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

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+"""
+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)