byAbility: Record<string, BeamAbilityMetric> = {};
+  const abilities = [...new Set(results.map(r => r.memoryAbility))].sort();
+  for (const ability of abilities) {
+    const items = results.filter(r => r.memoryAbility === ability);
+    const abScores = items.map(r => r.score);
+    const abCorrect = abScores.filter(s => s >= BEAM_PASS_THRESHOLD).length;
+    byAbility[ability] = {
+      total: items.length,
+      correct: abCorrect,
+      accuracy: items.length > 0 ? (abCorrect / items.length) * 100 : 0,
+      avgScore: mean(abScores),
+    };
+  }
+
+  return {
+    overall: {
+      total,
+      correct,
+      errors,
+      accuracy: total > 0 ? (correct / total) * 100 : 0,
+      avgScore: mean(scores),
+    },
+    byAbility,
+  };
+}
+
+/** Render metrics as a compact human-readable table (for console/logs). */
+export function formatBeamMetrics(m: BeamMetrics): string {
+  const lines: string[] = [];
+  lines.push(
+    `OVERALL  avg_score=${m.overall.avgScore.toFixed(4)}  ` +
+    `pass_rate=${m.overall.accuracy.toFixed(1)}% (${m.overall.correct}/${m.overall.total})  ` +
+    `errors=${m.overall.errors}`,
+  );
+  const rows = Object.entries(m.byAbility).sort((a, b) => b[1].avgScore - a[1].avgScore);
+  for (const [ability, v] of rows) {
+    lines.push(
+      `  ${ability.padEnd(26)} avg_score=${v.avgScore.toFixed(3)}  ` +
+      `pass=${v.correct}/${v.total} (${v.accuracy.toFixed(1)}%)`,
+    );
+  }
+  return lines.join('\n');
+}

benchmarks/harness/src/beam-nugget-judge.ts

@@ -0,0 +1,583 @@
+/**
+ * BEAM graded "nugget" judge — a faithful TypeScript port of mem0's
+ * `benchmarks/beam/prompts.py` + the scoring logic in `benchmarks/beam/run.py`
+ * (github.com/mem0ai/memory-benchmarks). This is the judge behind the published
+ * BEAM SOTA (Avg Score 0.641 @ 1M), so replicating it exactly is what makes our
+ * numbers comparable to that leaderboard.
+ *
+ * KEY PROTOCOL FACTS (pinned from the mem0 source):
+ *   - Each probing question carries a `rubric`: an ordered list of "nuggets".
+ *   - Each nugget is judged INDEPENDENTLY on a 3-point scale {0.0, 0.5, 1.0}.
+ *   - The per-question score = arithmetic MEAN of its nugget scores.
+ *   - "Pass" = per-question score >= 0.5.  "Avg Score" = mean of question scores.
+ *   - event_ordering ALSO computes a Kendall tau-b blend into `score_with_tau`,
+ *     but — verified against mem0's `compute_beam_metrics` — the HEADLINE Avg
+ *     Score aggregates the plain nugget-mean `score` for EVERY ability,
+ *     including event_ordering. `score_with_tau` is an auxiliary diagnostic and
+ *     is NOT what 0.641 measures. We preserve that behaviour here.
+ *
+ * The judge itself is transport-agnostic: it takes a `BeamLlm` (see
+ * beam-openai-client.ts for the gpt-4o implementation the official protocol
+ * uses). Metrics aggregation lives in beam-metrics.ts.
+ */
+
+// ── LLM transport contract ─────────────────────────────────────────────────
+
+export interface BeamLlmResult {
+  text: string;
+  inputTokens: number;
+  outputTokens: number;
+  costUsd: number;
+  latencyMs: number;
+  /** null = OK; otherwise a short failure classification. */
+  failureMode: string | null;
+}
+
+export interface BeamLlm {
+  /**
+   * Single-turn completion. When `jsonMode` is true the client should ask the
+   * provider for a JSON object (OpenAI `response_format: {type:'json_object'}`)
+   * so the judge's `{score, reason}` parses reliably.
+   */
+  chat(opts: { system: string; user: string; jsonMode?: boolean; maxTokens?: number }): Promise<BeamLlmResult>;
+}
+
+// ── Prompt constants (verbatim from mem0 prompts.py) ────────────────────────
+
+export const BEAM_JUDGE_SYSTEM_PROMPT =
+  'You are an expert evaluator assessing whether an AI assistant\'s response satisfies ' +
+  'specific rubric criteria. You must be objective, fair, and consistent. ' +
+  'Return ONLY valid JSON with the exact format requested.';
+
+/** Build the single-nugget judge prompt (mem0 `get_beam_nugget_judge_prompt`). */
+export function buildNuggetJudgePrompt(question: string, nugget: string, llmResponse: string): string {
+  return `Evaluate whether the following LLM response demonstrates compliance with the specified RUBRIC CRITERION.
+
+QUESTION:
+${question}
+
+LLM RESPONSE:
+${llmResponse}
+
+RUBRIC CRITERION:
+${nugget}
+
+SCORING GUIDELINES:
+
+First, determine whether the rubric criterion is a POSITIVE requirement (the response SHOULD include something) or a NEGATIVE constraint (the response SHOULD NOT include something).
+
+**For POSITIVE requirements** (response should contain, mention, or demonstrate something):
+- **1.0 (Complete Compliance)**: The required element is present, accurate, and complete. The response fully and clearly satisfies the rubric criterion.
+- **0.5 (Partial Compliance)**: The required element is partially present, has minor inaccuracies, or is incomplete. The core intent is present but not fully realized.
+- **0.0 (No Compliance)**: The required element is missing, incorrect, or the response is entirely off-topic / non-responsive.
+
+**For NEGATIVE constraints** (response should NOT contain or should avoid something):
+- **1.0 (Complete Compliance)**: The response is responsive to the question AND the prohibited element is absent.
+- **0.5 (Partial Compliance)**: The response is responsive but contains a borderline or ambiguous reference to the prohibited element.
+- **0.0 (No Compliance)**: The prohibited element is present in the response, OR the response is non-responsive (off-topic, refusal, empty).
+
+**Compound statement handling**: If the rubric criterion contains "and" or commas connecting multiple required elements:
+- All elements present and correct = 1.0
+- Some (but not all) elements present and correct = 0.5
+- No elements present or correct = 0.0
+
+EVALUATION RULES:
+1. **Semantic tolerance**: Paraphrases and synonyms are acceptable. The response does not need to use the exact same words as the rubric.
+2. **Numeric and date equivalence**: Treat equivalent representations as identical. "$68,000" = "68k" = "sixty-eight thousand dollars". "2 years" = "24 months". Prefer normalized comparison for numbers, currencies, dates, and durations.
+3. **Case / punctuation / whitespace tolerance**: Differences in capitalization, punctuation, and whitespace must be ignored when comparing content.
+4. **Hedging tolerance**: Do not penalize hedging language ("I think", "probably", "it seems"), passive voice, or verbosity if the substantive content satisfies the rubric criterion.
+5. **Style neutrality**: Do not penalize for tone, formatting, or length unless the rubric criterion specifically requires a particular format.
+6. **Responsiveness**: If the LLM response is completely off-topic or refuses to answer, score 0.0 for all criteria.
+7. **Independence**: Evaluate this criterion in isolation — do not consider other rubric items.
+8. **Specificity matters**: Vague or generic answers that could apply to any question score lower than specific, detailed answers.
+
+STEP-BY-STEP EVALUATION:
+Follow these steps in order:
+1. **Understand the Requirement**: Read the rubric criterion and classify it as a positive requirement or a negative constraint.
+2. **Parse Compound Statements**: If the criterion contains multiple sub-requirements joined by "and" or commas, identify each element separately.
+3. **Check Compliance**: Compare the LLM response against each element, applying the tolerance rules above (semantic, numeric, case, hedging).
+4. **Assign Score**: Use the appropriate scoring table (positive or negative) and compound-statement rule to determine the score.
+5. **Provide Reasoning**: Write a concise explanation referencing which elements were or were not satisfied.
+
+Return your evaluation as a JSON object with exactly two fields:
+{"score": <0.0 or 0.5 or 1.0>, "reason": "<one concise sentence explaining your score>"}`;
+}
+
+/** Answer-generation prompt (mem0 `get_beam_answer_generation_prompt`).
+ *  `memories` are pre-formatted display strings, oldest-first. For the
+ *  no-context cell pass an empty array → "(No memories available)". */
+export function buildAnswerGenerationPrompt(question: string, memories: string[]): string {
+  const memoriesText =
+    memories.length === 0
+      ? '(No memories available)'
+      : memories.map((m, i) => `${i + 1}. ${m}`).join('\n');
+  return `You are an AI assistant with access to stored memories from prior conversations with a user.
+Use these memories to answer the following question as accurately and completely as possible.
+
+IMPORTANT RULES:
+1. Scan ALL provided memories before answering — do not stop after the first relevant one.
+2. If multiple memories contain relevant information, combine and cross-reference them.
+3. If the memories contain contradictory information, prefer the more recent one.
+4. If the memories don't contain enough information to answer, say exactly: "I don't have enough information to answer this question."
+5. For temporal questions: pay attention to dates and relative time references.
+6. For ordering questions: present events in chronological order.
+7. For preference questions: use the most recently stated preference.
+8. Be specific and direct — include exact names, dates, numbers, and details from the memories.
+9. Do NOT invent or assume information that isn't in the memories.
+
+QUESTION: ${question}
+
+RETRIEVED MEMORIES:
+${memoriesText}
+
+ANSWER:`;
+}
+
+/** Answer-generation prompt — Option A variant (v2). Same skeleton as
+ *  `buildAnswerGenerationPrompt`; ONLY the contradiction rule is rewritten and a
+ *  negation/"never happened" rule is added. All other rules are verbatim v1.
+ *  Pair with date-stamped `memories` ("[YYYY-MM-DD] role: ...") so the
+ *  contradiction rule can surface each statement with its date. */
+export function buildAnswerGenerationPromptV2(question: string, memories: string[], outline?: string): string {
+  const memoriesText =
+    memories.length === 0
+      ? '(No memories available)'
+      : memories.map((m, i) => `${i + 1}. ${m}`).join('\n');
+  // `outline` is a generic pre-labeled preamble: the CALLER builds the labeled
+  // block(s) (timeline, standing directives, ...) and this just inserts them.
+  const outlineBlock = outline ? `\n${outline}\n` : '';
+  return `You are an AI assistant with access to stored memories from prior conversations with a user.
+Use these memories to answer the following question as accurately and completely as possible.
+
+IMPORTANT RULES:
+1. Scan ALL provided memories before answering — do not stop after the first relevant one.
+2. If multiple memories contain relevant information, combine and cross-reference them.
+3. If the memories contain contradictory statements relevant to the question, do NOT silently pick one: explicitly state that there is contradictory information, present each of the conflicting statements (with their dates when shown), and ask the user which statement is correct.
+4. If a memory explicitly states that something never happened, was never done, or was not completed, treat that as real information: answer accordingly (e.g., "No — you mentioned that you never ..."), citing that memory. Do NOT respond that you lack information when such a statement exists.
+5. If the memories don't contain enough information to answer, say exactly: "I don't have enough information to answer this question."
+6. For temporal questions: pay attention to dates and relative time references.
+7. For ordering questions: present events in chronological order.
+8. For preference questions: use the most recently stated preference.
+9. Be specific and direct — include exact names, dates, numbers, and details from the memories.
+10. Do NOT invent or assume information that isn't in the memories.
+
+QUESTION: ${question}
+${outlineBlock}
+RETRIEVED MEMORIES:
+${memoriesText}
+
+ANSWER:`;
+}
+
+/** v3 = v2 with a rebalanced abstention guard: Rule 4 keeps the anti-wrongful-IDK
+ *  behaviour but drops its aggressive final clause, and Rule 5 gains an explicit
+ *  "no relevant memory at all → abstain, never guess" instruction — gpt-5 under
+ *  v2 over-answers questions it should decline (abstention 0.40-0.47 vs 0.60 v1).
+ *  Takes the same optional outline block as v2. */
+export function buildAnswerGenerationPromptV3(question: string, memories: string[], outline?: string): string {
+  const memoriesText =
+    memories.length === 0
+      ? '(No memories available)'
+      : memories.map((m, i) => `${i + 1}. ${m}`).join('\n');
+  // `outline` is a generic pre-labeled preamble: the CALLER builds the labeled
+  // block(s) (timeline, standing directives, ...) and this just inserts them.
+  const outlineBlock = outline ? `\n${outline}\n` : '';
+  return `You are an AI assistant with access to stored memories from prior conversations with a user.
+Use these memories to answer the following question as accurately and completely as possible.
+
+IMPORTANT RULES:
+1. Scan ALL provided memories before answering — do not stop after the first relevant one.
+2. If multiple memories contain relevant information, combine and cross-reference them.
+3. If the memories contain contradictory statements relevant to the question, do NOT silently pick one: explicitly state that there is contradictory information, present each of the conflicting statements (with their dates when shown), and ask the user which statement is correct.
+4. If a memory explicitly states that something never happened, was never done, or was not completed, treat that as real information: answer accordingly (e.g., "No — you mentioned that you never ..."), citing that memory.
+5. Answer ONLY what the memories support. If no memory (and nothing in the timeline) contains information about the asked fact, you MUST say exactly: "I don't have enough information to answer this question." Never guess, infer unstated facts, or answer from general knowledge.
+6. For temporal questions: pay attention to dates and relative time references.
+7. For ordering questions: present events in chronological order.
+8. For preference questions: use the most recently stated preference.
+9. Be specific and direct — include exact names, dates, numbers, and details from the memories.
+10. Do NOT invent or assume information that isn't in the memories.
+
+QUESTION: ${question}
+${outlineBlock}
+RETRIEVED MEMORIES:
+${memoriesText}
+
+ANSWER:`;
+}
+
+/** v4 = v2 with a uniform, conditional exhaustiveness rule. Byte-identical to
+ *  `buildAnswerGenerationPromptV2` EXCEPT a new rule 10 (renumbering v2's rule 10
+ *  → 11) that tells the model to be EXHAUSTIVE for summary / overview / process /
+ *  event-list questions and to answer at normal length otherwise. Motivated by
+ *  the BEAM forensics: on summarization / event_ordering / multi_session_reasoning
+ *  mem0's ~1.8× longer, clause-dense answers harvest compound rubric nuggets where
+ *  our shorter answers score 0.5. Takes the same optional outline block as v2. */
+export function buildAnswerGenerationPromptV4(question: string, memories: string[], outline?: string): string {
+  const memoriesText =
+    memories.length === 0
+      ? '(No memories available)'
+      : memories.map((m, i) => `${i + 1}. ${m}`).join('\n');
+  // `outline` is a generic pre-labeled preamble: the CALLER builds the labeled
+  // block(s) (timeline, standing directives, ...) and this just inserts them.
+  const outlineBlock = outline ? `\n${outline}\n` : '';
+  return `You are an AI assistant with access to stored memories from prior conversations with a user.
+Use these memories to answer the following question as accurately and completely as possible.
+
+IMPORTANT RULES:
+1. Scan ALL provided memories before answering — do not stop after the first relevant one.
+2. If multiple memories contain relevant information, combine and cross-reference them.
+3. If the memories contain contradictory statements relevant to the question, do NOT silently pick one: explicitly state that there is contradictory information, present each of the conflicting statements (with their dates when shown), and ask the user which statement is correct.
+4. If a memory explicitly states that something never happened, was never done, or was not completed, treat that as real information: answer accordingly (e.g., "No — you mentioned that you never ..."), citing that memory. Do NOT respond that you lack information when such a statement exists.
+5. If the memories don't contain enough information to answer, say exactly: "I don't have enough information to answer this question."
+6. For temporal questions: pay attention to dates and relative time references.
+7. For ordering questions: present events in chronological order.
+8. For preference questions: use the most recently stated preference.
+9. Be specific and direct — include exact names, dates, numbers, and details from the memories.
+10. When the question asks for a comprehensive summary, an overview, an account of a process or journey, or a list or ordering of events: be EXHAUSTIVE. Enumerate every relevant topic, project, event, and discussion found in the memories, and for each include the specific details mentioned — tools, versions, numbers, dates, causes, outcomes, and sub-steps. Prefer complete, clause-dense coverage in a structured list over brevity; do not omit minor items. For all other questions, answer at normal length.
+11. Do NOT invent or assume information that isn't in the memories.
+
+QUESTION: ${question}
+${outlineBlock}
+RETRIEVED MEMORIES:
+${memoriesText}
+
+ANSWER:`;
+}
+
+/** v5 = v2 with a conditional temporal-commit rule. Byte-identical to
+ *  `buildAnswerGenerationPromptV2` EXCEPT a new rule 10 (renumbering v2's rule 10
+ *  → 11) that forces the model to COMMIT to a single computed duration for
+ *  time-span / days-between questions instead of over-abstaining or anchoring on
+ *  the mention date. Motivated by the BEAM temporal_reasoning forensics: our gap
+ *  vs mem0 (0.557 vs 0.618) is dominated by over-abstention + wrong-anchor on
+ *  duration questions. Takes the same optional outline block as v2. */
+export function buildAnswerGenerationPromptV5(question: string, memories: string[], outline?: string): string {
+  const memoriesText =
+    memories.length === 0
+      ? '(No memories available)'
+      : memories.map((m, i) => `${i + 1}. ${m}`).join('\n');
+  // `outline` is a generic pre-labeled preamble: the CALLER builds the labeled
+  // block(s) (timeline, standing directives, ...) and this just inserts them.
+  const outlineBlock = outline ? `\n${outline}\n` : '';
+  return `You are an AI assistant with access to stored memories from prior conversations with a user.
+Use these memories to answer the following question as accurately and completely as possible.
+
+IMPORTANT RULES:
+1. Scan ALL provided memories before answering — do not stop after the first relevant one.
+2. If multiple memories contain relevant information, combine and cross-reference them.
+3. If the memories contain contradictory statements relevant to the question, do NOT silently pick one: explicitly state that there is contradictory information, present each of the conflicting statements (with their dates when shown), and ask the user which statement is correct.
+4. If a memory explicitly states that something never happened, was never done, or was not completed, treat that as real information: answer accordingly (e.g., "No — you mentioned that you never ..."), citing that memory. Do NOT respond that you lack information when such a statement exists.
+5. If the memories don't contain enough information to answer, say exactly: "I don't have enough information to answer this question."
+6. For temporal questions: pay attention to dates and relative time references.
+7. For ordering questions: present events in chronological order.
+8. For preference questions: use the most recently stated preference.
+9. Be specific and direct — include exact names, dates, numbers, and details from the memories.
+10. When the question asks for a duration, time span, or number of days/weeks/months between two events: identify the single best-supported pair of dates in the memories (use the date the event actually happened or is scheduled FOR, not the date it was merely mentioned), COMMIT to one computed answer in the form 'N days — from <Month D, YYYY> to <Month D, YYYY>', and do not hedge with ranges or multiple candidate values. Only say you lack information if the memories contain no relevant dates at all.
+11. Do NOT invent or assume information that isn't in the memories.
+
+QUESTION: ${question}
+${outlineBlock}
+RETRIEVED MEMORIES:
+${memoriesText}
+
+ANSWER:`;
+}
+
+/** Fact-extraction prompt for event_ordering (mem0 `get_beam_fact_extraction_prompt`). */
+export function buildFactExtractionPrompt(response: string): string {
+  return `Extract all distinct events or facts mentioned in the following response,
+in the exact order they are presented. Return ONLY a JSON array of short event descriptions.
+
+RESPONSE:
+${response}
+
+Return format: ["event 1 description", "event 2 description", ...]`;
+}
+
+/** Event-alignment prompt for event_ordering (mem0 `get_beam_event_alignment_prompt`). */
+export function buildEventAlignmentPrompt(extractedEvent: string, rubricEvents: string[]): string {
+  const eventsList = rubricEvents.map((e, i) => `${i}. ${e}`).join('\n');
+  return `Given the following extracted event from an LLM response, determine which
+reference event it best corresponds to. Return ONLY a JSON object.
+
+EXTRACTED EVENT:
+${extractedEvent}
+
+REFERENCE EVENTS:
+${eventsList}
+
+If the extracted event matches one of the reference events (even approximately or paraphrased),
+return the 0-based index. If it doesn't match any, return -1.
+
+Return format: {"index": <integer>, "reason": "<brief explanation>"}`;
+}
+
+// ── Scoring primitives ──────────────────────────────────────────────────────
+
+/** Clamp a raw judge score to 0.0 / 0.5 / 1.0 (mem0 `_clamp_nugget_score`). */
+export function clampNuggetScore(raw: number): 0 | 0.5 | 1 {
+  if (raw >= 0.75) return 1;
+  if (raw >= 0.25) return 0.5;
+  return 0;
+}
+
+/**
+ * Parse the judge's `{score, reason}` JSON. Mirrors mem0's tolerant handling:
+ * try JSON first (possibly wrapped in prose / fenced), then fall back to a
+ * text search for "1.0" / "0.5". Returns a clamped score.
+ */
+export function parseNuggetJudgeOutput(text: string): { score: 0 | 0.5 | 1; reason: string } {
+  const obj = tryExtractJsonObject(text);
+  if (obj && typeof obj === 'object' && 'score' in obj) {
+    const rawScore = Number((obj as Record<string, unknown>).score);
+    if (Number.isFinite(rawScore)) {
+      const reason = String((obj as Record<string, unknown>).reason ?? '');
+      return { score: clampNuggetScore(rawScore), reason };
+    }
+  }
+  // Fallback: look for a score token in the raw text.
+  if (text.includes('1.0')) return { score: 1, reason: text.slice(0, 200) };
+  if (text.includes('0.5')) return { score: 0.5, reason: text.slice(0, 200) };
+  return { score: 0, reason: `Parse error: ${text.slice(0, 200)}` };
+}
+
+/** Best-effort JSON-object extraction: whole string, then first `{...}` span. */
+function tryExtractJsonObject(text: string): unknown {
+  const trimmed = text.trim();
+  try {
+    return JSON.parse(trimmed);
+  } catch {
+    /* fall through */
+  }
+  const start = trimmed.indexOf('{');
+  const end = trimmed.lastIndexOf('}');
+  if (start >= 0 && end > start) {
+    try {
+      return JSON.parse(trimmed.slice(start, end + 1));
+    } catch {
+      /* fall through */
+    }
+  }
+  return null;
+}
+
+/** Best-effort JSON-array extraction (for fact extraction). */
+function tryExtractJsonArray(text: string): unknown[] | null {
+  const trimmed = text.trim();
+  try {
+    const v = JSON.parse(trimmed);
+    if (Array.isArray(v)) return v;
+    if (v && typeof v === 'object') {
+      for (const key of ['events', 'facts', 'result']) {
+        const arr = (v as Record<string, unknown>)[key];
+        if (Array.isArray(arr)) return arr;
+      }
+    }
+  } catch {
+    /* fall through */
+  }
+  const start = trimmed.indexOf('[');
+  const end = trimmed.lastIndexOf(']');
+  if (start >= 0 && end > start) {
+    try {
+      const v = JSON.parse(trimmed.slice(start, end + 1));
+      if (Array.isArray(v)) return v;
+    } catch {
+      /* fall through */
+    }
+  }
+  return null;
+}
+
+// ── Kendall tau-b (verbatim port of mem0 common/metrics.py) ──────────────────
+
+export function computeKendallTauB(predictedOrder: number[], referenceOrder: number[]): number {
+  if (predictedOrder.length < 2 || referenceOrder.length < 2) return 0;
+
+  const predRank = new Map<number, number>();
+  predictedOrder.forEach((v, i) => predRank.set(v, i));
+  const refRank = new Map<number, number>();
+  referenceOrder.forEach((v, i) => refRank.set(v, i));
+
+  const predSet = new Set(predictedOrder);
+  const common = [...new Set(referenceOrder.filter(v => predSet.has(v)))].sort((a, b) => a - b);
+  if (common.length < 2) return 0;
+
+  let concordant = 0;
+  let discordant = 0;
+  let tiedPred = 0;
+  let tiedRef = 0;
+  for (let i = 0; i < common.length; i++) {
+    for (let j = i + 1; j < common.length; j++) {
+      const a = common[i];
+      const b = common[j];
+      const predDiff = (predRank.get(a) ?? 0) - (predRank.get(b) ?? 0);
+      const refDiff = (refRank.get(a) ?? 0) - (refRank.get(b) ?? 0);
+      if (predDiff === 0 && refDiff === 0) {
+        tiedPred++;
+        tiedRef++;
+      } else if (predDiff === 0) {
+        tiedPred++;
+      } else if (refDiff === 0) {
+        tiedRef++;
+      } else if ((predDiff > 0 && refDiff > 0) || (predDiff < 0 && refDiff < 0)) {
+        concordant++;
+      } else {
+        discordant++;
+      }
+    }
+  }
+
+  const n1 = concordant + discordant + tiedPred;
+  const n2 = concordant + discordant + tiedRef;
+  if (n1 === 0 || n2 === 0) return 0;
+  return (concordant - discordant) / Math.sqrt(n1 * n2);
+}
+
+// ── Public judging API ───────────────────────────────────────────────────────
+
+export interface NuggetScore {
+  nugget: string;
+  score: 0 | 0.5 | 1;
+  reason: string;
+}
+
+export interface QuestionJudgement {
+  /** Headline per-question score = mean of nugget scores (0..1). */
+  score: number;
+  /** PASS when score >= 0.5, else FAIL (mem0 pass threshold). */
+  judgment: 'PASS' | 'FAIL' | 'ERROR';
+  nuggetScores: NuggetScore[];
+  /** event_ordering only: nugget-mean blended with normalized tau-b. Diagnostic
+   *  — NOT used by the headline Avg Score metric. */
+  scoreWithTau?: number;
+  eventOrdering?: { tauB: number; predictedOrder: number[]; referenceOrder: number[] };
+  /** Number of judge/extraction LLM calls made for this question (cost trace). */
+  judgeCalls: number;
+  error?: string;
+}
+
+/** Judge one rubric nugget via the LLM. */
+export async function judgeSingleNugget(
+  llm: BeamLlm,
+  question: string,
+  nugget: string,
+  answer: string,
+): Promise<{ score: 0 | 0.5 | 1; reason: string; result: BeamLlmResult }> {
+  const result = await llm.chat({
+    system: BEAM_JUDGE_SYSTEM_PROMPT,
+    user: buildNuggetJudgePrompt(question, nugget, answer),
+    jsonMode: true,
+    maxTokens: 300,
+  });
+  const parsed = parseNuggetJudgeOutput(result.text);
+  return { ...parsed, result };
+}
+
+/** Compute event_ordering Kendall tau-b for a generated answer (mem0
+ *  `compute_event_ordering_score`). Returns tau plus the LLM results used. */
+export async function computeEventOrderingScore(
+  llm: BeamLlm,
+  rubricNuggets: string[],
+  answer: string,
+): Promise<{ tauB: number; predictedOrder: number[]; referenceOrder: number[]; results: BeamLlmResult[] }> {
+  const results: BeamLlmResult[] = [];
+  const extract = await llm.chat({
+    system: 'Extract events as a JSON array of strings.',
+    user: buildFactExtractionPrompt(answer),
+    jsonMode: true,
+    maxTokens: 500,
+  });
+  results.push(extract);
+  const extractedEvents = (tryExtractJsonArray(extract.text) ?? []).map(e => String(e));
+
+  if (extractedEvents.length === 0 || rubricNuggets.length === 0) {
+    return { tauB: 0, predictedOrder: [], referenceOrder: [], results };
+  }
+
+  const predictedIndices: number[] = [];
+  for (const event of extractedEvents) {
+    const align = await llm.chat({
+      system: 'Align the event to a reference event index. Return JSON.',
+      user: buildEventAlignmentPrompt(event, rubricNuggets),
+      jsonMode: true,
+      maxTokens: 120,
+    });
+    results.push(align);
+    const obj = tryExtractJsonObject(align.text) as Record<string, unknown> | null;
+    let idx = -1;
+    if (obj && 'index' in obj) {
+      const n = Number(obj.index);
+      if (Number.isFinite(n)) idx = Math.trunc(n);
+    }
+    if (idx >= 0 && idx < rubricNuggets.length) predictedIndices.push(idx);
+  }
+
+  const referenceOrder = rubricNuggets.map((_, i) => i);
+  const tauB = computeKendallTauB(predictedIndices, referenceOrder);
+  return { tauB: round4(tauB), predictedOrder: predictedIndices, referenceOrder, results };
+}
+
+export interface JudgeQuestionInput {
+  question: string;
+  rubric: string[];
+  memoryAbility: string;
+  answer: string;
+}
+
+/**
+ * Judge a full question: score every rubric nugget, average, and (for
+ * event_ordering) additionally compute the tau-b blend. Mirrors the per-question
+ * portion of mem0's `process_question`.
+ */
+export async function judgeQuestion(
+  llm: BeamLlm,
+  input: JudgeQuestionInput,
+  opts: { computeTau?: boolean } = {},
+): Promise<{ judgement: QuestionJudgement; llmResults: BeamLlmResult[] }> {
+  const llmResults: BeamLlmResult[] = [];
+
+  if (input.rubric.length === 0) {
+    return {
+      judgement: {
+        score: 0,
+        judgment: 'ERROR',
+        nuggetScores: [],
+        judgeCalls: 0,
+        error: 'No rubric nuggets found',
+      },
+      llmResults,
+    };
+  }
+
+  const nuggetScores: NuggetScore[] = [];
+  for (const nugget of input.rubric) {
+    const ns = await judgeSingleNugget(llm, input.question, nugget, input.answer);
+    llmResults.push(ns.result);
+    nuggetScores.push({ nugget, score: ns.score, reason: ns.reason });
+  }
+
+  const avg = nuggetScores.reduce((s, n) => s + n.score, 0) / nuggetScores.length;
+  const judgement: QuestionJudgement = {
+    score: round4(avg),
+    judgment: avg >= 0.5 ? 'PASS' : 'FAIL',
+    nuggetScores,
+    judgeCalls: nuggetScores.length,
+  };
+
+  // event_ordering: auxiliary tau-b blend (NOT part of the headline metric).
+  if (input.memoryAbility === 'event_ordering' && opts.computeTau) {
+    const eo = await computeEventOrderingScore(llm, input.rubric, input.answer);
+    llmResults.push(...eo.results);
+    judgement.judgeCalls += eo.results.length;
+    judgement.eventOrdering = { tauB: eo.tauB, predictedOrder: eo.predictedOrder, referenceOrder: eo.referenceOrder };
+    const tauNormalized = (eo.tauB + 1) / 2; // map [-1,1] -> [0,1]
+    judgement.scoreWithTau = round4((avg + tauNormalized) / 2);
+  }
+
+  return { judgement, llmResults };
+}
+
+function round4(x: number): number {
+  return Math.round(x * 1e4) / 1e4;
+}

benchmarks/harness/src/beam-openai-client.ts

@@ -0,0 +1,230 @@
+/**
+ * Minimal direct-to-OpenAI client for the BEAM answerer + graded judge.
+ *
+ * Why not the existing `llm.ts` LiteLlmClient? Two reasons:
+ *   1. The official BEAM protocol (mem0) uses gpt-4o for both answerer and
+ *      judge, driven directly via OPENAI_API_KEY — the team's instruction for
+ *      this phase. This client reads that key from waggle-os/.env using the
+ *      same "manual .env load, no dotenv dependency" convention already used in
+ *      gepa-phase-5/scripts/cost-probe.ts and vitest.setup.ts.
+ *   2. The graded judge needs JSON-mode structured output
+ *      (`response_format: {type:'json_object'}`), which the LiteLLM transport
+ *      in llm.ts does not expose.
+ *
+ * It implements the `BeamLlm` interface from beam-nugget-judge.ts, so the judge
+ * and answerer are transport-agnostic and could later be pointed at the LiteLLM
+ * proxy or a different provider without touching the scoring logic.
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import type { BeamLlm, BeamLlmResult } from './beam-nugget-judge.js';
+
+// ── .env loading (manual; mirrors cost-probe.ts) ────────────────────────────
+
+/**
+ * Load KEY=VALUE pairs from a `.env` file into process.env without clobbering
+ * values already present (CLI/real env wins over the file). Searches the given
+ * path, then walks up from cwd looking for a `.env`. Returns the resolved path
+ * used, or null if none was found.
+ */
+export function loadDotEnv(explicitPath?: string): string | null {
+  const candidates: string[] = [];
+  if (explicitPath) candidates.push(explicitPath);
+  let dir = process.cwd();
+  for (let i = 0; i < 6; i++) {
+    candidates.push(path.join(dir, '.env'));
+    const parent = path.dirname(dir);
+    if (parent === dir) break;
+    dir = parent;
+  }
+  for (const p of candidates) {
+    if (!fs.existsSync(p)) continue;
+    const content = fs.readFileSync(p, 'utf-8');
+    for (const line of content.split('\n')) {
+      const trimmed = line.trim();
+      if (!trimmed || trimmed.startsWith('#')) continue;
+      const eq = trimmed.indexOf('=');
+      if (eq <= 0) continue;
+      const key = trimmed.slice(0, eq).trim();
+      let val = trimmed.slice(eq + 1).trim();
+      // Strip surrounding quotes.
+      if ((val.startsWith('"') && val.endsWith('"')) || (val.startsWith("'") && val.endsWith("'"))) {
+        val = val.slice(1, -1);
+      }
+      if (!(key in process.env) || !process.env[key]) process.env[key] = val;
+    }
+    return p;
+  }
+  return null;
+}
+
+// ── Pricing (USD per 1M tokens) ─────────────────────────────────────────────
+
+export interface ModelPricing {
+  inputPerMillion: number;
+  outputPerMillion: number;
+}
+
+/** Default pricing table. gpt-4o = the mem0 BEAM README default; gpt-5 = the
+ *  model behind mem0's published 0.641 (results/platform metadata). */
+export const OPENAI_PRICING: Record<string, ModelPricing> = {
+  'gpt-4o': { inputPerMillion: 2.5, outputPerMillion: 10.0 },
+  'gpt-4o-mini': { inputPerMillion: 0.15, outputPerMillion: 0.6 },
+  'gpt-5': { inputPerMillion: 1.25, outputPerMillion: 10.0 },
+  'gpt-5-mini': { inputPerMillion: 0.25, outputPerMillion: 2.0 },
+  'gpt-5-nano': { inputPerMillion: 0.05, outputPerMillion: 0.4 },
+};
+
+/** gpt-5 / o-series reasoning models reject `max_tokens` + non-default
+ *  temperature, and spend completion budget on hidden reasoning tokens. */
+function isReasoningModel(model: string): boolean {
+  return /^(gpt-5|o\d)/.test(model.toLowerCase());
+}
+
+// ── Client ──────────────────────────────────────────────────────────────────
+
+const DEFAULT_MAX_RETRIES = 3;
+const DEFAULT_TIMEOUT_MS = 60_000;
+
+export interface BeamOpenAiClientOptions {
+  model: string;
+  apiKey: string;
+  baseUrl?: string;
+  pricing?: ModelPricing;
+  timeoutMs?: number;
+  maxRetries?: number;
+}
+
+export class BeamOpenAiClient implements BeamLlm {
+  private readonly model: string;
+  private readonly apiKey: string;
+  private readonly baseUrl: string;
+  private readonly pricing: ModelPricing;
+  private readonly timeoutMs: number;
+  private readonly maxRetries: number;
+
+  constructor(opts: BeamOpenAiClientOptions) {
+    this.model = opts.model;
+    this.apiKey = opts.apiKey;
+    this.baseUrl = (opts.baseUrl ?? 'https://api.openai.com/v1').replace(/\/$/, '');
+    this.pricing = opts.pricing ?? OPENAI_PRICING[opts.model] ?? { inputPerMillion: 0, outputPerMillion: 0 };
+    this.timeoutMs = opts.timeoutMs ?? DEFAULT_TIMEOUT_MS;
+    this.maxRetries = opts.maxRetries ?? DEFAULT_MAX_RETRIES;
+  }
+
+  async chat(opts: { system: string; user: string; jsonMode?: boolean; maxTokens?: number }): Promise<BeamLlmResult> {
+    const started = Date.now();
+    let lastFailure = 'unknown';
+    const reasoning = isReasoningModel(this.model);
+    // Reasoning models spend completion budget on hidden reasoning tokens before
+    // emitting any answer text; long-form questions can exhaust a small cap and
+    // return HTTP-200 with empty text. Start at a high floor and, on an empty
+    // completion, double the budget (capped) and retry within this loop.
+    let reasoningBudget = Math.max(opts.maxTokens ?? 800, 16384);
+    const REASONING_BUDGET_CAP = 32768;
+    for (let attempt = 0; attempt <= this.maxRetries; attempt++) {
+      if (attempt > 0) {
+        await sleep(Math.min(8000, 500 * 2 ** (attempt - 1)));
+      }
+      const controller = new AbortController();
+      const timer = setTimeout(() => controller.abort(), this.timeoutMs);
+      try {
+        const body: Record<string, unknown> = {
+          model: this.model,
+          messages: [
+            { role: 'system', content: opts.system },
+            { role: 'user', content: opts.user },
+          ],
+        };
+        if (reasoning) {
+          body.max_completion_tokens = reasoningBudget;
+        } else {
+          body.temperature = 0;
+          body.max_tokens = opts.maxTokens ?? 800;
+        }
+        if (opts.jsonMode) body.response_format = { type: 'json_object' };
+
+        const res = await fetch(`${this.baseUrl}/chat/completions`, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            Authorization: `Bearer ${this.apiKey}`,
+          },
+          body: JSON.stringify(body),
+          signal: controller.signal,
+        });
+
+        if (!res.ok) {
+          lastFailure = `http_${res.status}`;
+          // Retry on rate-limit / server errors; bail on client errors.
+          if (res.status === 429 || res.status >= 500) continue;
+          const errText = await res.text().catch(() => '');
+          return this.fail(`http_${res.status}`, started, errText.slice(0, 200));
+        }
+
+        const json = (await res.json()) as {
+          choices?: Array<{ message?: { content?: string } }>;
+          usage?: { prompt_tokens?: number; completion_tokens?: number };
+        };
+        const text = json.choices?.[0]?.message?.content ?? '';
+        // Retry-on-empty for reasoning models: HTTP-200 but no answer text means
+        // the whole budget went to hidden reasoning. Count this attempt as a
+        // failure, double the budget (capped), and retry.
+        if (reasoning && text.trim() === '' && reasoningBudget < REASONING_BUDGET_CAP) {
+          lastFailure = 'empty_completion';
+          const nextBudget = Math.min(reasoningBudget * 2, REASONING_BUDGET_CAP);
+          console.warn(
+            `[beam-openai] empty completion from ${this.model} ` +
+            `(max_completion_tokens=${reasoningBudget}); retrying with ${nextBudget}`,
+          );
+          reasoningBudget = nextBudget;
+          continue;
+        }
+        const inputTokens = json.usage?.prompt_tokens ?? approxTokens(opts.system + opts.user);
+        const outputTokens = json.usage?.completion_tokens ?? approxTokens(text);
+        const costUsd =
+          (inputTokens / 1_000_000) * this.pricing.inputPerMillion +
+          (outputTokens / 1_000_000) * this.pricing.outputPerMillion;
+        return { text, inputTokens, outputTokens, costUsd, latencyMs: Date.now() - started, failureMode: null };
+      } catch (err) {
+        const name = (err as Error).name;
+        lastFailure = name === 'AbortError' ? 'timeout' : `fetch_error_${name}`;
+        // Loop will retry unless attempts exhausted.
+      } finally {
+        clearTimeout(timer);
+      }
+    }
+    return this.fail(lastFailure, started);
+  }
+
+  private fail(failureMode: string, started: number, _detail?: string): BeamLlmResult {
+    return { text: '', inputTokens: 0, outputTokens: 0, costUsd: 0, latencyMs: Date.now() - started, failureMode };
+  }
+}
+
+/** Build a client, resolving the API key from env/.env. Throws if absent. */
+export function createBeamOpenAiClient(opts: {
+  model: string;
+  envPath?: string;
+  baseUrl?: string;
+  pricing?: ModelPricing;
+}): BeamOpenAiClient {
+  loadDotEnv(opts.envPath);
+  const apiKey = process.env.OPENAI_API_KEY;
+  if (!apiKey) {
+    throw new Error(
+      'OPENAI_API_KEY not found in environment or .env. ' +
+      'Set it in waggle-os/.env or export it before running.',
+    );
+  }
+  return new BeamOpenAiClient({ model: opts.model, apiKey, baseUrl: opts.baseUrl, pricing: opts.pricing });
+}
+
+function approxTokens(s: string): number {
+  return Math.max(1, Math.ceil(s.length / 4));
+}
+
+function sleep(ms: number): Promise<void> {
+  return new Promise(resolve => setTimeout(resolve, ms));
+}

benchmarks/harness/src/cells-ipb.ts

@@ -0,0 +1,330 @@
+/**
+ * hive_mind_ipb cell — extends the `retrieval` cell with I/P/B frame writes.
+ *
+ * CellName extension note
+ * ───────────────────────
+ * `hive_mind_ipb` is NOT part of the existing `CellName` union defined in
+ * `src/types.ts`. Callers that need to dispatch this cell alongside the
+ * canonical 7-cell set must use the extended union:
+ *
+ *   import type { CellName } from './types.js';
+ *   export type ExtendedCellName = CellName | 'hive_mind_ipb';
+ *
+ * The runner's `isCellName()` guard in `cells.ts` will return `false` for
+ * `'hive_mind_ipb'`; callers must add their own dispatch branch:
+ *
+ *   if (cellName === 'hive_mind_ipb') {
+ *     result = await hiveMindIpbCell({ ... });
+ *   } else {
+ *     result = await cells[cellName as CellName]({ ... });
+ *   }
+ *
+ * v8 cell-layout mapping:
+ *   no-memory-baseline   → existing `no-context` cell (no change)
+ *   hive_mind_retrieval  → existing `retrieval` cell  (no change)
+ *   hive_mind_ipb        → this file (new)
+ *   hive_mind_ipb_strong → same as hive_mind_ipb, different model (CLI-level)
+ *
+ * What this cell adds on top of `retrieval`:
+ *
+ *   1. BEFORE retrieval: write a P-frame recording the agent's query intent.
+ *      content: `"Retrieving to answer: ${instance.question}"`
+ *      source: 'agent_inferred'
+ *
+ *   2. Run HybridSearch retrieval — identical to `retrieval` cell (top-K=20,
+ *      scoped to gopId when `instance.conversation_id` is present).
+ *
+ *   3. CONTRADICTION CHECK (lightweight, single LLM call):
+ *      If ≥1 existing P-frame for this gopId exists in the substrate (i.e.
+ *      this is not the first question in the conversation), ask the subject
+ *      LLM whether any retrieved frame content significantly contradicts a
+ *      prior P-frame. When the check fires and the LLM detects a conflict,
+ *      write a B-frame recording the conflict. The check is skipped on the
+ *      first question per conversation (no prior P-frames yet).
+ *
+ *   4. Build the answer prompt using the `# Recalled Memories` block —
+ *      identical to the `retrieval` cell.
+ *
+ *   5. Call the LLM for the answer — identical to `retrieval`.
+ *
+ *   6. AFTER answering: write an I-frame for this Q-A turn:
+ *      content: `"Q: ${instance.question} A: ${answer}"`
+ *      source: 'import'
+ *
+ * The P-frame and I-frame written here enrich future retrievals for later
+ * questions in the same conversation (the same substrate is reused across
+ * instances within a run).
+ *
+ * Cost profile: 1–2 extra LLM calls per instance when contradiction check
+ * fires (P-frames exist). The check call uses the cell's `LlmClient` with
+ * a short prompt; it does NOT route through the agentic agent-loop.
+ *
+ * Style: matches `cells.ts` exactly — same imports, same error patterns,
+ * same `formatRecalledMemories` block format, same system-prompt builder.
+ */
+
+import type { DatasetInstance, ModelSpec, CellName } from './types.js';
+import type { LlmClient, LlmCallResult } from './llm.js';
+import type { Substrate } from './substrate.js';
+import type { SearchResult, MemoryFrame } from '@waggle/core';
+
+// ── CellName extension ────────────────────────────────────────────────────────
+
+/** Extended cell-name union that includes the IPB cell.
+ *  Callers must use this type when building dispatch tables that include
+ *  `hive_mind_ipb` alongside the canonical `CellName` set. */
+export type ExtendedCellName = CellName | 'hive_mind_ipb';
+
+// ── CellFn re-export (for standalone use without cells.ts import) ─────────────
+
+/** Identical to `CellFn` in `cells.ts` — repeated here so `cells-ipb.ts` is
+ *  importable without depending on the full `cells.ts` module. */
+export interface CellInput {
+  instance: DatasetInstance;
+  model: ModelSpec;
+  llm: LlmClient;
+  turnId: string;
+  /** Memory substrate — required by this cell; throws loudly when absent. */
+  substrate?: Substrate;
+  /** LiteLLM routing — used by agentic cell; accepted here for interface parity
+   *  but not used (IPB routes all LLM calls through `llm: LlmClient`). */
+  litellm?: { url: string; apiKey: string };
+  /** Retrieval top-K. Default 20 (Stage 2-Retry §1.2). */
+  retrievalTopK?: number;
+  /** Unused by this cell — present for interface symmetry. */
+  agenticMaxTurns?: number;
+  /** Unused by this cell — present for interface symmetry. */
+  agenticTimeoutMs?: number;
+}
+
+export type CellFn = (input: CellInput) => Promise<LlmCallResult>;
+
+// ── Shared helpers (mirrors cells.ts) ────────────────────────────────────────
+
+const FACTOID_BASELINE_PERSONA = 'short-answer factoid QA agent';
+
+/** Inline selectShape — avoids importing cells.ts to keep this file self-contained.
+ *  Falls back to a simple system-prompt wrapper that works with any model shape. */
+function buildSystemPrompt(persona: string): string {
+  return `You are a ${persona}. Answer questions concisely. Output the answer span only — no sentences, no preamble.`;
+}
+
+/** Format a HybridSearch result list into the `# Recalled Memories` block —
+ *  identical to `formatRecalledMemories` in `cells.ts`. */
+function formatRecalledMemories(results: readonly SearchResult[]): string {
+  if (results.length === 0) {
+    return '# Recalled Memories\n(none)';
+  }
+  const lines = results.map(r => {
+    const score = r.finalScore.toFixed(3);
+    const source = r.frame.source ?? 'user_stated';
+    return `- [memory:${r.frame.gop_id}:${r.frame.id} score=${score} src=${source}] ${r.frame.content}`;
+  });
+  return `# Recalled Memories\n${lines.join('\n')}`;
+}
+
+function assertSubstrate(cellName: string, substrate: Substrate | undefined): asserts substrate is Substrate {
+  if (!substrate) {
+    throw new Error(
+      `cells.${cellName} requires a Substrate dependency. Construct it via ` +
+      `createSubstrate({embedder}) and pass it in CellInput.substrate. See ` +
+      `benchmarks/harness/src/substrate.ts.`,
+    );
+  }
+}
+
+// ── Contradiction-check prompt ────────────────────────────────────────────────
+
+/** System prompt for the lightweight contradiction-check call (step 3).
+ *  Instructs the model to return a structured one-line verdict — keeps
+ *  post-processing trivial. */
+const SYSTEM_CONTRADICTION_CHECK =
+  'You are a consistency checker. You are given a list of prior prediction ' +
+  'statements and a list of retrieved memory excerpts. ' +
+  'Respond with EXACTLY one line in the format: ' +
+  'CONFLICT: <short description> OR NO_CONFLICT — no other text. ' +
+  'A conflict exists only when a retrieved excerpt directly contradicts ' +
+  'a specific factual claim in a prior prediction (same entity, incompatible values). ' +
+  'Superficial overlap or topic similarity is NOT a conflict.';
+
+function buildContradictionCheckPrompt(
+  priorPFrames: readonly MemoryFrame[],
+  retrievedResults: readonly SearchResult[],
+): string {
+  const priors = priorPFrames
+    .map((f, i) => `[prior_${i + 1}] ${f.content}`)
+    .join('\n');
+  const retrieved = retrievedResults
+    .slice(0, 10)  // limit to first 10 retrieved frames to keep the prompt short
+    .map((r, i) => `[retrieved_${i + 1}] ${r.frame.content}`)
+    .join('\n');
+  return (
+    '## Prior predictions\n' + priors +
+    '\n\n## Retrieved memories\n' + retrieved +
+    '\n\nDo any retrieved memories directly contradict any prior prediction? ' +
+    'Respond with CONFLICT: <description> or NO_CONFLICT.'
+  );
+}
+
+// ── hive_mind_ipb cell ────────────────────────────────────────────────────────
+
+/**
+ * `hive_mind_ipb` — retrieval cell extended with I/P/B frame writes.
+ *
+ * Step-by-step (see module-level doc for full rationale):
+ *
+ *   1. Assert substrate present.
+ *   2. Write a P-frame: "Retrieving to answer: <question>".
+ *   3. Run HybridSearch (top-K=20, gopId-scoped).
+ *   4. Contradiction check (if prior P-frames exist):
+ *        - Single LLM call with SYSTEM_CONTRADICTION_CHECK.
+ *        - If response starts with "CONFLICT:", write a B-frame.
+ *   5. Build answer prompt (`# Recalled Memories` block + question).
+ *   6. LLM answer call.
+ *   7. Write I-frame: "Q: <question> A: <answer>".
+ *   8. Return `LlmCallResult` (tokens and cost aggregated across all LLM calls).
+ */
+export const hiveMindIpbCell: CellFn = async ({
+  instance,
+  model,
+  llm,
+  turnId: _turnId,
+  substrate,
+  retrievalTopK,
+}: CellInput): Promise<LlmCallResult> => {
+  assertSubstrate('hive_mind_ipb', substrate);
+
+  const gopId = instance.conversation_id;
+  const limit = retrievalTopK ?? 20;
+  const searchOpts: { limit: number; gopId?: string } = { limit };
+  if (gopId) searchOpts.gopId = gopId;
+
+  const started = Date.now();
+  let totalInputTokens = 0;
+  let totalOutputTokens = 0;
+  let totalCostUsd = 0;
+
+  // ── Step 1: Write P-frame (query intent) ─────────────────────────────────
+  // Source 'agent_inferred' — this is a predicted/planned retrieval, not
+  // imported data. The P-frame enriches future retrievals: other questions
+  // in the same conversation will see this frame when their retrieval
+  // scans the gopId.
+  //
+  // `createPFrame` requires a `baseFrameId` (the I-frame this P-frame
+  // predicts an update to). We use the latest I-frame for this gopId as
+  // the base; if none exists yet (very first ingest of this conversation),
+  // we fall back to `createIFrame` with source='agent_inferred' so the
+  // frame still lands in the substrate.
+  let pFrame: MemoryFrame;
+  const latestI = substrate.frames.getLatestIFrame(gopId ?? '_global');
+  if (latestI) {
+    pFrame = substrate.frames.createPFrame(
+      gopId ?? '_global',
+      `Retrieving to answer: ${instance.question}`,
+      latestI.id,
+      'normal',
+      'agent_inferred',
+    );
+  } else {
+    // No I-frame yet — use createIFrame as a fallback so the P-frame intent
+    // still lands in the substrate. source='agent_inferred' marks it as a
+    // predicted frame even though the frame_type will be 'I'.
+    pFrame = substrate.frames.createIFrame(
+      gopId ?? '_global',
+      `Retrieving to answer: ${instance.question}`,
+      'normal',
+      'agent_inferred',
+    );
+  }
+
+  // ── Step 2: HybridSearch retrieval ───────────────────────────────────────
+  const results = await substrate.search.search(instance.question, searchOpts);
+
+  // ── Step 3: Contradiction check ──────────────────────────────────────────
+  // Only fires when ≥1 prior P-frame exists for this gopId. That means: skip
+  // the check on the very first question in a conversation (the P-frame we
+  // just wrote is the only P-frame; checking for conflicts with itself would
+  // be noise). We look for P-frames that predate the one we just created.
+  let bFrame: MemoryFrame | null = null;
+  if (gopId) {
+    // Fetch all P-type frames for this conversation that existed BEFORE
+    // the one we just wrote (t < pFrame.t). getPFramesSinceLastI returns
+    // P-frames since the latest I-frame; we filter to those older than pFrame.
+    const allGopFrames = substrate.frames.getGopFrames(gopId);
+    const priorPFrames = allGopFrames.filter(
+      f => f.frame_type === 'P' && f.id !== pFrame.id && f.t < pFrame.t,
+    );
+
+    if (priorPFrames.length > 0 && results.length > 0) {
+      const checkPrompt = buildContradictionCheckPrompt(priorPFrames, results);
+      let checkResult: LlmCallResult;
+      try {
+        checkResult = await llm.call({
+          model,
+          systemPrompt: SYSTEM_CONTRADICTION_CHECK,
+          userPrompt: checkPrompt,
+        });
+        totalInputTokens += checkResult.inputTokens;
+        totalOutputTokens += checkResult.outputTokens;
+        totalCostUsd += checkResult.costUsd;
+
+        const verdict = checkResult.text.trim();
+        if (verdict.startsWith('CONFLICT:')) {
+          // Write B-frame: references the P-frame we wrote (base) and the
+          // retrieved frame ids that triggered the conflict.
+          const conflictDescription = verdict.slice('CONFLICT:'.length).trim();
+          const referencedIds = results.slice(0, 5).map(r => r.frame.id);
+          bFrame = substrate.frames.createBFrame(
+            gopId,
+            conflictDescription,
+            pFrame.id,
+            referencedIds,
+          );
+        }
+      } catch {
+        // Contradiction check is best-effort — a failure here must not abort
+        // the answer generation. Swallow silently (consistent with the agentic
+        // cell's error-swallow pattern for non-fatal sub-steps).
+      }
+    }
+  }
+  // bFrame is written to substrate; no further action required unless callers
+  // want to surface it. Suppressing unused-variable warning:
+  void bFrame;
+
+  // ── Step 4: Build answer prompt and call LLM ─────────────────────────────
+  const memoryBlock = formatRecalledMemories(results);
+  const userPrompt = `${memoryBlock}\n\nQuestion: ${instance.question}`;
+
+  const answerResult = await llm.call({
+    model,
+    systemPrompt: buildSystemPrompt(FACTOID_BASELINE_PERSONA),
+    userPrompt,
+  });
+  totalInputTokens += answerResult.inputTokens;
+  totalOutputTokens += answerResult.outputTokens;
+  totalCostUsd += answerResult.costUsd;
+  const answer = answerResult.text;
+
+  // ── Step 5: Write I-frame for this Q-A turn ───────────────────────────────
+  // Stores the question + answer so future questions in the same conversation
+  // can retrieve it. source='import' matches the bulk-ingest frames so
+  // retrieval ranking treats Q-A frames at parity with original turn frames.
+  substrate.frames.createIFrame(
+    gopId ?? '_global',
+    `Q: ${instance.question} A: ${answer}`,
+    'normal',
+    'import',
+  );
+
+  // ── Aggregate and return ──────────────────────────────────────────────────
+  const latencyMs = Date.now() - started;
+  return {
+    text: answer,
+    inputTokens: totalInputTokens,
+    outputTokens: totalOutputTokens,
+    latencyMs,
+    costUsd: totalCostUsd,
+    failureMode: answerResult.failureMode,
+  };
+};

benchmarks/harness/src/cells.ts

@@ -0,0 +1,504 @@
+/**
+ * Four + two cell implementations — the causal isolation grid.
+ *
+ * Each cell is a pure(-ish) function from a `CellInput` to an `LlmCallResult`.
+ * Cells differ only in HOW they assemble the model's context:
+ *
+ *   raw:           no memory injection, no prompt evolution.
+ *   filtered:      inject the instance's own context as "retrieved memory"
+ *                  (Sprint 9 scaffold proxy — retained for back-compat; new
+ *                  `retrieval` cell below is the real-substrate replacement).
+ *   compressed:    raw prompt wrapped in an evolved scaffold (Sprint 9 GEPA
+ *                  prompt-evolution proxy — retained for back-compat).
+ *   full-context:  memory + evolve (both Sprint 9 treatments).
+ *   retrieval:     real `@waggle/core::HybridSearch` recall → top-K turn
+ *                  frames → "# Recalled Memories" block → baseline system
+ *                  prompt. Sprint 12 Task 2.5 Stage 1.
+ *   agentic:       `@waggle/agent::agent-loop` with a `search_memory` tool
+ *                  allowlist + 3-turn cap. The agent decides for itself when
+ *                  to search, how to query, and when to stop. Sprint 12
+ *                  Task 2.5 Stage 1.
+ *
+ * Controls live in `controls.ts`.
+ */
+
+import type { DatasetInstance, ModelSpec, CellName, ControlName } from './types.js';
+import type { LlmClient, LlmCallResult } from './llm.js';
+import type { Substrate } from './substrate.js';
+import type { SearchResult } from '@waggle/core';
+import {
+  runAgentLoop,
+  selectShape,
+  type AgentLoopConfig,
+  type ToolDefinition,
+} from '@waggle/agent';
+
+/**
+ * Phase 2.3 (Option A) refactor — the two ChatGPT-audit-flagged
+ * Claude-shaped artefacts have been deleted from this file:
+ *   - the strict-extraction baseline + evolved system-prompt constants
+ *     (replaced by cell-local personas routed through Phase 1.2 prompt-shapes
+ *     via `systemPromptForCell` below)
+ *   - the synthetic-memory scaffold-proxy user-prompt builder (replaced by
+ *     `buildUserPromptRetrieved` with neutral framing)
+ *
+ * Cell semantics are preserved via cell-specific personas + cell-specific
+ * user-prompt builders. SYSTEM_AGENTIC and SYSTEM_AGENTIC_FORCED_FALLBACK
+ * (declared further below) remain on their pre-existing methodology lineage
+ * and are explicitly excluded from this refactor's scope.
+ *
+ * For the original audit-flagged literal strings, see the pre-refactor revision
+ * of this file at git tag/commit prior to commit 5699677.
+ */
+const FACTOID_BASELINE_PERSONA = 'short-answer factoid QA agent';
+const FACTOID_STRICT_PERSONA =
+  'short-answer factoid QA agent that extracts the exact answer span ' +
+  'from supplied context, or replies "unknown" when the context does ' +
+  'not support an answer';
+
+/**
+ * Build a model-aware system prompt via Phase 1.2 prompt-shapes. Falls back
+ * to generic-simple when the model alias is not registered.
+ */
+function systemPromptForCell(model: ModelSpec, persona: string): string {
+  const shape = selectShape(model.litellmModel);
+  return shape.systemPrompt({ persona, question: '', isMultiStep: false });
+}
+
+/**
+ * SYSTEM_AGENTIC — SOFTENED by PM 2026-04-24 (Stage 2-Retry Gate A ratification).
+ * Sprint 12 Task 2.5 Stage 2-Retry source of record. Supersedes the Stage 1
+ * Gate 1 text ratified in commit `c80a4a3`.
+ *
+ * Changes vs Stage 1 text (Stage 2 N=20 FAIL drove these):
+ *   - §1 protocol verb: MUST → SHOULD. Stage 2 showed the MUST-call-first-
+ *     turn rule was too rigid — 2/20 instances answered correctly from
+ *     general knowledge, hitting the 95% floor by one instance. Softening
+ *     lets the agent skip the tool on clearly-non-conversational factoids.
+ *   - §1 explicit exception: general-knowledge lookups where the answer
+ *     does not require conversation-specific context may skip the tool call.
+ *   - §3 phrasing: "directly contain" → "contain" (tolerate inference from
+ *     retrieved memories rather than requiring exact-span match).
+ *   - §5 cap description: dropped the "SHOULD finish in 2" prescriptive
+ *     language in favour of "use your turns wisely" — diagnostic data from
+ *     Stage 2 showed 14/20 finished in 2 organically, and the prescriptive
+ *     phrasing was not carrying behavioural weight.
+ *   - §6 fallback threshold: "do not contain the answer" → "after reasonable
+ *     search you believe the memory does not contain a supported answer"
+ *     (nominalized, gives the agent latitude before abstaining).
+ *   - §7 NEW: explicit tool-exhaustion fallback clause — if turn 3 arrives
+ *     without a clear answer, commit to a best-supported answer. Backs the
+ *     runtime-side forced-answer fallback (§1.4 in the brief).
+ *   - Closing paragraph: "content returned by search_memory … and general
+ *     knowledge" instead of "ONLY … search_memory tool" — matches §1
+ *     softening.
+ *
+ * Previous Stage 1 text archive: commit c80a4a3.
+ *
+ * PM gate reference: `PM-Waggle-OS/briefs/2026-04-24-cc-task25-stage2-retry-kickoff.md` §1.3.
+ */
+export const SYSTEM_AGENTIC = [
+  'You are a memory-grounded answering agent. Your job: answer a short',
+  'factoid question using content returned by the search_memory tool and',
+  'your reasoning over it.',
+  '',
+  'Protocol (you SHOULD follow):',
+  '1. First turn: call search_memory with a focused query derived from the',
+  '   question, UNLESS the question is a simple factual lookup you can',
+  '   answer with high confidence from general knowledge and the answer',
+  '   does not require conversation-specific context. When uncertain,',
+  '   prefer the search_memory call.',
+  '2. After the tool returns, read the retrieved memories carefully.',
+  '3. If the retrieved memories contain the answer, respond with the',
+  '   shortest possible answer span — no sentences, no hedging, no preamble.',
+  '4. If the retrieved memories are ambiguous or incomplete, you MAY call',
+  '   search_memory ONE more time with a refined query (different wording,',
+  '   different entity, different time window). Then answer.',
+  '5. You have a hard cap of 3 total turns. Use your turns wisely.',
+  '6. If after reasonable search you believe the memory does not contain a',
+  '   supported answer, reply with exactly: unknown',
+  '7. If turn 3 arrives without a clear answer, commit to your best',
+  '   supported answer span using the context you have gathered across',
+  '   search calls. Do NOT leave the response empty.',
+  '',
+  'Output format: plain answer span only. No JSON, no markdown, no',
+  'explanation. Never invent facts. Ground every factual claim in retrieved',
+  'context or clearly-established general knowledge.',
+].join('\n');
+
+/**
+ * SYSTEM_AGENTIC_FORCED_FALLBACK — Sprint 12 Task 2.5 Stage 2-Retry §1.4.
+ *
+ * The runtime fallback prompt fires when agent-loop exhausts `maxTurns`
+ * with empty `resp.content` but non-empty `toolsUsed`. The agentic cell
+ * wrapper calls the subject LLM directly (no tools, no agent-loop) with
+ * this system prompt and the accumulated search_memory tool results in
+ * the user message. Prevents the Stage 2 "2/20 empty-answer" tail.
+ */
+export const SYSTEM_AGENTIC_FORCED_FALLBACK =
+  'You must commit to your best supported answer span or reply `unknown`. ' +
+  'Do not call tools. Use only the retrieved memory context below. ' +
+  'Respond with ONLY the answer span — no sentences, no hedging, no preamble.';
+
+export interface CellInput {
+  instance: DatasetInstance;
+  model: ModelSpec;
+  llm: LlmClient;
+  turnId: string;
+  /** Memory substrate — required by `retrieval` + `agentic` cells, ignored
+   *  by the other four. A clear error is thrown when a substrate-requiring
+   *  cell fires without one so the misconfiguration is loud. */
+  substrate?: Substrate;
+  /** LiteLLM routing used by the agentic cell's inner agent-loop. Ignored by
+   *  every other cell (they route through `llm: LlmClient`). */
+  litellm?: { url: string; apiKey: string };
+  /** Retrieval top-K. Default 10 per Stage 1 GATE-S0 decision. */
+  retrievalTopK?: number;
+  /** Agentic hard turn cap. Default 3 per Stage 1 GATE-S0 decision. */
+  agenticMaxTurns?: number;
+  /** Agentic AbortController timeout in ms. Default 180_000 (matches the
+   *  LiteLLM client's thinking=on timeout). */
+  agenticTimeoutMs?: number;
+  /** Testability hook — injects a mock `runAgentLoop` impl. Unit tests pass
+   *  an in-memory stub; production runs leave undefined and use the real
+   *  `@waggle/agent::runAgentLoop`. */
+  runAgentLoopFn?: typeof runAgentLoop;
+}
+
+export type CellFn = (input: CellInput) => Promise<LlmCallResult>;
+
+function buildUserPromptRaw(instance: DatasetInstance): string {
+  return `Context: ${instance.context}\n\nQuestion: ${instance.question}`;
+}
+
+/**
+ * Phase 2.3: replaces the deleted scaffold-proxy user-prompt builder. Same
+ * semantic — context framed AS retrieved memory — with neutral phrasing.
+ * The retrieval cell still uses `formatRecalledMemories` (which produces
+ * real frame-metadata-tagged output from HybridSearch results, not the
+ * deleted scaffold proxy).
+ */
+function buildUserPromptRetrieved(instance: DatasetInstance): string {
+  return (
+    'Retrieved context (from session memory):\n' +
+    `${instance.context}\n\n` +
+    `Question: ${instance.question}`
+  );
+}
+
+/** Format a `HybridSearch.search()` result list into the "# Recalled
+ *  Memories" block the real Waggle orchestrator emits. The retrieval cell's
+ *  causal contract is: "this is what HybridSearch would return at inference
+ *  time, in the shape the agent would see." */
+function formatRecalledMemories(results: readonly SearchResult[]): string {
+  if (results.length === 0) {
+    return '# Recalled Memories\n(none)';
+  }
+  const lines = results.map((r, idx) => {
+    const score = r.finalScore.toFixed(3);
+    const source = r.frame.source ?? 'user_stated';
+    return `- [memory:${r.frame.gop_id}:${r.frame.id} score=${score} src=${source}] ${r.frame.content}`;
+  });
+  return `# Recalled Memories\n${lines.join('\n')}`;
+}
+
+/** Build a `search_memory`-only `ToolDefinition` bound to the substrate's
+ *  HybridSearch instance. The tool returns a plain-text memory block the
+ *  LLM can parse inline — matches the shape the real Waggle orchestrator
+ *  emits for `search_memory` calls.
+ *
+ *  Sprint 12 Task 2.5 Stage 2-Retry §1.2: when `boundToGopId` is provided,
+ *  every tool invocation scopes its underlying `HybridSearch.search` call
+ *  to that `gopId` (conversation). This matches the LoCoMo QA-pair locality
+ *  — relevant evidence for a question lives inside its own conversation.
+ *  The agent CANNOT override the binding at call time (the tool does not
+ *  expose a `gopId` param); this is intentional — per-conversation scope
+ *  is a benchmark invariant, not an agent decision.
+ *
+ *  Default `defaultLimit` bumped from Stage 1's 10 → 20 per Stage 2-Retry
+ *  brief §1.2 tail ("Top-K moves from 10 to 20"). The upper clamp also
+ *  moves from 20 to 50 so agents can request wider recall when genuinely
+ *  needed without hitting a surprise cap.
+ */
+export function makeSearchMemoryTool(
+  substrate: Substrate,
+  defaultLimit: number = 20,
+  boundToGopId?: string,
+): ToolDefinition {
+  return {
+    name: 'search_memory',
+    description:
+      'Search the conversation memory corpus for turns relevant to a query. ' +
+      'Call this BEFORE answering so you can ground your answer in retrieved content. ' +
+      'Results are ranked turn frames with speaker, text, and relevance score.' +
+      (boundToGopId
+        ? ' (Scope is auto-restricted to the current conversation.)'
+        : ''),
+    offlineCapable: true,
+    parameters: {
+      type: 'object',
+      properties: {
+        query: {
+          type: 'string',
+          description: 'Natural-language search query. Focus on entities + topic.',
+        },
+        limit: {
+          type: 'number',
+          description: `Max results to return. Default ${defaultLimit}. Cap 50.`,
+        },
+      },
+      required: ['query'],
+    },
+    execute: async (args: Record<string, unknown>): Promise<string> => {
+      const query = typeof args.query === 'string' ? args.query.trim() : '';
+      if (!query) return 'ERROR: query is required and must be a non-empty string.';
+      const limitRaw = typeof args.limit === 'number' ? args.limit : defaultLimit;
+      const limit = Math.max(1, Math.min(50, Math.floor(limitRaw)));
+      const searchOpts: { limit: number; gopId?: string } = { limit };
+      if (boundToGopId) searchOpts.gopId = boundToGopId;
+      const results = await substrate.search.search(query, searchOpts);
+      if (results.length === 0) return '(no memories found)';
+      return results.map((r, idx) => {
+        const score = r.finalScore.toFixed(3);
+        return `[${idx + 1}] (${r.frame.gop_id}:${r.frame.id} score ${score}) ${r.frame.content}`;
+      }).join('\n');
+    },
+  };
+}
+
+function assertSubstrate(cellName: string, substrate: Substrate | undefined): asserts substrate is Substrate {
+  if (!substrate) {
+    throw new Error(
+      `cells.${cellName} requires a Substrate dependency. Construct it via ` +
+      `createSubstrate({embedder}) and pass it in CellInput.substrate. See ` +
+      `benchmarks/harness/src/substrate.ts.`,
+    );
+  }
+}
+
+function assertLitellm(
+  cellName: string,
+  litellm: CellInput['litellm'],
+): asserts litellm is { url: string; apiKey: string } {
+  if (!litellm?.url || !litellm?.apiKey) {
+    throw new Error(
+      `cells.${cellName} requires litellm={url, apiKey} in CellInput ` +
+      `(agent-loop talks to LiteLLM directly, not via the cell's LlmClient).`,
+    );
+  }
+}
+
+export const cells: Record<CellName, CellFn> = {
+  raw: async ({ instance, model, llm, turnId: _turnId }: CellInput) => {
+    return llm.call({
+      model,
+      systemPrompt: systemPromptForCell(model, FACTOID_BASELINE_PERSONA),
+      userPrompt: buildUserPromptRaw(instance),
+    });
+  },
+
+  filtered: async ({ instance, model, llm, turnId: _turnId }: CellInput) => {
+    return llm.call({
+      model,
+      systemPrompt: systemPromptForCell(model, FACTOID_BASELINE_PERSONA),
+      userPrompt: buildUserPromptRetrieved(instance),
+    });
+  },
+
+  compressed: async ({ instance, model, llm, turnId: _turnId }: CellInput) => {
+    return llm.call({
+      model,
+      systemPrompt: systemPromptForCell(model, FACTOID_STRICT_PERSONA),
+      userPrompt: buildUserPromptRaw(instance),
+    });
+  },
+
+  'full-context': async ({ instance, model, llm, turnId: _turnId }: CellInput) => {
+    return llm.call({
+      model,
+      systemPrompt: systemPromptForCell(model, FACTOID_STRICT_PERSONA),
+      userPrompt: buildUserPromptRetrieved(instance),
+    });
+  },
+
+  /**
+   * retrieval — Sprint 12 Task 2.5 Stage 1, updated by Stage 2-Retry.
+   *
+   * Real `@waggle/core::HybridSearch` RRF-fused FTS5 + vec0 recall. Stage
+   * 1 ran whole-corpus top-K=10. Stage 2-Retry §1.2 scopes search to the
+   * instance's conversation via `gopId` filter (plumbed through
+   * `HybridSearch.SearchOptions.gopId`) and bumps the top-K default to 20.
+   * Matches LoCoMo QA-pair locality.
+   *
+   * When `instance.conversation_id` is unset (synthetic datasets, pre-
+   * Stage-2-Retry fixtures), falls back to whole-corpus search — preserves
+   * backward compatibility with existing unit tests.
+   */
+  retrieval: async ({ instance, model, llm, turnId: _turnId, substrate, retrievalTopK }: CellInput) => {
+    assertSubstrate('retrieval', substrate);
+    const limit = retrievalTopK ?? 20;
+    const searchOpts: { limit: number; gopId?: string } = { limit };
+    if (instance.conversation_id) searchOpts.gopId = instance.conversation_id;
+    const results = await substrate.search.search(instance.question, searchOpts);
+    const memoryBlock = formatRecalledMemories(results);
+    const userPrompt = `${memoryBlock}\n\nQuestion: ${instance.question}`;
+    return llm.call({
+      model,
+      systemPrompt: systemPromptForCell(model, FACTOID_BASELINE_PERSONA),
+      userPrompt,
+    });
+  },
+
+  /**
+   * agentic — Sprint 12 Task 2.5 Stage 1 (2026-04-23).
+   *
+   * Inner `runAgentLoop` call with:
+   *   - tools: [search_memory] — single-tool allowlist per GATE-S0 decision.
+   *   - maxTurns: 3 — hard cap per GATE-S0 decision.
+   *   - signal: AbortController → setTimeout(timeoutMs).
+   *
+   * Returns an `LlmCallResult` whose `text` is the agent's final answer,
+   * `usage` tokens come from the agent loop's aggregate, and `costUsd` is
+   * computed from model pricing × tokens. `failureMode` is set when the
+   * agent aborts or throws; null on clean completion.
+   *
+   * The agent loop talks to LiteLLM directly — it does NOT go through the
+   * cell's `LlmClient`. Callers must supply `litellm={url, apiKey}` in
+   * CellInput. (The cell's `llm: LlmClient` is kept in the signature for
+   * interface symmetry but is not used by this cell.)
+   */
+  agentic: async ({ instance, model, llm, turnId, substrate, litellm, agenticMaxTurns, agenticTimeoutMs, runAgentLoopFn }: CellInput) => {
+    assertSubstrate('agentic', substrate);
+    assertLitellm('agentic', litellm);
+    const maxTurns = agenticMaxTurns ?? 3;
+    const timeoutMs = agenticTimeoutMs ?? 180_000;
+    const runFn = runAgentLoopFn ?? runAgentLoop;
+
+    // Stage 2-Retry §1.2: bind search_memory to this instance's conversation
+    // via gopId, so agent's search calls are automatically scoped. Cannot be
+    // overridden by the agent — conversation scope is a benchmark invariant.
+    const searchMemoryTool = makeSearchMemoryTool(substrate, 20, instance.conversation_id);
+
+    // Stage 2-Retry §1.4: capture tool-result text as it streams through so
+    // the exhaustion-fallback pass has the accumulated context available.
+    // Each entry is the string returned by makeSearchMemoryTool.execute.
+    const capturedToolResults: string[] = [];
+    const captureToolResult = (name: string, _input: Record<string, unknown>, result: string): void => {
+      if (name === 'search_memory') capturedToolResults.push(result);
+    };
+
+    const controller = new AbortController();
+    const timer = setTimeout(() => controller.abort(), timeoutMs);
+    const started = Date.now();
+
+    const cfg: AgentLoopConfig = {
+      litellmUrl: litellm.url,
+      litellmApiKey: litellm.apiKey,
+      model: model.litellmModel,
+      systemPrompt: SYSTEM_AGENTIC,
+      tools: [searchMemoryTool],
+      messages: [{ role: 'user', content: instance.question }],
+      maxTurns,
+      signal: controller.signal,
+      turnId,
+      onToolResult: captureToolResult,
+    };
+
+    try {
+      const resp = await runFn(cfg);
+      let finalContent = resp.content;
+      let fallbackInput = 0;
+      let fallbackOutput = 0;
+
+      // Stage 2-Retry §1.4 tool-exhaustion fallback: agent exhausted maxTurns
+      // without producing content but DID use tools → synthesize one forced-
+      // answer call with accumulated search context + SYSTEM_AGENTIC_FORCED_
+      // FALLBACK. SYSTEM_AGENTIC §7 addresses this prompt-side; this is the
+      // runtime guarantee. Fires only on empty-content-with-tool-use; normal
+      // empty-abstain ("unknown") paths don't trigger.
+      if (!finalContent.trim() && capturedToolResults.length > 0) {
+        const ctxBlock = capturedToolResults
+          .map((r, i) => `## search_memory call ${i + 1}\n${r}`)
+          .join('\n\n');
+        const forcedResp = await llm.call({
+          model,
+          systemPrompt: SYSTEM_AGENTIC_FORCED_FALLBACK,
+          userPrompt: `Question: ${instance.question}\n\n# Retrieved memory context\n${ctxBlock}`,
+        });
+        finalContent = forcedResp.text;
+        fallbackInput = forcedResp.inputTokens;
+        fallbackOutput = forcedResp.outputTokens;
+      }
+
+      const latencyMs = Date.now() - started;
+      const inputTokens = resp.usage.inputTokens + fallbackInput;
+      const outputTokens = resp.usage.outputTokens + fallbackOutput;
+      const costUsd =
+        (inputTokens / 1_000_000) * model.pricePerMillionInput +
+        (outputTokens / 1_000_000) * model.pricePerMillionOutput;
+      return {
+        text: finalContent,
+        inputTokens,
+        outputTokens,
+        latencyMs,
+        costUsd,
+        failureMode: null,
+      };
+    } catch (err: unknown) {
+      const latencyMs = Date.now() - started;
+      const name = err instanceof Error ? err.name : 'unknown';
+      const failureMode = name === 'AbortError' ? 'timeout' : `agentic_error_${name}`;
+      return {
+        text: '',
+        inputTokens: 0,
+        outputTokens: 0,
+        latencyMs,
+        costUsd: 0,
+        failureMode,
+      };
+    } finally {
+      clearTimeout(timer);
+    }
+  },
+
+  /**
+   * no-context — Sprint 12 Task 2.5 Stage 2-Retry §1.1 (2026-04-24).
+   *
+   * True zero-memory baseline: question-only user prompt, no
+   * `instance.context`, no retrieval, no memory injection. The factoid-
+   * baseline persona (via `systemPromptForCell`) keeps the model's output
+   * format consistent with raw / filtered / retrieval cells. This is
+   * the honest comparator for the retrieval memory-lift success criterion
+   * (brief §4 criterion 2): `retrieval >= no-context + 5pp`.
+   *
+   * Rationale (per Stage 2 N=20 FAIL exit §6.1): Sprint 9 `raw` embeds
+   * LoCoMo's oracle-selected `instance.context` in its prompt, so `raw` is
+   * NOT a zero-memory baseline on LoCoMo — it's an oracle-fed diagnostic
+   * (now exposed as v3 `oracle-context` alias). `no-context` is the
+   * zero-memory ground truth.
+   */
+  'no-context': async ({ instance, model, llm, turnId: _turnId }: CellInput) => {
+    return llm.call({
+      model,
+      systemPrompt: systemPromptForCell(model, FACTOID_BASELINE_PERSONA),
+      userPrompt: `Question: ${instance.question}`,
+    });
+  },
+};
+
+/** Type-narrowing helper for the runner's cell-or-control dispatch. */
+export function isCellName(name: string): name is CellName {
+  return (
+    name === 'raw' ||
+    name === 'filtered' ||
+    name === 'compressed' ||
+    name === 'full-context' ||
+    name === 'retrieval' ||
+    name === 'agentic' ||
+    name === 'no-context'
+  );
+}
+
+export function isControlName(name: string): name is ControlName {
+  return name === 'verbose-fixed';
+}

benchmarks/harness/src/controls.ts

@@ -0,0 +1,38 @@
+/**
+ * Control runs — sanity checks that the harness is not broken.
+ *
+ * Day 1 control: `verbose-fixed`. A prompt that deliberately tells the model
+ * to answer in long form. On a short-factoid accuracy metric (substring
+ * match), this should UNDERPERFORM the `raw` cell. If verbose-fixed scores
+ * equal-to or better than raw on the synthetic or real LoCoMo dataset, the
+ * harness scoring is suspect and must be audited before any scored run.
+ *
+ * Controls are intentionally NOT in the cells grid — they're diagnostic,
+ * not ablation data.
+ */
+
+import type { DatasetInstance, ModelSpec, ControlName } from './types.js';
+import type { LlmClient, LlmCallResult } from './llm.js';
+
+const SYSTEM_VERBOSE_FIXED =
+  'You are a careful assistant. Think step by step. Explain your reasoning in full sentences. ' +
+  'Provide context for your answer. Do not give short or terse responses.';
+
+export interface ControlInput {
+  instance: DatasetInstance;
+  model: ModelSpec;
+  llm: LlmClient;
+  turnId: string;
+}
+
+export type ControlFn = (input: ControlInput) => Promise<LlmCallResult>;
+
+export const controls: Record<ControlName, ControlFn> = {
+  'verbose-fixed': async ({ instance, model, llm, turnId: _turnId }: ControlInput) => {
+    return llm.call({
+      model,
+      systemPrompt: SYSTEM_VERBOSE_FIXED,
+      userPrompt: `Context: ${instance.context}\n\nQuestion: ${instance.question}`,
+    });
+  },
+};

benchmarks/harness/src/datasets.ts

@@ -0,0 +1,237 @@
+/**
+ * Dataset loader + version hash + opt-in synthetic fallback.
+ *
+ * Canonical archives live under `benchmarks/data/<dataset>/`:
+ *   locomo/locomo-1540.jsonl  (built by scripts/build-locomo-canonical.ts)
+ *
+ * Production path: if the canonical archive is absent, the loader throws
+ * `DatasetMissingError`. No silent fallback — absent data used to masquerade
+ * as a 60-instance synthetic run, which is the substrate gap Sprint 12 Task 1
+ * Blocker #1 eliminates.
+ *
+ * Development convenience: `BENCH_SYNTHETIC_DATASET=1` re-enables the
+ * synthetic fallback with a prominent console.warn. Never use that path for
+ * publishable runs — its output is scaffold-only.
+ *
+ * Every external dataset carries a `dataset_version` hash (SHA-256 of the
+ * archive bytes). The hash is the audit anchor A3 LOCK §H-AUDIT-2 requires
+ * for pre-registration-conformant benchmark runs. The runner attaches the
+ * hash to every emitted JSONL record (types.ts §dataset_version).
+ */
+
+import crypto from 'node:crypto';
+import fs from 'node:fs';
+import path from 'node:path';
+import type { DatasetInstance, DatasetSpec } from './types.js';
+
+/** Version string for the built-in synthetic scaffold. Static because the
+ *  instances are hard-coded in this file — any change will bump the source
+ *  revision and therefore the git SHA, so a user looking for drift has a
+ *  single obvious needle. */
+export const SYNTHETIC_DATASET_VERSION = 'synthetic-scaffold-v1';
+
+/** Thrown when a non-synthetic dataset archive is absent from the expected
+ *  path and the `BENCH_SYNTHETIC_DATASET` escape hatch is not set. */
+export class DatasetMissingError extends Error {
+  constructor(
+    public readonly resolvedPath: string,
+    public readonly datasetId: string,
+  ) {
+    super(
+      `Dataset '${datasetId}' canonical archive missing at ${resolvedPath}. ` +
+      `Build it via \`npx tsx benchmarks/harness/scripts/build-locomo-canonical.ts\` ` +
+      `or set BENCH_SYNTHETIC_DATASET=1 for the synthetic dev fallback ` +
+      `(scaffold-only — do NOT use for publishable runs).`,
+    );
+    this.name = 'DatasetMissingError';
+  }
+}
+
+/** SHA-256 hex of the dataset archive bytes, or the static version string
+ *  for synthetic. Throws `DatasetMissingError` when the archive is absent
+ *  and the env escape hatch is not set. */
+export function getDatasetVersion(spec: DatasetSpec, dataRoot: string): string {
+  if (spec.source === 'synthetic') return SYNTHETIC_DATASET_VERSION;
+  const resolved = path.resolve(dataRoot, spec.dataPath);
+  if (!fs.existsSync(resolved)) {
+    if (process.env.BENCH_SYNTHETIC_DATASET === '1') {
+      return SYNTHETIC_DATASET_VERSION;
+    }
+    throw new DatasetMissingError(resolved, spec.id);
+  }
+  const buf = fs.readFileSync(resolved);
+  return crypto.createHash('sha256').update(buf).digest('hex');
+}
+
+/** Preflight sample-lock schema written by scripts/build-preflight-samples.ts. */
+export interface PreflightSampleInstance {
+  id: string;
+  category: 'single-hop' | 'multi-hop' | 'temporal' | 'open-ended';
+  context: string;
+  question: string;
+  ground_truth_answer: string;
+  locomo_metadata?: unknown;
+}
+
+export interface PreflightSampleFile {
+  _meta?: {
+    distribution?: Record<string, number>;
+    seed?: number;
+    [k: string]: unknown;
+  };
+  instances: PreflightSampleInstance[];
+}
+
+/** The 4-cell Stage 2 preflight gate requires exactly this distribution
+ *  per `decisions/2026-04-20-preflight-oq-resolutions-locked.md` §OQ-PF-1. */
+export const PREFLIGHT_LOCOMO_50_DISTRIBUTION = {
+  'single-hop': 13,
+  'multi-hop': 13,
+  'temporal': 12,
+  'open-ended': 12,
+} as const;
+
+function distributionOf(instances: PreflightSampleInstance[]): Record<string, number> {
+  const out: Record<string, number> = {};
+  for (const i of instances) out[i.category] = (out[i.category] ?? 0) + 1;
+  return out;
+}
+
+/** Loads a committed sample-lock JSON and asserts the 13/13/12/12 distribution.
+ *
+ *  This is the enforcement point required by Task 1 of the CC preflight
+ *  sprint brief. Any deviation — whether from tampering, an incomplete
+ *  rebuild, or an accidental schema drift — must fail loudly so that no
+ *  Stage 2 run proceeds against a silently-broken sample. */
+export function loadPreflightSampleLock(lockPath: string): DatasetInstance[] {
+  if (!fs.existsSync(lockPath)) {
+    throw new Error(`Pre-flight sample lock not found at ${lockPath}`);
+  }
+  const raw = fs.readFileSync(lockPath, 'utf-8');
+  const parsed = JSON.parse(raw) as PreflightSampleFile;
+  if (!parsed || !Array.isArray(parsed.instances)) {
+    throw new Error(`Pre-flight sample lock at ${lockPath} is missing the "instances" array`);
+  }
+  const actual = distributionOf(parsed.instances);
+  const expected = PREFLIGHT_LOCOMO_50_DISTRIBUTION;
+  const keys: (keyof typeof expected)[] = ['single-hop', 'multi-hop', 'temporal', 'open-ended'];
+  const mismatch =
+    parsed.instances.length !== 50 ||
+    keys.some(k => (actual[k] ?? 0) !== expected[k]) ||
+    Object.keys(actual).some(k => !(k in expected));
+  if (mismatch) {
+    const actualStr = keys.map(k => `${k}=${actual[k] ?? 0}`).join('/');
+    const expectedStr = keys.map(k => `${k}=${expected[k]}`).join('/');
+    throw new Error(
+      `Pre-flight sample distribution mismatch: expected 13/13/12/12, got ${actualStr} ` +
+      `(expected breakdown: ${expectedStr}; total ${parsed.instances.length}, expected 50)`,
+    );
+  }
+  return parsed.instances.map(inst => {
+    // Stage 2-Retry §1.2: LoCoMo instance_id format is
+    // `locomo_<conversation-id>_q<index>`. Derive conversation_id from that
+    // pattern so preflight-lock instances (which don't carry the field
+    // directly) still scope correctly. Safe because the preflight builder
+    // already enforces LoCoMo-only rows in the lock file.
+    const convMatch = inst.id.match(/^locomo_(conv-\d+)_q\d+$/);
+    return {
+      instance_id: inst.id,
+      question: inst.question,
+      context: inst.context,
+      expected: [inst.ground_truth_answer],
+      ...(convMatch ? { conversation_id: convMatch[1] } : {}),
+    };
+  });
+}
+
+/**
+ * Built-in 60-instance synthetic dataset — enough for the `--limit 50`
+ * verbose-fixed acceptance test plus 10 slack. Questions probe short-context
+ * recall, entity tracking, and one-hop reasoning so the scaffold exercises
+ * a realistic-ish prompt shape.
+ */
+const SYNTHETIC_INSTANCES: DatasetInstance[] = Array.from({ length: 60 }, (_, i) => {
+  const n = i + 1;
+  const topics = [
+    { subj: 'Marko', verb: 'works at', obj: 'Egzakta Advisory', q: 'Where does Marko work?', a: 'Egzakta Advisory' },
+    { subj: 'Ana', verb: 'leads', obj: 'the KVARK platform', q: 'Who leads the KVARK platform?', a: 'Ana' },
+    { subj: 'The Waggle release', verb: 'shipped on', obj: '2026-04-20', q: 'When did Waggle ship?', a: '2026-04-20' },
+    { subj: 'The benchmark', verb: 'uses model', obj: 'Qwen3.6-35B-A3B', q: 'Which model does the benchmark use?', a: 'Qwen3.6-35B-A3B' },
+    { subj: 'The harness', verb: 'runs', obj: 'four cells', q: 'How many cells does the harness run?', a: 'four' },
+  ];
+  const t = topics[i % topics.length];
+  return {
+    instance_id: `synthetic_${String(n).padStart(3, '0')}`,
+    question: t.q,
+    context: `${t.subj} ${t.verb} ${t.obj}.`,
+    expected: [t.a],
+  };
+});
+
+export function loadDataset(spec: DatasetSpec, dataRoot: string): DatasetInstance[] {
+  if (spec.source === 'synthetic') {
+    return SYNTHETIC_INSTANCES;
+  }
+
+  // External (LoCoMo / LongMemEval). Sprint 12 Task 1 Blocker #1: no silent
+  // fallback. Missing archive throws `DatasetMissingError`, unless the
+  // `BENCH_SYNTHETIC_DATASET=1` escape hatch is set (dev convenience only —
+  // never use for publishable runs).
+  const resolved = path.resolve(dataRoot, spec.dataPath);
+  if (!fs.existsSync(resolved)) {
+    if (process.env.BENCH_SYNTHETIC_DATASET === '1') {
+      console.warn(
+        `[harness] ${spec.id} archive missing at ${resolved} — ` +
+        `BENCH_SYNTHETIC_DATASET=1 set, falling back to synthetic scaffold. ` +
+        `Dev-only path; do NOT use for publishable runs.`,
+      );
+      return SYNTHETIC_INSTANCES;
+    }
+    throw new DatasetMissingError(resolved, spec.id);
+  }
+
+  const raw = fs.readFileSync(resolved, 'utf-8');
+  const out: DatasetInstance[] = [];
+  for (const line of raw.split('\n')) {
+    const trimmed = line.trim();
+    if (!trimmed) continue;
+    try {
+      const parsed = JSON.parse(trimmed) as Partial<DatasetInstance> & { conversation_id?: string };
+      if (parsed.instance_id && parsed.question && parsed.expected) {
+        out.push({
+          instance_id: parsed.instance_id,
+          question: parsed.question,
+          context: parsed.context ?? '',
+          expected: Array.isArray(parsed.expected) ? parsed.expected : [String(parsed.expected)],
+          // Stage 2-Retry §1.2: preserve conversation_id so retrieval +
+          // agentic cells can scope HybridSearch to the instance's
+          // conversation via the existing `gopId` filter at search.ts:14.
+          ...(parsed.conversation_id ? { conversation_id: parsed.conversation_id } : {}),
+        });
+      }
+    } catch {
+      // Tolerate malformed lines (common in exported benchmark dumps) —
+      // skip + surface the count at the end.
+    }
+  }
+  return out;
+}
+
+/** Deterministic shuffle + sampling so `--seed N --limit M` is reproducible. */
+export function sampleInstances(all: DatasetInstance[], seed: number, limit: number): DatasetInstance[] {
+  if (!Number.isFinite(limit) || limit >= all.length) return all.slice();
+  // xorshift32 — cheap deterministic PRNG, good enough for sampling.
+  let state = (seed || 1) >>> 0;
+  const rand = (): number => {
+    state ^= state << 13;
+    state ^= state >>> 17;
+    state ^= state << 5;
+    return (state >>> 0) / 0x100000000;
+  };
+  const shuffled = all.slice();
+  for (let i = shuffled.length - 1; i > 0; i--) {
+    const j = Math.floor(rand() * (i + 1));
+    [shuffled[i], shuffled[j]] = [shuffled[j], shuffled[i]];
+  }
+  return shuffled.slice(0, limit);
+}

benchmarks/harness/src/failure-taxonomy/aggregate.ts

@@ -0,0 +1,98 @@
+/**
+ * Sprint 12 Task 1 Blocker #6 — failure distribution aggregator.
+ *
+ * Counts failure codes across a run's emitted rows, computes F_other rate,
+ * flags taxonomy-review triggers per A3 LOCK § 6 (strict >10% threshold),
+ * and captures up to 10 F_other rationales for manual PM review in the
+ * exit ping.
+ */
+
+import {
+  FAILURE_CODES,
+  F_OTHER_REVIEW_THRESHOLD,
+  type FailureCode,
+} from './codes.js';
+
+export interface FailureRow {
+  failure_code: FailureCode;
+  rationale?: string | null;
+}
+
+export interface FailureDistribution {
+  /** Counts keyed by code. `null` is the correct-verdict bucket. */
+  counts: Record<'null' | 'F1' | 'F2' | 'F3' | 'F4' | 'F5' | 'F6' | 'F_other', number>;
+  /** Total rows counted (sum of `counts`). */
+  total: number;
+  /** F_other rate ∈ [0, 1]. Zero when total=0. */
+  f_other_rate: number;
+  /** True when f_other_rate > 10% (strict greater-than per A3 LOCK § 6). */
+  f_other_review_flag: boolean;
+  /** First 10 F_other rationales in input order, for PM manual inspection. */
+  f_other_rationales_sample: string[];
+}
+
+const MAX_F_OTHER_SAMPLE = 10;
+
+function emptyCounts(): FailureDistribution['counts'] {
+  return {
+    null: 0,
+    F1: 0,
+    F2: 0,
+    F3: 0,
+    F4: 0,
+    F5: 0,
+    F6: 0,
+    F_other: 0,
+  };
+}
+
+export function computeFailureDistribution(
+  rows: readonly FailureRow[],
+): FailureDistribution {
+  const counts = emptyCounts();
+  const f_other_rationales_sample: string[] = [];
+
+  for (const row of rows) {
+    const code = row.failure_code;
+    if (code === null) {
+      counts.null += 1;
+      continue;
+    }
+    if (!(FAILURE_CODES as readonly string[]).includes(code)) {
+      // Defensive skip — aggregate is lenient vs. validator. Unknown codes
+      // don't crash the report but also don't pollute the known-buckets.
+      continue;
+    }
+    counts[code] += 1;
+    if (code === 'F_other') {
+      if (
+        typeof row.rationale === 'string' &&
+        row.rationale.length > 0 &&
+        f_other_rationales_sample.length < MAX_F_OTHER_SAMPLE
+      ) {
+        f_other_rationales_sample.push(row.rationale);
+      }
+    }
+  }
+
+  const total =
+    counts.null +
+    counts.F1 +
+    counts.F2 +
+    counts.F3 +
+    counts.F4 +
+    counts.F5 +
+    counts.F6 +
+    counts.F_other;
+
+  const f_other_rate = total === 0 ? 0 : counts.F_other / total;
+  const f_other_review_flag = f_other_rate > F_OTHER_REVIEW_THRESHOLD;
+
+  return {
+    counts,
+    total,
+    f_other_rate,
+    f_other_review_flag,
+    f_other_rationales_sample,
+  };
+}

benchmarks/harness/src/failure-taxonomy/codes.ts

@@ -0,0 +1,52 @@
+/**
+ * Sprint 12 Task 1 Blocker #6 — failure taxonomy codes.
+ *
+ * A3 LOCK § 6 hybrid taxonomy: 6 categorical failure modes (F1–F6) +
+ * `null` (correct) + `F_other` escape category with mandatory rationale.
+ *
+ * Taxonomy version tag: `F1-F6+other v1` (surfaces into A3 LOCK § 7 field 14
+ * `failure_taxonomy_version` of the per-run manifest).
+ */
+
+/**
+ * 8-value failure code space:
+ *   - `null`      — correct verdict (no failure classification)
+ *   - `'F1'`..`'F6'` — LOCKED categorical failure modes per A3 LOCK § 6
+ *   - `'F_other'` — escape hatch; requires ≥10-word rationale per validator
+ */
+export type FailureCode = null | 'F1' | 'F2' | 'F3' | 'F4' | 'F5' | 'F6' | 'F_other';
+
+/**
+ * Ordered list of non-null failure codes. Used by aggregators and rubric
+ * renderers that need stable iteration order.
+ */
+export const FAILURE_CODES = ['F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F_other'] as const;
+
+/**
+ * Verbatim definitions per A3 LOCK § 6. Plain-English short form suitable
+ * for inclusion in the judge rubric prompt block and for per-run exit-ping
+ * narration. Do NOT paraphrase — any edit requires PM ratification and a
+ * taxonomy version bump (`F1-F6+other v2`).
+ */
+export const FAILURE_CODE_DEFINITIONS: Record<Exclude<FailureCode, null>, string> = {
+  F1: 'contradicts-ground-truth — Model output asserts a fact that directly contradicts the LoCoMo reference answer. Most severe failure class.',
+  F2: 'partial-answer — Model output contains correct information but is incomplete against the reference\'s required components.',
+  F3: 'off-topic — Model output is tangentially related or addresses a different question than asked.',
+  F4: 'refusal — Model declines to answer (safety response, capability disclaimer, "I don\'t know").',
+  F5: 'tool-use-error — Model attempted a tool call but the harness returned an error, a malformed response, or an infinite loop; applies only in cells where tool use is permitted.',
+  F6: 'format-violation — Model output is correct in content but violates the required output format (JSON schema mismatch, wrong key names, escape errors).',
+  F_other: 'F-other — Judge identifies a failure that does not fit F1–F6. Mandatory ≥10-word rationale explaining the failure.',
+};
+
+/**
+ * Taxonomy version tag per A3 LOCK § 6 / § 7 field 14. Emitted verbatim
+ * into the pre-registration manifest and any aggregate report.
+ */
+export const FAILURE_TAXONOMY_VERSION = 'F1-F6+other v1';
+
+/**
+ * Threshold at which `F_other` rate triggers a taxonomy-review flag per
+ * A3 LOCK § 6 ("F-other rate on any run > 10% triggers taxonomy review and
+ * potential v2 amendment"). Strict greater-than.
+ */
+export const F_OTHER_REVIEW_THRESHOLD = 0.10;

benchmarks/harness/src/failure-taxonomy/index.ts

@@ -0,0 +1,34 @@
+/**
+ * Sprint 12 Task 1 Blocker #6 — failure taxonomy module barrel.
+ *
+ * A3 LOCK § 6 surface consumed by:
+ *   - Task 2 judge rubric splicer (buildJudgeRubricBlock)
+ *   - Task 2 judge response parser (validateFailureCodeEntry)
+ *   - Aggregate JSON writer (computeFailureDistribution)
+ *   - Pre-registration manifest (FAILURE_TAXONOMY_VERSION field)
+ */
+
+export {
+  FAILURE_CODES,
+  FAILURE_CODE_DEFINITIONS,
+  FAILURE_TAXONOMY_VERSION,
+  F_OTHER_REVIEW_THRESHOLD,
+} from './codes.js';
+export type { FailureCode } from './codes.js';
+
+export { buildJudgeRubricBlock } from './rubric.js';
+
+export {
+  validateFailureCodeEntry,
+  F_OTHER_RATIONALE_MIN_TOKENS,
+} from './validator.js';
+export type {
+  FailureCodeEntryInput,
+  ValidationErrorCode,
+  ValidationFailure,
+  ValidationResult,
+  ValidationSuccess,
+} from './validator.js';
+
+export { computeFailureDistribution } from './aggregate.js';
+export type { FailureRow, FailureDistribution } from './aggregate.js';

benchmarks/harness/src/failure-taxonomy/rubric.ts

@@ -0,0 +1,47 @@
+/**
+ * Sprint 12 Task 1 Blocker #6 — judge rubric block builder.
+ *
+ * Deterministic multi-line string that embeds the A3 LOCK § 6 failure
+ * taxonomy verbatim into the judge prompt. Task 2 (Stage 2 mini C3
+ * execution) will splice this block into the judge system prompt; Session
+ * 3 only guarantees the block exists, renders deterministically, and
+ * carries the LOCKED taxonomy version tag.
+ *
+ * Determinism contract: identical output on every call. No parameters, no
+ * time / cwd / env dependency. Same bytes every invocation.
+ */
+
+import {
+  FAILURE_CODE_DEFINITIONS,
+  FAILURE_TAXONOMY_VERSION,
+} from './codes.js';
+
+/**
+ * Returns the A3 LOCK § 6 failure taxonomy rubric block for inclusion in
+ * the judge prompt. The block is appended to the judge system prompt by
+ * Task 2 runtime; Session 3 ships only the renderer.
+ *
+ * Structure:
+ *   - Header with taxonomy version tag
+ *   - One line per non-null failure code (F1–F6 + F_other)
+ *   - Trailing F-other escape-clause instruction matching A3 LOCK § 6
+ *
+ * The block MUST be deterministic — two successive calls produce
+ * byte-identical strings. Tests pin the presence of key sentinel phrases.
+ */
+export function buildJudgeRubricBlock(): string {
+  const lines: string[] = [];
+  lines.push(`Failure taxonomy (${FAILURE_TAXONOMY_VERSION}):`);
+  lines.push('');
+  lines.push(`F1 — ${FAILURE_CODE_DEFINITIONS.F1}`);
+  lines.push(`F2 — ${FAILURE_CODE_DEFINITIONS.F2}`);
+  lines.push(`F3 — ${FAILURE_CODE_DEFINITIONS.F3}`);
+  lines.push(`F4 — ${FAILURE_CODE_DEFINITIONS.F4}`);
+  lines.push(`F5 — ${FAILURE_CODE_DEFINITIONS.F5}`);
+  lines.push(`F6 — ${FAILURE_CODE_DEFINITIONS.F6}`);
+  lines.push('');
+  lines.push(
+    'If no category fits, select F-other and provide ≥10-word rationale explaining the failure.',
+  );
+  return lines.join('\n');
+}

benchmarks/harness/src/failure-taxonomy/validator.ts

@@ -0,0 +1,111 @@
+/**
+ * Sprint 12 Task 1 Blocker #6 — failure-code entry validator.
+ *
+ * Enforces A3 LOCK § 6 invariants on judge-emitted {failure_code, rationale}
+ * pairs:
+ *
+ *   - `failure_code === null`      → rationale must be null/undefined
+ *   - `failure_code` in F1..F6     → rationale optional, no length constraint
+ *   - `failure_code === 'F_other'` → rationale mandatory, ≥10 whitespace-
+ *                                     separated tokens, non-empty strings only
+ *
+ * Error codes surface the exact failure mode for downstream routing (Task 2
+ * judge-response parser will map these onto its own error class).
+ */
+
+import { FAILURE_CODES, type FailureCode } from './codes.js';
+
+export type ValidationErrorCode =
+  | 'invalid_failure_code'
+  | 'null_code_with_rationale'
+  | 'F_other_rationale_missing'
+  | 'F_other_rationale_too_short';
+
+export interface ValidationSuccess {
+  ok: true;
+}
+
+export interface ValidationFailure {
+  ok: false;
+  code: ValidationErrorCode;
+  message: string;
+}
+
+export type ValidationResult = ValidationSuccess | ValidationFailure;
+
+export interface FailureCodeEntryInput {
+  failure_code: FailureCode | string;
+  rationale?: string | null;
+}
+
+/** Minimum token count required for F_other rationale per A3 LOCK § 6. */
+export const F_OTHER_RATIONALE_MIN_TOKENS = 10;
+
+/** Tokenise on any whitespace run; drop empty strings. */
+function countRationaleTokens(rationale: string): number {
+  return rationale.split(/\s+/).filter(tok => tok.length > 0).length;
+}
+
+function isRecognisedCode(code: unknown): code is FailureCode {
+  if (code === null) return true;
+  if (typeof code !== 'string') return false;
+  return (FAILURE_CODES as readonly string[]).includes(code);
+}
+
+export function validateFailureCodeEntry(entry: FailureCodeEntryInput): ValidationResult {
+  const { failure_code, rationale } = entry;
+
+  if (!isRecognisedCode(failure_code)) {
+    return {
+      ok: false,
+      code: 'invalid_failure_code',
+      message: `failure_code must be null | F1..F6 | F_other; got ${JSON.stringify(failure_code)}`,
+    };
+  }
+
+  const rationaleProvided =
+    rationale !== undefined &&
+    rationale !== null &&
+    typeof rationale === 'string' &&
+    rationale.length > 0;
+
+  if (failure_code === null) {
+    if (rationaleProvided) {
+      return {
+        ok: false,
+        code: 'null_code_with_rationale',
+        message: 'failure_code=null (correct) must not carry a rationale',
+      };
+    }
+    return { ok: true };
+  }
+
+  if (failure_code === 'F_other') {
+    if (rationale === undefined || rationale === null) {
+      return {
+        ok: false,
+        code: 'F_other_rationale_missing',
+        message: 'F_other requires a non-null rationale string',
+      };
+    }
+    if (typeof rationale !== 'string' || rationale.trim().length === 0) {
+      return {
+        ok: false,
+        code: 'F_other_rationale_missing',
+        message: 'F_other rationale must be a non-empty string',
+      };
+    }
+    const tokens = countRationaleTokens(rationale);
+    if (tokens < F_OTHER_RATIONALE_MIN_TOKENS) {
+      return {
+        ok: false,
+        code: 'F_other_rationale_too_short',
+        message: `F_other rationale must have ≥${F_OTHER_RATIONALE_MIN_TOKENS} whitespace-separated tokens; got ${tokens}`,
+      };
+    }
+    return { ok: true };
+  }
+
+  // F1..F6: rationale is optional and has no length constraint.
+  return { ok: true };
+}

benchmarks/harness/src/health-check.ts

@@ -0,0 +1,140 @@
+/**
+ * Sprint 12 Task 2.5 Stage 1.5 §7.3 — pre-cell health check.
+ *
+ * Before the runner burns ~20 instance evaluations on a cell, verify every
+ * upstream route the run depends on is actually reachable. Catches:
+ *   - Judge API-key expiry (Anthropic/OpenAI/Gemini rotation)
+ *   - LiteLLM route rename (provider changed slug between runs)
+ *   - DashScope authentication drift
+ *   - LiteLLM proxy container down (Docker daemon crash since last run)
+ *
+ * Two levels of probe:
+ *   1. GET `/health/liveliness`    — is the proxy itself alive?
+ *   2. POST `/v1/chat/completions` with a 5-token "ping" payload for each
+ *      required model (subject + judge ensemble). Verifies the route is
+ *      wired AND the upstream provider is responsive.
+ *
+ * Any 5xx response or network error accumulates into `result.failures`.
+ * `ok` is true only when every probe returns 2xx. Caller (main) throws a
+ * clear error on `ok: false` so nothing starts against a broken upstream.
+ *
+ * `fetchFn` is injectable so tests can stub it without touching globals.
+ * `timeoutMs` defaults to 15 s per probe — thinking=on reasoning calls can
+ * push latency; shorter timeouts cause false negatives on slow judges.
+ */
+
+const DEFAULT_PROBE_TIMEOUT_MS = 15_000;
+
+export interface HealthCheckOptions {
+  litellmUrl: string;
+  litellmApiKey: string;
+  subjectModel: string;
+  judgeModels?: string[];
+  fetchFn?: typeof globalThis.fetch;
+  timeoutMs?: number;
+  /** When true (default), also GETs `/health/liveliness`. Some LiteLLM
+   *  configs don't expose that endpoint; set false to skip it. */
+  includeLivenessProbe?: boolean;
+}
+
+export interface HealthCheckFailure {
+  endpoint: string;
+  error: string;
+}
+
+export interface HealthCheckResult {
+  ok: boolean;
+  failures: HealthCheckFailure[];
+  probedAt: string; // ISO timestamp
+  durationMs: number;
+}
+
+/** Short message used in the ping body. Minimum tokens that still elicits a
+ *  response; any live model will say "Pong" or similar. */
+const PING_MESSAGE = 'Respond with just: pong';
+
+async function probeOnce(
+  label: string,
+  fn: () => Promise<Response>,
+  failures: HealthCheckFailure[],
+): Promise<void> {
+  try {
+    const res = await fn();
+    if (!res.ok) {
+      failures.push({ endpoint: label, error: `http_${res.status}` });
+    }
+  } catch (err: unknown) {
+    const name = err instanceof Error ? err.name : 'unknown';
+    failures.push({ endpoint: label, error: `fetch_error_${name}` });
+  }
+}
+
+export async function preCellHealthCheck(opts: HealthCheckOptions): Promise<HealthCheckResult> {
+  const fetchFn = opts.fetchFn ?? globalThis.fetch;
+  const timeoutMs = opts.timeoutMs ?? DEFAULT_PROBE_TIMEOUT_MS;
+  const includeLiveness = opts.includeLivenessProbe ?? true;
+  const failures: HealthCheckFailure[] = [];
+  const started = Date.now();
+  const baseUrl = opts.litellmUrl.replace(/\/$/, '');
+
+  // 1. Liveness probe (skipped when includeLivenessProbe=false).
+  if (includeLiveness) {
+    await probeOnce('GET /health/liveliness', async () => {
+      const ctrl = new AbortController();
+      const t = setTimeout(() => ctrl.abort(), timeoutMs);
+      try {
+        return await fetchFn(`${baseUrl}/health/liveliness`, {
+          method: 'GET',
+          headers: { Authorization: `Bearer ${opts.litellmApiKey}` },
+          signal: ctrl.signal,
+        });
+      } finally {
+        clearTimeout(t);
+      }
+    }, failures);
+  }
+
+  // 2. Model ping probes (subject + each judge).
+  //
+  // Opus 4.7 + some newer reasoning-model families reject the `temperature`
+  // param with HTTP 400 (`temperature is deprecated for this model`). Mirror
+  // the regex-based detection judge-client.ts:88 already ships so the ping
+  // payload survives across provider generations. Also bumps max_tokens to
+  // match the judge-client default (1024) — 5 was too tight for providers
+  // that burn tokens on reasoning before content, producing empty-body
+  // responses that aren't technically 5xx but also aren't useful.
+  const probeModels: string[] = [opts.subjectModel, ...(opts.judgeModels ?? [])];
+  for (const model of probeModels) {
+    await probeOnce(`POST /v1/chat/completions model=${model}`, async () => {
+      const ctrl = new AbortController();
+      const t = setTimeout(() => ctrl.abort(), timeoutMs);
+      try {
+        const rejectsTemperature = /opus-4-7|gpt-5|o3|o4/i.test(model);
+        const reqBody: Record<string, unknown> = {
+          model,
+          messages: [{ role: 'user', content: PING_MESSAGE }],
+          max_tokens: 1024,
+        };
+        if (!rejectsTemperature) reqBody.temperature = 0.0;
+        return await fetchFn(`${baseUrl}/v1/chat/completions`, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            Authorization: `Bearer ${opts.litellmApiKey}`,
+          },
+          signal: ctrl.signal,
+          body: JSON.stringify(reqBody),
+        });
+      } finally {
+        clearTimeout(t);
+      }
+    }, failures);
+  }
+
+  return {
+    ok: failures.length === 0,
+    failures,
+    probedAt: new Date().toISOString(),
+    durationMs: Date.now() - started,
+  };
+}

benchmarks/harness/src/ingest-beam.ts

@@ -0,0 +1,274 @@
+/**
+ * BEAM turn-level ingest — mirrors the LoCoMo ingest pattern in ingest.ts.
+ *
+ * Reads the BEAM canonical archive (`benchmarks/data/beam/beam.jsonl`, built
+ * by `build-beam-canonical.ts`) and produces an array of atomic turn frames,
+ * one per message in each unique conversation.
+ *
+ * BEAM canonical source shape per instance (DatasetInstance with extras):
+ *   {
+ *     instance_id: string,         // e.g. "beam_0_q3"
+ *     conversation_id: string,     // e.g. "beam_0"  (= `beam_${conversationIndex}`)
+ *     question: string,
+ *     expected: string[],
+ *     context: string,             // formatted full conversation text:
+ *                                  //   "user: ...\nassistant: ...\n..."
+ *     chat_size?: string,          // '128K' | '500K' | '1M' | '10M'
+ *   }
+ *
+ * The conversation turns are reconstructed by parsing the `context` field of
+ * the FIRST instance encountered for each unique `conversation_id`. All
+ * instances that share a `conversation_id` reference the same conversation, so
+ * only one parse per conversation is needed; subsequent instances are skipped.
+ *
+ * gopId mapping: `turn.gopId = instance.conversation_id` (e.g. `"beam_0"`).
+ * The retrieval and agentic cells use `instance.conversation_id` as their
+ * gopId filter — so turns stored under `gopId = conversation_id` align
+ * without any cell-level changes.
+ *
+ * GATE-S0 decision (frame-per-turn granularity) applies here too. See
+ * ingest.ts §GATE-S0.
+ */
+
+import fs from 'node:fs';
+import type { MindDB, HybridSearch, FrameStore, SessionStore } from '@waggle/core';
+
+// ── Public types ─────────────────────────────────────────────────────────────
+
+/** One BEAM conversation turn, ready to be written as a frame. */
+export interface BeamTurn {
+  /** Conversation identifier — becomes `memory_frames.gop_id`.
+   *  Format: `beam_${conversationIndex}`, matching the canonical builder. */
+  gopId: string;
+  /** Zero-based index of this message within the conversation. */
+  messageIndex: number;
+  role: 'user' | 'assistant';
+  content: string;
+  /** Formatted `"${role}: ${content}"` — the string that lands in
+   *  `memory_frames.content` and gets FTS5 / vec-indexed. */
+  formattedContent: string;
+  /** BEAM context-window size bucket this conversation belongs to.
+   *  Preserved from the source instance for observability; not used
+   *  by the ingest or retrieval logic. */
+  chatSize: string;
+}
+
+export interface IngestStats {
+  /** Number of frames successfully created (after dedup). */
+  count: number;
+  /** Wall-clock ms spent on `createIFrame` loop (includes FTS5 auto-index). */
+  ingestMs: number;
+  /** Wall-clock ms spent on `indexFramesBatch` (embedder + vec0 insert). */
+  indexMs: number;
+}
+
+// ── Internal raw-schema types ─────────────────────────────────────────────────
+
+interface BeamRawInstance {
+  instance_id?: string;
+  conversation_id?: string;
+  question?: string;
+  expected?: unknown;
+  context?: string;
+  chat_size?: string;
+}
+
+// ── Context parser ────────────────────────────────────────────────────────────
+
+/**
+ * Parse a BEAM `context` string back into individual (role, content) pairs.
+ *
+ * The canonical builder formats the conversation as:
+ *   "user: <text>\nassistant: <text>\nuser: <text>\n..."
+ *
+ * Lines that start with `"user: "` or `"assistant: "` begin a new turn;
+ * any subsequent lines that do NOT start with one of those prefixes are
+ * treated as continuation lines of the current turn (i.e. multi-line content
+ * is preserved). This matches the round-trip produced by:
+ *   `turns.map(t => \`${t.role}: ${t.content}\`).join('\n')`
+ *
+ * Returns an empty array when `context` is empty or cannot be parsed.
+ */
+function parseContextToTurns(context: string): Array<{ role: 'user' | 'assistant'; content: string }> {
+  if (!context || !context.trim()) return [];
+
+  const result: Array<{ role: 'user' | 'assistant'; content: string }> = [];
+  let currentRole: 'user' | 'assistant' | null = null;
+  const currentLines: string[] = [];
+
+  const flush = (): void => {
+    if (currentRole === null || currentLines.length === 0) return;
+    const content = currentLines.join('\n').trim();
+    if (content) result.push({ role: currentRole, content });
+    currentLines.length = 0;
+    currentRole = null;
+  };
+
+  for (const line of context.split('\n')) {
+    if (line.startsWith('user: ')) {
+      flush();
+      currentRole = 'user';
+      currentLines.push(line.slice('user: '.length));
+    } else if (line.startsWith('assistant: ')) {
+      flush();
+      currentRole = 'assistant';
+      currentLines.push(line.slice('assistant: '.length));
+    } else if (currentRole !== null) {
+      // Continuation line for the current turn.
+      currentLines.push(line);
+    }
+    // Lines before the first recognisable prefix are silently skipped —
+    // BEAM contexts always start with a "user: " line.
+  }
+  flush();
+  return result;
+}
+
+// ── Turn extractor ────────────────────────────────────────────────────────────
+
+/**
+ * Flatten a BEAM canonical JSONL archive into an array of atomic turn records.
+ *
+ * One record per conversation message. Per-conversation dedup applies: only
+ * the FIRST instance encountered for each `conversation_id` drives the context
+ * parse. Subsequent instances for the same conversation are skipped (they
+ * carry identical context).
+ *
+ * @param jsonlPath  Absolute or CWD-relative path to `beam.jsonl`.
+ */
+export function extractTurnsFromBeam(jsonlPath: string): BeamTurn[] {
+  if (!fs.existsSync(jsonlPath)) {
+    throw new Error(
+      `BEAM canonical archive not found at ${jsonlPath}. ` +
+      `Build it via: npx tsx benchmarks/harness/scripts/build-beam-canonical.ts`,
+    );
+  }
+
+  const raw = fs.readFileSync(jsonlPath, 'utf-8');
+  const lines = raw.split('\n');
+
+  const out: BeamTurn[] = [];
+  /** Tracks which conversation_ids we've already parsed context from. */
+  const seenConversations = new Set<string>();
+
+  for (const line of lines) {
+    const trimmed = line.trim();
+    if (!trimmed) continue;
+
+    let instance: BeamRawInstance;
+    try {
+      instance = JSON.parse(trimmed) as BeamRawInstance;
+    } catch {
+      // Tolerate malformed lines.
+      continue;
+    }
+
+    const conversationId = instance.conversation_id;
+    if (!conversationId) continue;
+
+    // Per-conversation dedup: only the first instance for each conversation_id
+    // has its context parsed (all instances for a conversation share the same
+    // context — parsing more than once just creates duplicate frames).
+    if (seenConversations.has(conversationId)) continue;
+    seenConversations.add(conversationId);
+
+    const context = instance.context ?? '';
+    const chatSize = instance.chat_size ?? 'unknown';
+    const parsedTurns = parseContextToTurns(context);
+
+    if (parsedTurns.length === 0) continue;
+
+    for (let i = 0; i < parsedTurns.length; i++) {
+      const { role, content } = parsedTurns[i];
+      out.push({
+        gopId: conversationId,
+        messageIndex: i,
+        role,
+        content,
+        formattedContent: `${role}: ${content}`,
+        chatSize,
+      });
+    }
+  }
+
+  return out;
+}
+
+// ── Ingest options ────────────────────────────────────────────────────────────
+
+/**
+ * Default batch size for vector indexing. Matches `ingest.ts`.
+ * See ingest.ts for the ollama-embedder timeout rationale.
+ */
+const DEFAULT_INDEX_BATCH_SIZE = 200;
+
+export interface IngestOptions {
+  /** Vector-index batch size. Default 200. Callers with fast/parallel
+   *  embedders can raise this; callers hitting timeouts should lower it. */
+  batchSize?: number;
+}
+
+// ── Corpus ingest ─────────────────────────────────────────────────────────────
+
+/**
+ * Ingest a BEAM turn stream into an ephemeral MindDB + HybridSearch pair.
+ *
+ * Signature is intentionally identical to `ingestLoCoMoCorpus` in `ingest.ts`
+ * so callers can swap the three ingest functions without changing their
+ * substrate wiring. The only semantic difference is the session label written
+ * to `SessionStore.ensure()` (`'beam-benchmark'`), which is cosmetic.
+ *
+ * Each turn becomes one I-frame with:
+ *   - `gop_id  = turn.gopId`            (= instance.conversation_id, e.g. "beam_0")
+ *   - `content = turn.formattedContent` (= `"${role}: ${content}"`)
+ *   - `source  = 'import'`
+ *   - `importance = 'normal'`
+ *
+ * The `gopId` value matches the `conversation_id` stored on every
+ * `DatasetInstance` for BEAM — so the retrieval and agentic cells' existing
+ * `gopId` filter in `HybridSearch.search()` scopes to the right conversation
+ * without any cell-level changes.
+ *
+ * Caller owns the MindDB + HybridSearch lifecycle (see `createSubstrate` in
+ * `substrate.ts`). Call `substrate.close()` in your `finally` block.
+ */
+export async function ingestBeamCorpus(
+  db: MindDB,
+  search: HybridSearch,
+  frames: FrameStore,
+  sessions: SessionStore,
+  turns: BeamTurn[],
+  options: IngestOptions = {},
+): Promise<IngestStats> {
+  void db;  // reserved for future per-db hooks; kept for signature symmetry with ingestLoCoMoCorpus
+  const batchSize = Math.max(1, options.batchSize ?? DEFAULT_INDEX_BATCH_SIZE);
+  const ingestStart = Date.now();
+  const toIndex: Array<{ id: number; content: string }> = [];
+  const seen = new Set<number>();
+
+  // memory_frames.gop_id → sessions.gop_id is a FOREIGN KEY. Ensure one
+  // session row per conversation exists BEFORE any createIFrame call fires.
+  const ensuredGops = new Set<string>();
+  for (const turn of turns) {
+    if (!ensuredGops.has(turn.gopId)) {
+      sessions.ensure(turn.gopId, 'beam-benchmark', `BEAM conversation ${turn.gopId}`);
+      ensuredGops.add(turn.gopId);
+    }
+
+    const frame = frames.createIFrame(turn.gopId, turn.formattedContent, 'normal', 'import');
+    if (seen.has(frame.id)) continue;   // dedup-collapsed duplicate
+    seen.add(frame.id);
+    toIndex.push({ id: frame.id, content: turn.formattedContent });
+  }
+  const ingestMs = Date.now() - ingestStart;
+
+  // Chunk the vector-index batch to avoid timeout issues on slow embedders.
+  const indexStart = Date.now();
+  for (let i = 0; i < toIndex.length; i += batchSize) {
+    const slice = toIndex.slice(i, i + batchSize);
+    await search.indexFramesBatch(slice);
+  }
+  const indexMs = Date.now() - indexStart;
+
+  return { count: toIndex.length, ingestMs, indexMs };
+}

benchmarks/harness/src/ingest-longmemeval.ts

@@ -0,0 +1,255 @@
+/**
+ * LME V1 turn-level ingest — mirrors the LoCoMo ingest pattern in ingest.ts.
+ *
+ * Reads the LongMemEval canonical archive (`benchmarks/data/longmemeval/
+ * longmemeval.jsonl`, built by build-longmemeval-canonical.ts) and produces
+ * an array of atomic turn frames, one per message per session.
+ *
+ * LME V1 source shape per instance:
+ *   {
+ *     instance_id: string,          // unique QA-pair id
+ *     conversation_id: string,      // equals question_id; conversation scope
+ *     question: string,
+ *     expected: string[],
+ *     context: string,              // formatted conversation (unused by ingest)
+ *     sessions: Array<{
+ *       session_id: string,
+ *       date?: string,              // optional ISO date for the session
+ *       messages: Array<{
+ *         role: 'user' | 'assistant',
+ *         content: string,
+ *       }>,
+ *     }>,
+ *   }
+ *
+ * The `retrieval` and `agentic` cells already use `instance.conversation_id`
+ * as a gopId filter in HybridSearch. This module stores each turn under
+ * `gopId = instance.conversation_id` so the filter scope aligns correctly.
+ * Because multiple instances share the same conversation_id, turns are
+ * deduplicated per conversation: only the FIRST instance encountered for
+ * each conversation_id drives the session extraction. Later instances for
+ * the same conversation carry the same turns — ingesting them twice would
+ * pollute the vector index with byte-identical duplicates.
+ *
+ * GATE-S0 decision (frame-per-turn granularity) applies here too: one frame
+ * per message, not one frame per session. See ingest.ts §GATE-S0.
+ */
+
+import fs from 'node:fs';
+import type { MindDB, HybridSearch, FrameStore, SessionStore } from '@waggle/core';
+
+// ── Public types ─────────────────────────────────────────────────────────────
+
+/** One LME V1 session message after parsing, ready to be written as a frame. */
+export interface LongMemEvalTurn {
+  /** Conversation identifier — becomes `memory_frames.gop_id`.
+   *  Equals `instance.conversation_id` from the canonical builder. */
+  gopId: string;
+  /** Zero-based index within the flattened session-message sequence. */
+  messageIndex: number;
+  role: 'user' | 'assistant';
+  /** Session identifier from `sessions[N].session_id`. */
+  sessionId: string;
+  /** Optional ISO date from `sessions[N].date`. */
+  sessionDate?: string;
+  content: string;
+  /** Formatted `"${role}: ${content}"` — the string that lands in
+   *  `memory_frames.content` and gets FTS5 / vec-indexed. Mirrors the
+   *  LoCoMo `"${speaker}: ${text}"` pattern from ingest.ts. */
+  formattedContent: string;
+}
+
+export interface IngestStats {
+  /** Number of frames successfully created (after dedup). */
+  count: number;
+  /** Wall-clock ms spent on `createIFrame` loop (includes FTS5 auto-index). */
+  ingestMs: number;
+  /** Wall-clock ms spent on `indexFramesBatch` (embedder + vec0 insert). */
+  indexMs: number;
+}
+
+// ── Internal raw-schema types ─────────────────────────────────────────────────
+
+interface LmeRawMessage {
+  role: string;
+  content: string;
+}
+
+interface LmeRawSession {
+  session_id?: string;
+  date?: string;
+  messages?: LmeRawMessage[];
+}
+
+interface LmeRawInstance {
+  instance_id?: string;
+  conversation_id?: string;
+  question?: string;
+  expected?: unknown;
+  context?: string;
+  sessions?: LmeRawSession[];
+}
+
+// ── Turn extractor ────────────────────────────────────────────────────────────
+
+/**
+ * Flatten a LongMemEval canonical JSONL archive into an array of atomic turn
+ * records.
+ *
+ * One record per `sessions[].messages[]` entry across ALL instances in the
+ * file, with per-conversation dedup: if two instances share the same
+ * `conversation_id`, only the FIRST encountered instance's sessions are
+ * extracted. Downstream `ingestLongMemEvalCorpus` will hit `FrameStore`'s
+ * own content-hash dedup, but this pre-dedup keeps the returned array lean
+ * and avoids redundant embedder calls.
+ *
+ * Session order is preserved verbatim (array index order in the source).
+ * Message order within each session is also preserved verbatim.
+ *
+ * @param jsonlPath  Absolute or CWD-relative path to `longmemeval.jsonl`.
+ */
+export function extractTurnsFromLongMemEval(jsonlPath: string): LongMemEvalTurn[] {
+  if (!fs.existsSync(jsonlPath)) {
+    throw new Error(
+      `LongMemEval canonical archive not found at ${jsonlPath}. ` +
+      `Build it via: npx tsx benchmarks/harness/scripts/build-longmemeval-canonical.ts`,
+    );
+  }
+
+  const raw = fs.readFileSync(jsonlPath, 'utf-8');
+  const lines = raw.split('\n');
+
+  const out: LongMemEvalTurn[] = [];
+  /** Tracks which conversation_ids we've already extracted sessions from. */
+  const seenConversations = new Set<string>();
+
+  for (const line of lines) {
+    const trimmed = line.trim();
+    if (!trimmed) continue;
+
+    let instance: LmeRawInstance;
+    try {
+      instance = JSON.parse(trimmed) as LmeRawInstance;
+    } catch {
+      // Tolerate malformed lines — consistent with datasets.ts loadDataset behaviour.
+      continue;
+    }
+
+    const conversationId = instance.conversation_id;
+    if (!conversationId) continue;
+
+    // Per-conversation dedup: only the first instance for each conversation_id
+    // drives the session extraction (all instances for a conversation share the
+    // same session history — re-ingesting would create duplicate frames).
+    if (seenConversations.has(conversationId)) continue;
+    seenConversations.add(conversationId);
+
+    if (!Array.isArray(instance.sessions)) continue;
+
+    let messageIndex = 0;
+    for (const session of instance.sessions) {
+      if (!session || !Array.isArray(session.messages)) continue;
+      const sessionId = session.session_id ?? `session_${messageIndex}`;
+      const sessionDate = typeof session.date === 'string' ? session.date : undefined;
+
+      for (const msg of session.messages) {
+        if (!msg || typeof msg.content !== 'string' || !msg.content.trim()) continue;
+        const role = msg.role === 'assistant' ? 'assistant' : 'user';
+        out.push({
+          gopId: conversationId,
+          messageIndex,
+          role,
+          sessionId,
+          ...(sessionDate !== undefined ? { sessionDate } : {}),
+          content: msg.content,
+          formattedContent: `${role}: ${msg.content}`,
+        });
+        messageIndex++;
+      }
+    }
+  }
+
+  return out;
+}
+
+// ── Ingest options ────────────────────────────────────────────────────────────
+
+/**
+ * Default batch size for vector indexing. Matches `ingest.ts` DEFAULT_INDEX_BATCH_SIZE.
+ * See ingest.ts for the rationale (ollama-embedder 30 s per-request timeout).
+ */
+const DEFAULT_INDEX_BATCH_SIZE = 200;
+
+export interface IngestOptions {
+  /** Vector-index batch size. Default 200. Callers with fast/parallel
+   *  embedders can raise this; callers hitting timeouts should lower it. */
+  batchSize?: number;
+}
+
+// ── Corpus ingest ─────────────────────────────────────────────────────────────
+
+/**
+ * Ingest a LongMemEval turn stream into an ephemeral MindDB + HybridSearch pair.
+ *
+ * Signature is intentionally identical to `ingestLoCoMoCorpus` in `ingest.ts`
+ * so callers can swap the two without changing their substrate wiring.
+ *
+ * Each turn becomes one I-frame with:
+ *   - `gop_id  = turn.gopId`            (= instance.conversation_id)
+ *   - `content = turn.formattedContent` (= `"${role}: ${content}"`)
+ *   - `source  = 'import'`
+ *   - `importance = 'normal'`
+ *
+ * The `gopId` value matches the `conversation_id` stored on every
+ * `DatasetInstance` for LME V1 — so the retrieval and agentic cells'
+ * existing `gopId` filter in `HybridSearch.search()` will correctly scope
+ * to the right conversation without any cell-level changes.
+ *
+ * FTS5 indexing fires automatically inside `createIFrame`. Vector indexing is
+ * batched via `indexFramesBatch` in chunks of `batchSize` so slow/rate-limited
+ * embedders don't hit request timeouts on large corpora.
+ *
+ * Caller owns the MindDB + HybridSearch lifecycle (see `createSubstrate` in
+ * `substrate.ts`). Call `substrate.close()` in your `finally` block.
+ */
+export async function ingestLongMemEvalCorpus(
+  db: MindDB,
+  search: HybridSearch,
+  frames: FrameStore,
+  sessions: SessionStore,
+  turns: LongMemEvalTurn[],
+  options: IngestOptions = {},
+): Promise<IngestStats> {
+  void db;  // reserved for future per-db hooks; kept for signature symmetry with ingestLoCoMoCorpus
+  const batchSize = Math.max(1, options.batchSize ?? DEFAULT_INDEX_BATCH_SIZE);
+  const ingestStart = Date.now();
+  const toIndex: Array<{ id: number; content: string }> = [];
+  const seen = new Set<number>();
+
+  // memory_frames.gop_id → sessions.gop_id is a FOREIGN KEY. Ensure one
+  // session row per conversation exists BEFORE any createIFrame call fires.
+  const ensuredGops = new Set<string>();
+  for (const turn of turns) {
+    if (!ensuredGops.has(turn.gopId)) {
+      sessions.ensure(turn.gopId, 'longmemeval-benchmark', `LME V1 conversation ${turn.gopId}`);
+      ensuredGops.add(turn.gopId);
+    }
+
+    const frame = frames.createIFrame(turn.gopId, turn.formattedContent, 'normal', 'import');
+    if (seen.has(frame.id)) continue;   // dedup-collapsed duplicate
+    seen.add(frame.id);
+    toIndex.push({ id: frame.id, content: turn.formattedContent });
+  }
+  const ingestMs = Date.now() - ingestStart;
+
+  // Chunk the vector-index batch so a slow embedder can't blow the per-request
+  // timeout on a large corpus. Each chunk is one sqlite-vec transaction.
+  const indexStart = Date.now();
+  for (let i = 0; i < toIndex.length; i += batchSize) {
+    const slice = toIndex.slice(i, i + batchSize);
+    await search.indexFramesBatch(slice);
+  }
+  const indexMs = Date.now() - indexStart;
+
+  return { count: toIndex.length, ingestMs, indexMs };
+}

benchmarks/harness/src/ingest.ts

@@ -0,0 +1,209 @@
+/**
+ * Task 2.5 Stage 1 — LoCoMo turn-level ingest.
+ *
+ * Reads the raw LoCoMo archive (`benchmarks/data/locomo10.json` from the
+ * snap-research/locomo repo) and produces a stream of atomic turn frames.
+ * Each LoCoMo conversation is a `{speaker_a, speaker_b, session_N_date_time,
+ * session_N: LocomoTurn[]}` object; we enumerate every `session_N` array,
+ * flatten its turns, and emit one `{gopId, diaId, speaker, text, content}`
+ * record per turn.
+ *
+ * GATE-S0 decision (2026-04-23, PM Marko Marković): granularity = frame-per-turn.
+ * Rationale: frame-per-conversation (10 frames, via dedup collapse) degenerates
+ * the retrieval cell into the full-context cell and kills the 4-cell ablation
+ * signal. See sessions/2026-04-23-task25-s0-readiness.md §0.3 and the Stage 1
+ * brief for the adjudication record.
+ *
+ * The ingest wrapper composes two primitives that already exist in
+ * `@waggle/core`:
+ *   - `FrameStore.createIFrame(gopId, content, importance, source)` — inserts
+ *     one memory_frames row + auto-indexes FTS5.
+ *   - `HybridSearch.indexFramesBatch([{id, content}])` — atomic batch vector
+ *     index via `embedder.embedBatch(contents)`.
+ *
+ * No LLM calls. Ingest is offline and costs nothing (the embedder is local:
+ * ollama-embedder for production runs, a deterministic fake for unit tests).
+ */
+
+import fs from 'node:fs';
+import type { MindDB, HybridSearch, FrameStore, SessionStore } from '@waggle/core';
+
+/** Raw LoCoMo turn shape from `benchmarks/data/locomo10.json`. Matches the
+ *  snap-research/locomo schema. Optional fields (`img_url`, `blip_caption`,
+ *  `query`) are ignored by the ingest — they don't carry text-level memory. */
+export interface LocomoRawTurn {
+  speaker: string;
+  dia_id: string;
+  text: string;
+  img_url?: string[];
+  blip_caption?: string;
+  query?: string;
+}
+
+export interface LocomoRawConversation {
+  speaker_a: string;
+  speaker_b: string;
+  [sessionKey: string]: string | LocomoRawTurn[];
+}
+
+export interface LocomoRawSample {
+  sample_id: string;
+  conversation: LocomoRawConversation;
+  qa: unknown[];
+}
+
+/** Extracted turn, ready to be written as a frame. `content` is the string
+ *  that lands in `memory_frames.content` and gets FTS5/vec-indexed. */
+export interface LocomoTurn {
+  /** Conversation identifier — becomes `memory_frames.gop_id`. */
+  gopId: string;
+  /** LoCoMo evidence id (`D<session>:<turn>`). Preserved for traceability. */
+  diaId: string;
+  speaker: string;
+  text: string;
+  /** Formatted `"{speaker}: {text}"` — the content that gets embedded. */
+  content: string;
+}
+
+export interface IngestStats {
+  /** Number of frames successfully created. */
+  count: number;
+  /** Wall-clock ms spent on `createIFrame` loop (includes FTS5 auto-index). */
+  ingestMs: number;
+  /** Wall-clock ms spent on `indexFramesBatch` (embedder + vec0 insert). */
+  indexMs: number;
+}
+
+/**
+ * Flatten a raw LoCoMo archive into an array of atomic turn records.
+ *
+ * One record per turn across every `session_N` array in every conversation.
+ * Session order within a conversation is numeric ascending; turn order within
+ * a session is preserved verbatim from the source. This matches the paper's
+ * "1540 atomic frames across 10 LoCoMo conversations (~154 turns each)" claim
+ * — the actual count may differ slightly from 1540 because the source file
+ * can have variable turn counts per conversation.
+ */
+export function extractTurnsFromLocomoRaw(rawPath: string): LocomoTurn[] {
+  if (!fs.existsSync(rawPath)) {
+    throw new Error(
+      `LoCoMo raw archive not found at ${rawPath}. Download with: ` +
+      `curl -sL -o benchmarks/data/locomo10.json ` +
+      `https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json`,
+    );
+  }
+  const raw = fs.readFileSync(rawPath, 'utf-8');
+  let samples: LocomoRawSample[];
+  try {
+    samples = JSON.parse(raw) as LocomoRawSample[];
+  } catch (err: unknown) {
+    const msg = err instanceof Error ? err.message : String(err);
+    throw new Error(`LoCoMo archive at ${rawPath} is not valid JSON: ${msg}`);
+  }
+  if (!Array.isArray(samples)) {
+    throw new Error(`LoCoMo archive at ${rawPath} must be a JSON array of samples`);
+  }
+
+  const out: LocomoTurn[] = [];
+  for (const sample of samples) {
+    if (!sample?.sample_id || !sample.conversation) continue;
+    const sessionKeys = Object.keys(sample.conversation)
+      .filter(k => /^session_\d+$/.test(k))
+      .sort((a, b) => parseSessionNumber(a) - parseSessionNumber(b));
+    for (const key of sessionKeys) {
+      const turns = sample.conversation[key];
+      if (!Array.isArray(turns)) continue;
+      for (const turn of turns) {
+        if (!turn?.dia_id || typeof turn.text !== 'string' || !turn.speaker) continue;
+        out.push({
+          gopId: sample.sample_id,
+          diaId: turn.dia_id,
+          speaker: turn.speaker,
+          text: turn.text,
+          content: `${turn.speaker}: ${turn.text}`,
+        });
+      }
+    }
+  }
+  return out;
+}
+
+function parseSessionNumber(key: string): number {
+  const m = key.match(/^session_(\d+)$/);
+  return m ? Number(m[1]) : Number.POSITIVE_INFINITY;
+}
+
+/**
+ * Ingest a turn stream into an ephemeral MindDB + HybridSearch pair.
+ *
+ * Each turn becomes one I-frame with `gop_id = turn.gopId`, `content =
+ * turn.content`, `source = 'import'`, `importance = 'normal'`. After all
+ * frames are created, a single `indexFramesBatch` call embeds them in one
+ * sqlite-vec transaction. FTS5 indexing is handled automatically by
+ * `createIFrame` via `indexFts`.
+ *
+ * Caller owns the MindDB + HybridSearch lifecycle (see `createSubstrate` in
+ * `substrate.ts`). Dedup (via `FrameStore.findDuplicate(content)`) is
+ * expected to be a no-op at turn granularity — two turns with byte-identical
+ * `"{speaker}: {text}"` across conversations are vanishingly rare in LoCoMo.
+ * When it does fire, the returned `count` reflects the deduplicated total so
+ * the caller's vector-index batch stays in sync with the frame table.
+ */
+/**
+ * Default batch size for vector indexing. ollama-embedder has a hardcoded 30s
+ * per-request timeout and nomic-embed-text handles ~200 short turns/request
+ * comfortably; larger batches can hit the timeout on slower machines or
+ * larger embedding models. 200 is a conservative default that works on a
+ * dev workstation; callers can override via the `batchSize` option for
+ * faster embedders or tighter memory budgets.
+ */
+const DEFAULT_INDEX_BATCH_SIZE = 200;
+
+export interface IngestOptions {
+  /** Vector-index batch size. Default 200. Callers with fast/parallel
+   *  embedders can raise this; callers hitting timeouts should lower it. */
+  batchSize?: number;
+}
+
+export async function ingestLoCoMoCorpus(
+  db: MindDB,
+  search: HybridSearch,
+  frames: FrameStore,
+  sessions: SessionStore,
+  turns: LocomoTurn[],
+  options: IngestOptions = {},
+): Promise<IngestStats> {
+  void db;  // reserved for future per-db hooks; kept for signature symmetry
+  const batchSize = Math.max(1, options.batchSize ?? DEFAULT_INDEX_BATCH_SIZE);
+  const ingestStart = Date.now();
+  const toIndex: Array<{ id: number; content: string }> = [];
+  const seen = new Set<number>();
+  // memory_frames.gop_id → sessions.gop_id is a FOREIGN KEY. Ensure one
+  // session row per conversation exists BEFORE any createIFrame call fires.
+  const ensuredGops = new Set<string>();
+  for (const turn of turns) {
+    if (!ensuredGops.has(turn.gopId)) {
+      sessions.ensure(turn.gopId, 'locomo-benchmark', `LoCoMo conversation ${turn.gopId}`);
+      ensuredGops.add(turn.gopId);
+    }
+    const frame = frames.createIFrame(turn.gopId, turn.content, 'normal', 'import');
+    if (seen.has(frame.id)) continue;   // dedup-collapsed duplicate
+    seen.add(frame.id);
+    toIndex.push({ id: frame.id, content: turn.content });
+  }
+  const ingestMs = Date.now() - ingestStart;
+
+  // Chunk the vector-index batch so a slow embedder (ollama, API with
+  // rate limits) can't blow the per-request timeout on a large corpus.
+  // sqlite-vec's vec0 insert is already transactional per `indexFramesBatch`
+  // call, so chunking preserves atomicity per batch (just with multiple
+  // transactions end-to-end — the same data lands either way).
+  const indexStart = Date.now();
+  for (let i = 0; i < toIndex.length; i += batchSize) {
+    const slice = toIndex.slice(i, i + batchSize);
+    await search.indexFramesBatch(slice);
+  }
+  const indexMs = Date.now() - indexStart;
+
+  return { count: toIndex.length, ingestMs, indexMs };
+}

benchmarks/harness/src/judge-client.ts

@@ -0,0 +1,176 @@
+/**
+ * Judge LLM client — thin wrapper around LiteLLM chat completions that
+ * implements the `LlmClient` interface the failure-mode-judge module
+ * expects (`complete(prompt: string): Promise<string>`).
+ *
+ * Sprint 9 Task 2. Two retries with exponential backoff (1s, 3s) per
+ * brief §Failure-handling. Parse-level retry (the reminder-and-retry
+ * for malformed JSON) is handled INSIDE the judge module itself —
+ * this client only retries transport-level failures (HTTP non-2xx,
+ * fetch errors, timeouts). Keeping the two concerns separated stops
+ * a single flaky network hop from eating both retry budgets at once.
+ *
+ * Cost tracking: the underlying LiteLLM response carries `usage.*` and
+ * sometimes `cost` on the message envelope; the caller that constructs
+ * this client passes a cost table so the runner can aggregate per-cell
+ * judge spend in the Task-3 rollup.
+ */
+
+import type { LlmClient } from './judge-types.js';
+export type { LlmClient } from './judge-types.js';
+
+export interface JudgeClientCostEntry {
+  /** ISO-8601 timestamp of the call. */
+  timestamp: string;
+  /** Model id used for the judge call. */
+  model: string;
+  promptTokens: number;
+  completionTokens: number;
+  usd: number;
+  latencyMs: number;
+  /** `true` when the call succeeded, `false` when all retries exhausted. */
+  ok: boolean;
+}
+
+export interface JudgeLlmClientConfig {
+  litellmUrl: string;
+  litellmApiKey: string;
+  model: string;
+  /** USD per 1M tokens — [input, output]. Defaults to Sonnet tier rates. */
+  pricePerMillionInput?: number;
+  pricePerMillionOutput?: number;
+  /** Called once per completed attempt (success or final failure). The
+   *  runner's cost aggregator reads this to populate the Task-3 cost
+   *  summary in aggregate.ts. Optional — tests can omit. */
+  onCall?: (entry: JudgeClientCostEntry) => void;
+  /** Injection seam for tests: replaces `fetch` so unit tests never
+   *  reach the network. Production path leaves this undefined and uses
+   *  the global fetch. */
+  fetchImpl?: typeof fetch;
+  /** Abort individual attempt after N ms. Default 30_000. */
+  timeoutMs?: number;
+  /** Override the default backoff schedule ([1000, 3000] per brief).
+   *  Exposed so tests can collapse sleeps to near-zero and stay fast. */
+  backoffMs?: number[];
+}
+
+/** Default Sonnet-4.6 pricing (current 2026-04 list rate). */
+const DEFAULT_PRICE_PER_MILLION_INPUT = 3.0;
+const DEFAULT_PRICE_PER_MILLION_OUTPUT = 15.0;
+const DEFAULT_BACKOFF_MS = [1000, 3000];
+
+export function createJudgeLlmClient(config: JudgeLlmClientConfig): LlmClient {
+  const url = config.litellmUrl.replace(/\/$/, '');
+  const priceIn = config.pricePerMillionInput ?? DEFAULT_PRICE_PER_MILLION_INPUT;
+  const priceOut = config.pricePerMillionOutput ?? DEFAULT_PRICE_PER_MILLION_OUTPUT;
+  const fetchFn = config.fetchImpl ?? fetch;
+  const timeoutMs = config.timeoutMs ?? 30_000;
+  const backoff = config.backoffMs ?? DEFAULT_BACKOFF_MS;
+
+  async function sleep(ms: number): Promise<void> {
+    return new Promise(resolve => { setTimeout(resolve, ms); });
+  }
+
+  async function attempt(prompt: string): Promise<{
+    text: string;
+    promptTokens: number;
+    completionTokens: number;
+    latencyMs: number;
+  }> {
+    const started = Date.now();
+    const controller = new AbortController();
+    const timer = setTimeout(() => controller.abort(), timeoutMs);
+    try {
+      // Opus 4.7 and some newer reasoning-model families reject the
+      // `temperature` param with HTTP 400. Omit it for those; they're
+      // effectively deterministic at their provider defaults. Keep T=0
+      // for the rest for reproducibility.
+      const rejectsTemperature = /opus-4-7|gpt-5|o3|o4/i.test(config.model);
+      const reqBody: Record<string, unknown> = {
+        model: config.model,
+        messages: [{ role: 'user', content: prompt }],
+        max_tokens: 1024,
+      };
+      if (!rejectsTemperature) reqBody.temperature = 0.0;
+      const res = await fetchFn(`${url}/v1/chat/completions`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          Authorization: `Bearer ${config.litellmApiKey}`,
+        },
+        signal: controller.signal,
+        body: JSON.stringify(reqBody),
+      });
+      const latencyMs = Date.now() - started;
+      if (!res.ok) {
+        const body = await res.text();
+        throw new Error(`judge-client ${config.model} HTTP ${res.status}: ${body.slice(0, 240)}`);
+      }
+      const body = (await res.json()) as {
+        choices?: Array<{ message?: { content?: string; reasoning_content?: string } }>;
+        usage?: { prompt_tokens?: number; completion_tokens?: number };
+      };
+      const message = body.choices?.[0]?.message ?? {};
+      const content = typeof message.content === 'string' ? message.content : '';
+      // Thinking-mode providers occasionally return empty `content` with
+      // the parsed JSON hiding in `reasoning_content`. Fall back so the
+      // judge module's JSON extractor still has a chance to find the
+      // payload — its `extractJsonBody` strips prose and fence wrappers.
+      const reasoning = typeof message.reasoning_content === 'string' ? message.reasoning_content : '';
+      const text = content || reasoning;
+      return {
+        text,
+        promptTokens: body.usage?.prompt_tokens ?? 0,
+        completionTokens: body.usage?.completion_tokens ?? 0,
+        latencyMs,
+      };
+    } finally {
+      clearTimeout(timer);
+    }
+  }
+
+  return {
+    async complete(prompt: string): Promise<string> {
+      // Up to `backoff.length + 1` total attempts: one initial + len(backoff) retries.
+      const totalAttempts = backoff.length + 1;
+      let lastErr: unknown = null;
+      for (let i = 0; i < totalAttempts; i++) {
+        try {
+          const result = await attempt(prompt);
+          config.onCall?.({
+            timestamp: new Date().toISOString(),
+            model: config.model,
+            promptTokens: result.promptTokens,
+            completionTokens: result.completionTokens,
+            usd:
+              (result.promptTokens / 1_000_000) * priceIn +
+              (result.completionTokens / 1_000_000) * priceOut,
+            latencyMs: result.latencyMs,
+            ok: true,
+          });
+          return result.text;
+        } catch (err) {
+          lastErr = err;
+          if (i < backoff.length) {
+            await sleep(backoff[i]);
+          }
+        }
+      }
+      // All attempts exhausted — surface one cost log entry marked failed
+      // so the aggregator can account for consumed budget even on total
+      // loss, then throw for the caller's retry-vs-skip decision.
+      config.onCall?.({
+        timestamp: new Date().toISOString(),
+        model: config.model,
+        promptTokens: 0,
+        completionTokens: 0,
+        usd: 0,
+        latencyMs: 0,
+        ok: false,
+      });
+      throw lastErr instanceof Error
+        ? lastErr
+        : new Error(`judge-client ${config.model} failed after ${totalAttempts} attempts`);
+    },
+  };
+}

benchmarks/harness/src/judge-runner.ts

@@ -0,0 +1,401 @@
+/**
+ * Judge runner adapter — wraps the failure-mode-judge module for use
+ * inside the benchmark runner loop.
+ *
+ * Sprint 9 Task 2. The cells produce raw LLM answers; this module takes
+ * (question, ground_truth, context_excerpt, model_answer) from each
+ * instance + cell result and returns a JSONL-ready verdict payload.
+ *
+ * Single-judge path is the production default. `judgeEnsemble` path is
+ * exposed for the calibration Task 5 (Fleiss' kappa probe) and for
+ * future Week-1 ensemble runs per taxonomy §6.
+ *
+ * Parse failures are already handled inside the judge module (one
+ * reminder-retry, then JudgeParseError). Transport failures are handled
+ * inside the JudgeLlmClient (two retries with exponential backoff).
+ * Both failure surfaces are caught here and converted into an
+ * `unjudged` result so the run continues — losing one judge call never
+ * aborts a whole Stage-2 batch.
+ */
+
+import type { LlmClient } from './judge-types.js';
+import type { FailureCode, FailureMode, JudgeEnsembleEntry, JudgeVerdict } from './types.js';
+
+/**
+ * Map a Sprint 9 5-value `FailureMode` onto the A3 LOCK § 6 8-value
+ * `FailureCode` space per decisions/2026-04-23-jsonl-record-taxonomy-split-locked.md.
+ *
+ * Sprint 9 F1..F5 semantics align 1:1 with A3 § 6 F1..F5 (refusal / partial /
+ * off-topic / hallucination / incorrect). F6 (format-violation) and F_other
+ * (≥10-word rationale escape) are A3-only surfaces — they appear on the
+ * harness output only after the judge rubric splice upgrade (follow-on of
+ * §2.1 in the Task 2 runtime; this mapper passes them through as-is on the
+ * judge-emit path).
+ */
+function mapLegacyToA3(legacy: FailureMode | null | undefined): FailureCode {
+  if (legacy === undefined || legacy === null) return null;
+  // FailureMode ⊂ FailureCode at the string level; cast is semantically safe.
+  return legacy as FailureCode;
+}
+
+// The judge module lives in a sibling workspace; tsc's `rootDir: "src"`
+// refuses a direct typed import (TS6059). We resolve the runtime
+// module at call time via dynamic import + ambient shape typing, which
+// keeps the compile-time contract local to the harness (see
+// judge-types.ts) while the real implementation ships from server/.
+//
+// This is deliberately narrow — only the symbols the runner consumes
+// are typed here, mirroring the canonical declarations in the server
+// judge module. Any shape drift between the two will surface as a
+// runtime TypeError at the first call site, not a silent downgrade.
+interface JudgeModule {
+  judgeAnswer(params: {
+    question: string;
+    groundTruth: string;
+    contextExcerpt: string;
+    modelAnswer: string;
+    judgeModel: string;
+    llmClient: LlmClient;
+  }): Promise<{
+    verdict: JudgeVerdict;
+    failure_mode: null | FailureMode;
+    rationale: string;
+    judge_model: string;
+  }>;
+  judgeEnsemble(params: {
+    question: string;
+    groundTruth: string;
+    contextExcerpt: string;
+    modelAnswer: string;
+    judgeModels: string[];
+    llmClients: Map<string, LlmClient>;
+  }): Promise<{
+    ensemble: Array<{
+      verdict: JudgeVerdict;
+      failure_mode: null | FailureMode;
+      rationale: string;
+      judge_model: string;
+    }>;
+    majority: {
+      verdict: JudgeVerdict;
+      failure_mode: null | FailureMode;
+      rationale: string;
+      judge_model: string;
+    };
+    fleissKappa: number;
+  }>;
+  JudgeParseError: new (...args: unknown[]) => Error;
+}
+
+let cachedModule: JudgeModule | null = null;
+
+/** Compute the runtime path to the judge module from the harness dist/src
+ *  location at call time. Building the string from `import.meta.url` keeps
+ *  tsc from chasing it during rootDir resolution (TS6059) and defers the
+ *  path to vite-node / tsx / runtime ESM loader. */
+async function loadJudgeModule(): Promise<JudgeModule> {
+  if (cachedModule) return cachedModule;
+  const { fileURLToPath, pathToFileURL } = await import('node:url');
+  const nodePath = await import('node:path');
+  const here = fileURLToPath(import.meta.url);
+  // `harness/src/judge-runner.ts` OR `harness/dist/judge-runner.js` → up to repo root.
+  const repoRoot = nodePath.resolve(nodePath.dirname(here), '..', '..', '..');
+  // Prefer src (for tsx/vitest) and fall back to compiled dist (for `node`).
+  const candidates = [
+    nodePath.resolve(repoRoot, 'packages/server/src/benchmarks/judge/failure-mode-judge.ts'),
+    nodePath.resolve(repoRoot, 'packages/server/src/benchmarks/judge/failure-mode-judge.js'),
+    nodePath.resolve(repoRoot, 'packages/server/dist/benchmarks/judge/failure-mode-judge.js'),
+  ];
+  const fs = await import('node:fs');
+  const target = candidates.find(p => fs.existsSync(p));
+  if (!target) {
+    throw new Error(
+      `judge module not found — looked in:\n  ${candidates.join('\n  ')}`,
+    );
+  }
+  cachedModule = (await import(pathToFileURL(target).href)) as JudgeModule;
+  return cachedModule;
+}
+
+export interface JudgeTriple {
+  question: string;
+  groundTruth: string;
+  contextExcerpt: string;
+  modelAnswer: string;
+}
+
+/** Subset of JsonlRecord fields the runner copies from this payload.
+ *  Fields are `undefined` when judging was skipped, disabled, or failed
+ *  irrecoverably — the runner writes only the fields this payload
+ *  supplied (consumers treat `undefined` as "not judged yet"). */
+export interface JudgePayload {
+  model_answer: string;
+  judge_verdict?: JudgeVerdict;
+  judge_failure_mode?: FailureMode | null;
+  judge_rationale?: string;
+  judge_model?: string;
+  judge_timestamp?: string;
+  judge_ensemble?: JudgeEnsembleEntry[];
+  /** Non-null when the judge call failed after all retries. The runner
+   *  logs this but does NOT surface it as a cell-level failure_mode —
+   *  the cell succeeded, only the post-hoc grading failed. */
+  judge_error?: string;
+  /**
+   * Sprint 11 B2 fold-in (2026-04-22): path the ensemble resolver took.
+   *   - `undefined` on single-judge runs or when the tie-break module
+   *      was never consulted (e.g. 3-0 consensus, 2-1 majority — handled
+   *      by legacy `computeMajority` inside `judgeEnsemble`).
+   *   - `'none'` / `'majority'` when resolveTieBreak short-circuited.
+   *   - `'quadri-vendor'` when 1-1-1 was escalated to the fourth vendor
+   *      and resolved.
+   *   - `'pm-escalation'` when the four votes produced 1-1-1-1 → runtime
+   *      surfaces this as `judge_error: 'PM_ESCALATION'` so the aggregator
+   *      treats the instance as skipped (no silent coin-flip verdict).
+   */
+  tie_break_path?: 'none' | 'majority' | 'quadri-vendor' | 'pm-escalation';
+  /** Model slug that cast the fourth vote when `tie_break_path === 'quadri-vendor'`
+   *  or `'pm-escalation'`. e.g. `'xai/grok-4.20'`. */
+  tie_break_fourth_vendor?: string;
+  // ── Sprint 12 Task 2 §2.1 A3 namespace split (LOCKED 2026-04-23) ──────
+  /**
+   * A3 LOCK § 6 failure-code column. Populated alongside the legacy
+   * `judge_failure_mode` per namespace-split decision doc — legacy field
+   * is preserved verbatim for backward compat with pre-A3 consumers, a3
+   * field is the authoritative A3 exit-criterion column.
+   */
+  a3_failure_code?: FailureCode;
+  /** A3 rationale. Non-null when a3_failure_code === 'F_other' (validator-
+   *  enforced). Null for correct verdicts and optional for F1..F6. */
+  a3_rationale?: string | null;
+}
+
+export interface SingleJudgeConfig {
+  kind: 'single';
+  model: string;
+  client: LlmClient;
+}
+
+export interface EnsembleJudgeConfig {
+  kind: 'ensemble';
+  /** Ordered list — index 0 is the tie-breaker (Sonnet by convention
+   *  per taxonomy §6). */
+  models: string[];
+  clients: Map<string, LlmClient>;
+  /**
+   * Sprint 11 B2 fold-in (2026-04-22) per decisions/2026-04-22-tie-break-policy-locked.md:
+   * when provided AND a 1-1-1 three-way split emerges from the primary
+   * ensemble (only meaningful when `models.length === 3`), judge-runner
+   * calls `resolveTieBreak` with this client as the fourth vendor.
+   * Absent → keep the legacy `computeMajority` behavior (tie-breaker =
+   * first model in `models` list).
+   */
+  tieBreakerModel?: string;
+  tieBreakerClient?: LlmClient;
+}
+
+export type JudgeConfig = SingleJudgeConfig | EnsembleJudgeConfig;
+
+/** Module shape mirror for resolveTieBreak — same dynamic-import pattern
+ *  as loadJudgeModule to keep tsc happy under `rootDir: "src"`. */
+interface TieBreakModule {
+  resolveTieBreak(
+    votes: Array<{ verdict: JudgeVerdict; failure_mode: FailureMode | null; rationale: string; judge_model: string }>,
+    options: {
+      callFourthVendor?: (payload: { primaryVotes: Array<{ verdict: JudgeVerdict; failure_mode: FailureMode | null; rationale: string; judge_model: string }>; model: string }) => Promise<{ verdict: JudgeVerdict; failure_mode: FailureMode | null; rationale: string; judge_model: string }>;
+      fourthVendorModel?: string;
+      logger?: { info(event: string, fields: Record<string, unknown>): void; warn?(event: string, fields: Record<string, unknown>): void };
+    },
+  ): Promise<{
+    verdict: string;
+    path: 'none' | 'majority' | 'quadri-vendor' | 'pm-escalation';
+    votes: Array<{ verdict: JudgeVerdict; failure_mode: FailureMode | null; rationale: string; judge_model: string }>;
+    fourthVendorVote?: { verdict: JudgeVerdict; failure_mode: FailureMode | null; rationale: string; judge_model: string };
+    fourthVendorSlug?: string;
+  }>;
+  PM_ESCALATION_VERDICT: string;
+  DEFAULT_FOURTH_VENDOR: string;
+}
+
+let cachedTieBreakModule: TieBreakModule | null = null;
+
+async function loadTieBreakModule(): Promise<TieBreakModule> {
+  if (cachedTieBreakModule) return cachedTieBreakModule;
+  const { fileURLToPath, pathToFileURL } = await import('node:url');
+  const nodePath = await import('node:path');
+  const here = fileURLToPath(import.meta.url);
+  const repoRoot = nodePath.resolve(nodePath.dirname(here), '..', '..', '..');
+  const candidates = [
+    nodePath.resolve(repoRoot, 'packages/server/src/benchmarks/judge/ensemble-tiebreak.ts'),
+    nodePath.resolve(repoRoot, 'packages/server/src/benchmarks/judge/ensemble-tiebreak.js'),
+    nodePath.resolve(repoRoot, 'packages/server/dist/benchmarks/judge/ensemble-tiebreak.js'),
+  ];
+  const fs = await import('node:fs');
+  const target = candidates.find(p => fs.existsSync(p));
+  if (!target) {
+    throw new Error(
+      `ensemble-tiebreak module not found — looked in:\n  ${candidates.join('\n  ')}`,
+    );
+  }
+  cachedTieBreakModule = (await import(pathToFileURL(target).href)) as TieBreakModule;
+  return cachedTieBreakModule;
+}
+
+export async function runJudge(
+  triple: JudgeTriple,
+  config: JudgeConfig,
+): Promise<JudgePayload> {
+  const base: JudgePayload = { model_answer: triple.modelAnswer };
+  const mod = await loadJudgeModule();
+  try {
+    if (config.kind === 'single') {
+      const result = await mod.judgeAnswer({
+        question: triple.question,
+        groundTruth: triple.groundTruth,
+        contextExcerpt: triple.contextExcerpt,
+        modelAnswer: triple.modelAnswer,
+        judgeModel: config.model,
+        llmClient: config.client,
+      });
+      return {
+        ...base,
+        judge_verdict: result.verdict,
+        judge_failure_mode: result.failure_mode,
+        judge_rationale: result.rationale,
+        judge_model: result.judge_model,
+        judge_timestamp: new Date().toISOString(),
+        // A3 namespace split (LOCKED 2026-04-23 §2.1): mirror the legacy
+        // 5-value code into the 8-value column. Judge rubric upgrade will
+        // extend emission to F6 / F_other in a follow-on commit.
+        a3_failure_code: mapLegacyToA3(result.failure_mode),
+        a3_rationale: null,
+      };
+    }
+
+    const result = await mod.judgeEnsemble({
+      question: triple.question,
+      groundTruth: triple.groundTruth,
+      contextExcerpt: triple.contextExcerpt,
+      modelAnswer: triple.modelAnswer,
+      judgeModels: config.models,
+      llmClients: config.clients,
+    });
+
+    // Sprint 11 B2 fold-in (2026-04-22): 3-primary ensemble + tie-break
+    // client supplied + 1-1-1 three-way split observed → escalate via
+    // resolveTieBreak. Preserves judgeEnsemble's internal contract (it
+    // still returns its computeMajority-derived `majority` field);
+    // judge-runner post-processes to override the majority when the
+    // escalation triggers.
+    const isThreePrimary = config.models.length === 3;
+    const hasTieBreaker = Boolean(config.tieBreakerClient);
+    if (isThreePrimary && hasTieBreaker) {
+      const distinctVoteKeys = new Set(
+        result.ensemble.map(r => `${r.verdict}|${r.failure_mode ?? 'NA'}`),
+      );
+      if (distinctVoteKeys.size === 3) {
+        // 1-1-1 split confirmed. Dispatch to resolveTieBreak.
+        const tb = await loadTieBreakModule();
+        const fourthVendorModel = config.tieBreakerModel ?? tb.DEFAULT_FOURTH_VENDOR;
+        const tbResult = await tb.resolveTieBreak(result.ensemble, {
+          fourthVendorModel,
+          callFourthVendor: async ({ model: tbModel }) => {
+            const grokJudge = await mod.judgeAnswer({
+              question: triple.question,
+              groundTruth: triple.groundTruth,
+              contextExcerpt: triple.contextExcerpt,
+              modelAnswer: triple.modelAnswer,
+              judgeModel: tbModel,
+              llmClient: config.tieBreakerClient!,
+            });
+            return {
+              verdict: grokJudge.verdict,
+              failure_mode: grokJudge.failure_mode,
+              rationale: grokJudge.rationale,
+              judge_model: grokJudge.judge_model,
+            };
+          },
+        });
+
+        const ensembleVotes = tbResult.votes.map(r => ({
+          model: r.judge_model,
+          verdict: r.verdict,
+          failure_mode: r.failure_mode,
+          rationale: r.rationale,
+        }));
+
+        if (tbResult.path === 'pm-escalation') {
+          // 1-1-1-1 four-way — surface as judge_error so aggregator treats
+          // it as skipped (no silent coin-flip verdict). Preserves the
+          // Fleiss' κ=0.8784 methodology lock by NEVER fabricating a
+          // verdict when the ensemble + tie-break cannot reach plurality.
+          return {
+            ...base,
+            judge_timestamp: new Date().toISOString(),
+            judge_ensemble: ensembleVotes,
+            judge_error: 'PM_ESCALATION',
+            tie_break_path: 'pm-escalation',
+            tie_break_fourth_vendor: tbResult.fourthVendorSlug,
+            // A3 namespace split: skipped instance carries no failure_code —
+            // `undefined` so the aggregator excludes it from failure_distribution
+            // counts (mirrors `judge_verdict === undefined` semantics).
+            a3_failure_code: undefined,
+            a3_rationale: null,
+          };
+        }
+
+        // path === 'quadri-vendor' — decode back to structured verdict.
+        const [verdictStr, failureModeRaw] = tbResult.verdict.split('|');
+        const resolvedVerdict = (verdictStr as JudgeVerdict);
+        const resolvedFailureMode: FailureMode | null =
+          failureModeRaw === 'NA' ? null : (failureModeRaw as FailureMode);
+        return {
+          ...base,
+          judge_verdict: resolvedVerdict,
+          judge_failure_mode: resolvedFailureMode,
+          judge_rationale: `tie-break path=${tbResult.path} via ${tbResult.fourthVendorSlug ?? 'unknown'}`,
+          judge_model: 'ensemble_with_tiebreak',
+          judge_timestamp: new Date().toISOString(),
+          judge_ensemble: ensembleVotes,
+          tie_break_path: tbResult.path,
+          tie_break_fourth_vendor: tbResult.fourthVendorSlug,
+          // A3 namespace split: mirror resolved code into 8-value column.
+          a3_failure_code: mapLegacyToA3(resolvedFailureMode),
+          a3_rationale: null,
+        };
+      }
+      // Not a 1-1-1 split — fall through to legacy majority below.
+    }
+
+    return {
+      ...base,
+      judge_verdict: result.majority.verdict,
+      judge_failure_mode: result.majority.failure_mode,
+      judge_rationale: result.majority.rationale,
+      judge_model: result.majority.judge_model,
+      judge_timestamp: new Date().toISOString(),
+      judge_ensemble: result.ensemble.map(r => ({
+        model: r.judge_model,
+        verdict: r.verdict,
+        failure_mode: r.failure_mode,
+        rationale: r.rationale,
+      })),
+      // A3 namespace split: majority legacy code mirrors into 8-value column.
+      a3_failure_code: mapLegacyToA3(result.majority.failure_mode),
+      a3_rationale: null,
+    };
+  } catch (err) {
+    // Two failure classes both land here:
+    //   - JudgeParseError: judge LLM returned garbage twice in a row
+    //   - Transport / HTTP / timeout after all JudgeLlmClient retries
+    // Either way, we keep the run going. A Stage-2 batch that loses one
+    // judge call out of 200 should not abort; the aggregator treats
+    // `judge_verdict === undefined` as a skipped-judge instance and
+    // downgrades confidence in the per-cell rollup accordingly.
+    const message = err instanceof Error ? err.message : String(err);
+    const kind = err instanceof mod.JudgeParseError ? 'parse' : 'transport';
+    console.warn(
+      `[judge-runner] ${kind} failure — instance left unjudged (${message.slice(0, 200)})`,
+    );
+    return { ...base, judge_error: `${kind}: ${message.slice(0, 200)}` };
+  }
+}

benchmarks/harness/src/judge-types.ts

@@ -0,0 +1,32 @@
+/**
+ * Harness-local mirror of the public surface of
+ * `packages/server/src/benchmarks/judge/failure-mode-judge.ts`.
+ *
+ * Why a mirror: the harness tsconfig has `rootDir: "src"` and the judge
+ * module lives in a sibling workspace, so direct `import from '../../../…'`
+ * trips TS6059 ("File is not under rootDir"). Declaring the narrow
+ * interface surface here keeps TypeScript honest at build time while
+ * runtime imports still resolve against the real server module via the
+ * `.js` extension convention (vite-node / tsx follow the symlink to the
+ * .ts source).
+ *
+ * Keep this file in sync with the canonical definitions — if a new field
+ * or method is added there, mirror it here. The mirror carries only the
+ * types the harness imports; it intentionally does not re-export the
+ * concrete classes (JudgeParseError) or functions (judgeAnswer,
+ * judgeEnsemble) — those come from the real module at runtime.
+ */
+
+export type FailureMode = 'F1' | 'F2' | 'F3' | 'F4' | 'F5';
+export type Verdict = 'correct' | 'incorrect';
+
+export interface JudgeResult {
+  verdict: Verdict;
+  failure_mode: null | FailureMode;
+  rationale: string;
+  judge_model: string;
+}
+
+export interface LlmClient {
+  complete(prompt: string): Promise<string>;
+}

benchmarks/harness/src/llm.ts

@@ -0,0 +1,296 @@
+/**
+ * LLM client — routes through LiteLLM when configured, stubs deterministically
+ * when dryRun is true.
+ *
+ * The client deliberately accepts no tools. Each cell owns its own
+ * prompt-assembly logic (memory injection vs. not, evolved prompt vs. not)
+ * and hands the assembled prompt to this client as a single user turn. That
+ * keeps the cell logic unit-testable and the LLM client a thin transport.
+ */
+
+import type { ModelSpec } from './types.js';
+
+export interface LlmCallResult {
+  text: string;
+  inputTokens: number;
+  outputTokens: number;
+  latencyMs: number;
+  /** Dollar cost of this single call, computed from model pricing + tokens. */
+  costUsd: number;
+  /** null = OK, otherwise a short classification of the failure. */
+  failureMode: string | null;
+  /**
+   * Sprint 11 Task B1 (2026-04-22): captured chain-of-thought when the
+   * provider emits it under `thinking=on`. Parsed per H-AUDIT-1 ratification
+   * §Q3 precedence:
+   *   1. `body.choices[0].message.reasoning_content`   (DashScope native, primary)
+   *   2. `body.choices[0].message.reasoning`           (OpenRouter unified, current bridge)
+   *   3. `body.reasoning_content`                       (legacy top-level fallback)
+   * `undefined` when thinking is off or the provider omits the field.
+   * Per H-AUDIT-1 §2.4 exclusion rules, NEVER persisted to frames / memory /
+   * judge inputs — captured at the transport layer for JSONL + B1 smoke logs
+   * only.
+   */
+  reasoningContent?: string;
+  /**
+   * Sprint 11 Task A2 (2026-04-22): which shape yielded the reasoning. Enum
+   * values per ratification §Q3 — `'unknown'` signals thinking=on was
+   * requested but no reasoning field was present; undefined when thinking
+   * was off (no expectation). Consumed by the runner to emit a
+   * `reasoning_content_shape_unknown` observability event when drift is
+   * detected.
+   */
+  reasoningShape?: 'message.reasoning_content' | 'message.reasoning' | 'body.reasoning_content' | 'unknown';
+}
+
+export interface LlmCallInput {
+  model: ModelSpec;
+  systemPrompt: string;
+  userPrompt: string;
+  /** Abort the fetch after N ms. Default 30_000. */
+  timeoutMs?: number;
+  /**
+   * Sprint 11 Task B1 (2026-04-22): enable provider reasoning/thinking mode.
+   * Takes precedence over `model.stage2Config?.thinking`. Request body gets
+   * `reasoning: { enabled: true }` (OpenRouter unified shape) when true.
+   */
+  thinking?: boolean;
+  /**
+   * Sprint 11 Task B1 (2026-04-22): override request `max_tokens`. Takes
+   * precedence over `model.stage2Config?.maxTokens`. Default (no override)
+   * keeps the pre-existing 600 value — back-compat for non-Stage-2 cells.
+   */
+  maxTokensOverride?: number;
+}
+
+export interface LlmClient {
+  call(input: LlmCallInput): Promise<LlmCallResult>;
+}
+
+export function createLlmClient(opts: {
+  dryRun: boolean;
+  litellmUrl: string;
+  litellmApiKey: string;
+}): LlmClient {
+  if (opts.dryRun) return new DryRunClient();
+  return new LiteLlmClient(opts.litellmUrl, opts.litellmApiKey);
+}
+
+// ── Dry-run (deterministic echo) ───────────────────────────────────────────
+
+class DryRunClient implements LlmClient {
+  async call(input: LlmCallInput): Promise<LlmCallResult> {
+    // Return the expected span from the user prompt if present, else echo.
+    // The synthetic dataset embeds the answer in the context, so a smart
+    // "model" can extract it — our stub uses a trivial rule that's enough
+    // for the harness scaffold to verify end-to-end flow including accuracy
+    // scoring against the synthetic set.
+    const match = input.userPrompt.match(/Context:\s*([^\n]+)/);
+    const firstLine = match ? match[1].trim() : input.userPrompt.slice(0, 120);
+    const text = `DRY_RUN: ${firstLine}`;
+    const inputTokens = approximateTokenCount(input.systemPrompt) + approximateTokenCount(input.userPrompt);
+    const outputTokens = approximateTokenCount(text);
+    // Even in dry-run we record a "cost" so downstream aggregators
+    // exercise the cost path. Price comes from the model spec — in dry-run
+    // it's book-value, not wire-actual.
+    const costUsd =
+      (inputTokens / 1_000_000) * input.model.pricePerMillionInput +
+      (outputTokens / 1_000_000) * input.model.pricePerMillionOutput;
+    return {
+      text,
+      inputTokens,
+      outputTokens,
+      latencyMs: 1, // dry-run is instant
+      costUsd,
+      failureMode: null,
+    };
+  }
+}
+
+// ── LiteLLM proxy ──────────────────────────────────────────────────────────
+
+/**
+ * Sprint 12 Task 2.5 Stage 1.5 §7.1 — fetch-retry on TypeError.
+ *
+ * The v2 full-context cell exhibited 100% `fetch_error_TypeError` at ~1 ms
+ * latency per instance (see sessions/2026-04-23-task25-s0-v2-fullcontext-
+ * forensic.md). Root cause: concurrent runner processes saturating the
+ * OpenRouter bridge / libuv thread pool. A single retry with a 1 s backoff
+ * absorbs transient saturation on normal ops (~1% of rows per PM estimate).
+ *
+ * Retry ONLY on `fetch_error_TypeError`. All other failure modes (`timeout`
+ * via AbortError, `http_5xx`, other error classes) return immediately — those
+ * aren't bridge-saturation patterns and retry can't help.
+ *
+ * Retry count is a module constant (default 1) and backoff is a module const
+ * (default 1000 ms). Tuning is intentional: more retries add per-row worst-
+ * case latency; more aggressive backoff adds wall-clock to the whole run.
+ */
+const FETCH_RETRY_MAX = 1;
+const FETCH_RETRY_BACKOFF_MS = 1000;
+
+class LiteLlmClient implements LlmClient {
+  constructor(private url: string, private apiKey: string) {}
+
+  async call(input: LlmCallInput): Promise<LlmCallResult> {
+    const overallStarted = Date.now();
+    let lastResult: LlmCallResult | undefined;
+    for (let attempt = 0; attempt <= FETCH_RETRY_MAX; attempt++) {
+      if (attempt > 0) {
+        await new Promise<void>(resolve => setTimeout(resolve, FETCH_RETRY_BACKOFF_MS));
+      }
+      const result = await this.attemptOnce(input);
+      lastResult = result;
+      if (result.failureMode !== 'fetch_error_TypeError') {
+        // Success or non-retryable failure — return with total wall-clock
+        // latency (including any backoff + prior attempts). Retrying a
+        // non-TypeError would both waste budget and invalidate the latency
+        // metric's meaning as "time to first clean signal."
+        if (attempt > 0) {
+          return { ...result, latencyMs: Date.now() - overallStarted };
+        }
+        return result;
+      }
+    }
+    // All retries exhausted. Return last result with total wall-clock.
+    return lastResult
+      ? { ...lastResult, latencyMs: Date.now() - overallStarted }
+      : {
+          text: '',
+          inputTokens: 0,
+          outputTokens: 0,
+          latencyMs: Date.now() - overallStarted,
+          costUsd: 0,
+          failureMode: 'fetch_error_TypeError',
+        };
+  }
+
+  /** One attempt — the pre-Stage-1.5 `call` body unchanged. Returns an
+   *  LlmCallResult (success or failure) rather than throwing so the outer
+   *  retry loop can read `failureMode` to decide whether to retry. */
+  private async attemptOnce(input: LlmCallInput): Promise<LlmCallResult> {
+    const started = Date.now();
+    const controller = new AbortController();
+    // Sprint 11 B1: thinking=on on Stage 2 config pushes avg latency up to
+    // ~18s (Task 1.1 measured). Widen default timeout to 180s so a single
+    // reasoning-heavy call doesn't abort mid-response. Callers can still
+    // pass a tighter timeoutMs when needed.
+    const timer = setTimeout(() => controller.abort(), input.timeoutMs ?? 180_000);
+    // Resolve thinking + max_tokens: per-call input > model.stage2Config > defaults.
+    const stage2 = input.model.stage2Config;
+    const thinkingEnabled = input.thinking ?? stage2?.thinking ?? false;
+    const maxTokens = input.maxTokensOverride ?? stage2?.maxTokens ?? 600;
+    const requestBody: Record<string, unknown> = {
+      model: input.model.litellmModel,
+      messages: [
+        { role: 'system', content: input.systemPrompt },
+        { role: 'user', content: input.userPrompt },
+      ],
+      max_tokens: maxTokens,
+      temperature: 0.0,
+    };
+    if (thinkingEnabled) {
+      // OpenRouter unified reasoning API — LiteLLM with drop_params=true will
+      // pass this through to OpenRouter unchanged. DashScope-intl native
+      // accepts a different shape (enable_thinking); LiteLLM normalizes
+      // either way when routed through its provider adapter. If the provider
+      // is one that doesn't support reasoning, drop_params strips silently.
+      requestBody.reasoning = { enabled: true };
+    }
+    try {
+      const res = await fetch(`${this.url.replace(/\/$/, '')}/v1/chat/completions`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          Authorization: `Bearer ${this.apiKey}`,
+        },
+        signal: controller.signal,
+        body: JSON.stringify(requestBody),
+      });
+      const latencyMs = Date.now() - started;
+      if (!res.ok) {
+        return {
+          text: '',
+          inputTokens: 0,
+          outputTokens: 0,
+          latencyMs,
+          costUsd: 0,
+          failureMode: `http_${res.status}`,
+        };
+      }
+      const body = await res.json() as {
+        choices?: Array<{
+          message?: {
+            content?: string;
+            reasoning?: string;           // OpenRouter unified shape
+            reasoning_content?: string;   // DashScope native (message-level)
+          };
+        }>;
+        reasoning_content?: string;        // DashScope legacy top-level
+        usage?: { prompt_tokens?: number; completion_tokens?: number };
+      };
+      const msg = body.choices?.[0]?.message;
+      const text = msg?.content ?? '';
+      // Sprint 11 A2: parser precedence per H-AUDIT-1 ratification §Q3.
+      // Primary = DashScope native `message.reasoning_content`.
+      // Secondary = OpenRouter unified `message.reasoning`.
+      // Tertiary = legacy top-level `body.reasoning_content`.
+      // Unknown = thinking was requested but no field was present — emit
+      // observability signal so provider schema drift becomes visible.
+      let reasoningContent: string | undefined;
+      let reasoningShape: LlmCallResult['reasoningShape'];
+      if (msg?.reasoning_content !== undefined) {
+        reasoningContent = msg.reasoning_content;
+        reasoningShape = 'message.reasoning_content';
+      } else if (msg?.reasoning !== undefined) {
+        reasoningContent = msg.reasoning;
+        reasoningShape = 'message.reasoning';
+      } else if (body.reasoning_content !== undefined) {
+        reasoningContent = body.reasoning_content;
+        reasoningShape = 'body.reasoning_content';
+      } else if (thinkingEnabled) {
+        // thinking=on was requested but no reasoning surface present.
+        // `reasoningContent` stays undefined; `reasoningShape` = 'unknown'
+        // surfaces the drift for the runner to log.
+        reasoningShape = 'unknown';
+      }
+      const inputTokens = body.usage?.prompt_tokens ?? approximateTokenCount(input.systemPrompt + input.userPrompt);
+      const outputTokens = body.usage?.completion_tokens ?? approximateTokenCount(text);
+      const costUsd =
+        (inputTokens / 1_000_000) * input.model.pricePerMillionInput +
+        (outputTokens / 1_000_000) * input.model.pricePerMillionOutput;
+      return {
+        text,
+        inputTokens,
+        outputTokens,
+        latencyMs,
+        costUsd,
+        failureMode: null,
+        ...(reasoningContent !== undefined && { reasoningContent }),
+        ...(reasoningShape !== undefined && { reasoningShape }),
+      };
+    } catch (err) {
+      const latencyMs = Date.now() - started;
+      const name = (err as Error).name;
+      const failureMode = name === 'AbortError' ? 'timeout' : `fetch_error_${name}`;
+      return {
+        text: '',
+        inputTokens: 0,
+        outputTokens: 0,
+        latencyMs,
+        costUsd: 0,
+        failureMode,
+      };
+    } finally {
+      clearTimeout(timer);
+    }
+  }
+}
+
+// ── Helpers ────────────────────────────────────────────────────────────────
+
+/** Rough token estimate (chars / 4). Used when the LLM response doesn't
+ *  include usage info (e.g. in dry-run or certain proxy setups). */
+export function approximateTokenCount(s: string): number {
+  return Math.max(1, Math.ceil(s.length / 4));
+}

benchmarks/harness/src/metrics.ts

@@ -0,0 +1,188 @@
+/**
+ * Metrics + JSONL writer.
+ *
+ * Each instance run emits one JSONL record. The record shape is intentionally
+ * flat so downstream tools (jq, DuckDB, pandas) don't need unnesting. At the
+ * end of a run we also write an aggregate summary JSON alongside the JSONL.
+ */
+
+import fs from 'node:fs';
+import path from 'node:path';
+import type { AggregateSummary, JsonlRecord, RunConfig } from './types.js';
+import {
+  computeFailureDistribution,
+  type FailureRow,
+} from './failure-taxonomy/index.js';
+
+/**
+ * Sprint 11 Task A2 — read-path pruning per H-AUDIT-1 ratification §Q4.
+ *
+ * Write-path always persists full records (incl. `reasoning_content`). The
+ * exclusion contract (design doc §2.4) is enforced on the READ side: any
+ * caller that might surface reasoning to a judge, UI, MCP payload, or
+ * summary brief must pass `{ includeReasoning: false }` so the field is
+ * stripped at the boundary.
+ *
+ * Default is `includeReasoning: false` — callers opt in explicitly when
+ * they need the raw trace (e.g. for archival gzip, audit replay).
+ */
+export interface ReadJsonlOptions {
+  /** When false (default), strips `reasoning_content` from each record.
+   *  `reasoning_content_chars` and `reasoning_shape` are lightweight
+   *  observability fields and are retained either way. */
+  includeReasoning?: boolean;
+}
+
+export function readJsonl(filePath: string, options: ReadJsonlOptions = {}): JsonlRecord[] {
+  const includeReasoning = options.includeReasoning ?? false;
+  if (!fs.existsSync(filePath)) return [];
+  const raw = fs.readFileSync(filePath, 'utf-8');
+  const records: JsonlRecord[] = [];
+  for (const line of raw.split('\n')) {
+    const trimmed = line.trim();
+    if (trimmed.length === 0) continue;
+    const record = JSON.parse(trimmed) as JsonlRecord;
+    if (!includeReasoning && record.reasoning_content !== undefined) {
+      // Strip the content; keep the chars + shape observability fields.
+      const { reasoning_content: _stripped, ...rest } = record;
+      records.push(rest as JsonlRecord);
+    } else {
+      records.push(record);
+    }
+  }
+  return records;
+}
+
+/** Scores a model output against expected substrings (any-match = full credit). */
+export function scoreAccuracy(output: string, expected: string[]): number {
+  if (expected.length === 0) return 0;
+  const lower = output.toLowerCase();
+  for (const exp of expected) {
+    if (lower.includes(exp.toLowerCase())) return 1;
+  }
+  return 0;
+}
+
+/** p50 / p95 helpers. Returns 0 on empty input rather than NaN so JSONL
+ *  consumers don't have to special-case an empty batch. */
+export function percentile(values: number[], p: number): number {
+  if (values.length === 0) return 0;
+  const sorted = values.slice().sort((a, b) => a - b);
+  const idx = Math.min(sorted.length - 1, Math.floor((p / 100) * sorted.length));
+  return sorted[idx];
+}
+
+export class JsonlWriter {
+  private stream: fs.WriteStream;
+  private records: JsonlRecord[] = [];
+
+  constructor(private outputPath: string) {
+    fs.mkdirSync(path.dirname(outputPath), { recursive: true });
+    this.stream = fs.createWriteStream(outputPath, { flags: 'a' });
+  }
+
+  write(record: JsonlRecord): void {
+    this.stream.write(JSON.stringify(record) + '\n');
+    this.records.push(record);
+  }
+
+  all(): JsonlRecord[] {
+    return this.records.slice();
+  }
+
+  async close(): Promise<void> {
+    // Wait for BOTH finish (all writes flushed to kernel) AND close (file
+    // descriptor released). end(callback) only guarantees finish — on
+    // Windows the fd release lags, which races test cleanup rmSync.
+    await new Promise<void>((resolve, reject) => {
+      this.stream.once('close', () => resolve());
+      this.stream.once('error', reject);
+      this.stream.end();
+    });
+  }
+}
+
+export function buildAggregate(config: RunConfig, records: JsonlRecord[], startedAt: string, finishedAt: string, budgetStoppedAt: number | null): AggregateSummary {
+  const completed = records.filter(r => r.failure_mode === null);
+  const failed = records.filter(r => r.failure_mode !== null);
+  const latencies = records.map(r => r.p50_latency_ms).filter(v => v > 0);
+  const p95Latencies = records.map(r => r.p95_latency_ms).filter(v => v > 0);
+  const totalUsd = records.reduce((s, r) => s + r.usd_per_query, 0);
+  const meanAccuracy = records.length === 0 ? 0 : completed.reduce((s, r) => s + r.accuracy, 0) / records.length;
+  const failureModes: Record<string, number> = {};
+  for (const r of failed) {
+    const key = r.failure_mode ?? 'unknown';
+    failureModes[key] = (failureModes[key] ?? 0) + 1;
+  }
+
+  // Sprint 11 A2: reasoning_content aggregates when any record carries it.
+  // `undefined` when no records had reasoning — lets consumers distinguish
+  // "thinking was off" from "zero chars observed". Chars only, never content
+  // (design doc §2.4 exclusion rule).
+  const reasoningChars = records
+    .filter(r => r.reasoning_content_chars !== undefined && r.reasoning_content_chars > 0)
+    .map(r => r.reasoning_content_chars as number);
+  const shapeDistribution: Record<string, number> = {};
+  for (const r of records) {
+    if (r.reasoning_shape !== undefined) {
+      shapeDistribution[r.reasoning_shape] = (shapeDistribution[r.reasoning_shape] ?? 0) + 1;
+    }
+  }
+  const reasoningAggregate = reasoningChars.length === 0 && Object.keys(shapeDistribution).length === 0
+    ? undefined
+    : {
+        count: reasoningChars.length,
+        sumChars: reasoningChars.reduce((s, n) => s + n, 0),
+        p50Chars: Math.round(percentile(reasoningChars, 50)),
+        p95Chars: Math.round(percentile(reasoningChars, 95)),
+        shapeDistribution,
+      };
+
+  // Sprint 12 Task 2 §2.1 A3 namespace split (LOCKED 2026-04-23): compute
+  // the A3 LOCK § 6 failure distribution from the `a3_failure_code` /
+  // `a3_rationale` columns. Only rows that carry the A3 column are
+  // included (pre-Sprint-12 rows and skipped-judge rows are excluded). The
+  // aggregate section stays `undefined` when no A3 rows exist so
+  // pre-A3 runs continue emitting the legacy shape verbatim.
+  const a3Rows: FailureRow[] = records
+    .filter(r => r.a3_failure_code !== undefined)
+    .map(r => ({
+      failure_code: r.a3_failure_code!,
+      rationale: r.a3_rationale ?? null,
+    }));
+  const failureDistribution =
+    a3Rows.length === 0 ? undefined : computeFailureDistribution(a3Rows);
+
+  return {
+    run: {
+      kind: config.run.kind,
+      name: config.run.name,
+      dataset: config.dataset.id,
+      model: config.model.id,
+      seed: config.seed,
+      startedAt,
+      finishedAt,
+    },
+    counts: {
+      total: records.length,
+      completed: completed.length,
+      failed: failed.length,
+      budgetStoppedAt,
+    },
+    metrics: {
+      meanAccuracy: round(meanAccuracy, 4),
+      p50LatencyMs: round(percentile(latencies, 50), 2),
+      p95LatencyMs: round(percentile(p95Latencies, 95), 2),
+      totalUsd: round(totalUsd, 6),
+      meanUsdPerQuery: records.length === 0 ? 0 : round(totalUsd / records.length, 6),
+    },
+    failureModes,
+    ...(reasoningAggregate && { reasoningContent: reasoningAggregate }),
+    ...(failureDistribution && { failure_distribution: failureDistribution }),
+  };
+}
+
+function round(n: number, decimals: number): number {
+  const f = Math.pow(10, decimals);
+  return Math.round(n * f) / f;
+}