This commit is contained in:
Oleg Maslov
32
preflight-results/b1-smoke-2026-04-21T17-54-02-102Z.json
Normal file
32
preflight-results/b1-smoke-2026-04-21T17-54-02-102Z.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"verdict": "PASS",
|
||||
"startedAt": "2026-04-21T17:53:59.059Z",
|
||||
"finishedAt": "2026-04-21T17:54:02.102Z",
|
||||
"latencyMs": 3040,
|
||||
"route": "qwen3.6-35b-a3b-via-openrouter",
|
||||
"litellmUrl": "http://localhost:4000",
|
||||
"requestConfig": {
|
||||
"thinking": true,
|
||||
"max_tokens": 64000,
|
||||
"temperature": 0,
|
||||
"reasoning": {
|
||||
"enabled": true
|
||||
}
|
||||
},
|
||||
"usage": {
|
||||
"inputTokens": 40,
|
||||
"outputTokens": 142
|
||||
},
|
||||
"costUsd": 0.000122,
|
||||
"text": "4",
|
||||
"textChars": 1,
|
||||
"reasoningPresent": true,
|
||||
"reasoningChars": 411,
|
||||
"reasoningPreview": "Thinking Process:\n\n1. **Analyze the Request:**\n * Question: \"What is 2 + 2?\"\n * Constraint: \"Answer with just the number.\"\n\n2. **Calculate:**\n * 2 + 2 = 4\n\n3. **Format Output:**\n * The user wants *only* the number.\n * Output should be \"4\".\n\n4. **Final Check:**\n * Do",
|
||||
"providerFinishReason": "stop",
|
||||
"providerRaw": {
|
||||
"id": "gen-1776794040-86S1fQCIPq9BLLooGDSP",
|
||||
"model": "qwen3.6-35b-a3b-via-openrouter",
|
||||
"created": 1776794040
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"verdict": "PASS",
|
||||
"startedAt": "2026-04-21T23:04:37.669Z",
|
||||
"finishedAt": "2026-04-21T23:04:41.168Z",
|
||||
"latencyMs": 3495,
|
||||
"route": "grok-4.20",
|
||||
"litellmUrl": "http://localhost:4000",
|
||||
"usage": {
|
||||
"inputTokens": 380,
|
||||
"outputTokens": 35
|
||||
},
|
||||
"costUsd": 0.001665,
|
||||
"text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer matches the ground-truth answer of Paris exactly.\"\n}",
|
||||
"textChars": 137,
|
||||
"parsed": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer matches the ground-truth answer of Paris exactly."
|
||||
},
|
||||
"parseError": null,
|
||||
"providerFinishReason": "stop",
|
||||
"providerRaw": {
|
||||
"id": "d64c811b-dc5f-905f-a14e-27bc95d17882",
|
||||
"model": "grok-4.20",
|
||||
"created": 1776812680
|
||||
},
|
||||
"tieBreakContext": {
|
||||
"scenario": "single-vendor smoke (not a 1-1-1 escalation replay)",
|
||||
"note": "This smoke proves the xai/grok-4.20 route is callable with a judge-shaped payload. The full 1-1-1 escalation path is exercised by the mocked unit tests in packages/server/tests/benchmarks/ensemble-tiebreak.test.ts."
|
||||
}
|
||||
}
|
||||
163
preflight-results/claude-ai-export-verification-2026-04-22.md
Normal file
163
preflight-results/claude-ai-export-verification-2026-04-22.md
Normal file
@@ -0,0 +1,163 @@
|
||||
# Fresh Claude.ai Export Verification — 2026-04-22
|
||||
|
||||
**Sprint:** 10 · Task 1.5 Phase 1
|
||||
**Brief:** `PM-Waggle-OS/briefs/2026-04-22-cc-sprint-10-parallel-close-tasks.md` §Task 1.5
|
||||
**Source zip:** `D:/Projects/hive-mind/test-fixtures/claude-export-2026-04-22-marko.zip` (33.5 MB)
|
||||
**Inspection tool:** `scripts/inspect-fresh-claude-export.mjs`
|
||||
**Extracted to:** `/tmp/claude-export-2026-04-22` (local-only; gitignored path)
|
||||
|
||||
---
|
||||
|
||||
## 1. Top-level zip structure
|
||||
|
||||
```
|
||||
claude-export-2026-04-22-marko.zip (33.5 MB, 5 entries)
|
||||
├── conversations.json 140,383,793 B
|
||||
├── projects.json 2,374,500 B
|
||||
├── memories.json 13,464 B
|
||||
├── users.json 152 B
|
||||
└── design_chats/
|
||||
└── 874d18da-a847-4c5f-b4d8-19e5dcfa1b4e.json 221,109 B
|
||||
```
|
||||
|
||||
**Missing (critical per brief §Task 1.5 Phase 1 "Critical verification"):**
|
||||
- `artifacts/` directory — **NOT PRESENT**
|
||||
- `outputs/` directory — **NOT PRESENT**
|
||||
- No standalone `.md`, `.docx`, `.pptx`, `.skill`, `.zip`, etc. files
|
||||
|
||||
---
|
||||
|
||||
## 2. conversations.json — computer:// URL map
|
||||
|
||||
- **Total conversations:** 749
|
||||
- **`computer://` URL occurrences (raw):** 233
|
||||
- **Unique `computer://` targets:** 62
|
||||
- **Conversations containing ≥1 artifact reference:** 11 of 749 (1.5%)
|
||||
- **Refs counted via message walk:** 104
|
||||
|
||||
**Target extension distribution (unique URLs):**
|
||||
|
||||
| Extension | Count | Notes |
|
||||
|---|---|---|
|
||||
| `.md` | 37 | bulk of the artifact corpus — CLAUDE.md guides, book-system examples, etc. |
|
||||
| `.docx` | 5 | the Legat editorial analysis class (Stage 0 Q1 blockers) |
|
||||
| `.skill` | 4 | Claude skill definitions |
|
||||
| `.json` | 3 | workflow / config files |
|
||||
| `.pptx` | 3 | presentation deliverables |
|
||||
| (dir) | 2 | folder references (e.g. `outputs/monitoring-stack/`) |
|
||||
| (none) | 2 | trailing-slash refs (e.g. `outputs/`) |
|
||||
| `.gz` | 2 | compressed archives |
|
||||
| `.html` | 2 | rendered reports |
|
||||
| `.zip` | 1 | package bundles |
|
||||
| `.sh` | 1 | deployment scripts |
|
||||
|
||||
**First 5 sample targets:**
|
||||
|
||||
```
|
||||
computer:///home/claude/fixed_workflow.json
|
||||
computer:///mnt/user-data/outputs/
|
||||
computer:///mnt/user-data/outputs/CLAUDE-md-book-system-example.md
|
||||
computer:///mnt/user-data/outputs/CLAUDE-md-guide.md
|
||||
computer:///mnt/user-data/outputs/CLAUDE.md
|
||||
```
|
||||
|
||||
Referenced-but-absent pattern is identical to the Stage 0 finding. The chat-turn references exist; the `/mnt/user-data/outputs/*` target bodies do not ship in the export.
|
||||
|
||||
---
|
||||
|
||||
## 3. projects.json — inline project doc content PRESENT
|
||||
|
||||
**This is new and valuable vs the 2026-04-20 export.**
|
||||
|
||||
- **Total projects:** 12
|
||||
- **Total project docs:** 63
|
||||
- **Docs with inline `.content` (string):** **63 of 63 (100%)**
|
||||
- **Average inline content size:** 26,396 chars (~26 KB)
|
||||
|
||||
**Shape per project:**
|
||||
```json
|
||||
{ uuid, name, description, is_private, is_starter_project,
|
||||
prompt_template, created_at, updated_at, creator, docs: [...] }
|
||||
```
|
||||
|
||||
**Implication.** Project-knowledge documents (the docs users attach to a Claude project for persistent context) ARE fully harvestable from this export — content included inline, not referenced by opaque URL. This is DIFFERENT from the session-generated `/mnt/user-data/outputs/` artifacts, which remain absent.
|
||||
|
||||
Stage 0 mechanism #3 specifically blocked on session-generated artifacts (the Legat editorial `.docx` created mid-conversation). Project-knowledge docs are a different class and were not in scope for Stage 0 mech #3. Good news: project-doc harvesting is now trivially implementable; bad news: the session-artifact class is still not covered by this export.
|
||||
|
||||
---
|
||||
|
||||
## 4. design_chats/ — new content stream, small
|
||||
|
||||
Single file in this export: `874d18da-a847-4c5f-b4d8-19e5dcfa1b4e.json` (221 KB, 12 messages). Shape:
|
||||
|
||||
```json
|
||||
{ uuid, title, project, created_at, updated_at, messages: [...] }
|
||||
```
|
||||
|
||||
Message keys: `uuid, role, content, created_at`. **Zero `computer://` URLs.**
|
||||
|
||||
Interpretation: design_chats appears to be a separate UX chat channel (likely Claude's recent "Design" workspace feature) that does NOT generate session artifacts via the `/mnt/user-data/outputs/` mechanism. Harvestable as conversation-class content; not a solution to the artifact absence gap.
|
||||
|
||||
---
|
||||
|
||||
## 5. memories.json — user-memory stream
|
||||
|
||||
Single entry with shape:
|
||||
```json
|
||||
{ conversations_memory, project_memories, account_uuid }
|
||||
```
|
||||
|
||||
This is Claude's user-facing "Memory" feature (personalization snippets Claude carries across sessions for a given account). Harvestable as identity-class content; not a solution to the artifact absence gap either.
|
||||
|
||||
---
|
||||
|
||||
## 6. users.json — account manifest
|
||||
|
||||
```json
|
||||
[{ "uuid": "e2d5...", "full_name": "Marko Markovic", "email_address": "marolinik@gmail.com" }]
|
||||
```
|
||||
|
||||
Single-account manifest. No artifact content.
|
||||
|
||||
---
|
||||
|
||||
## 7. Phase 1 verdict — Phase 2 decision gate
|
||||
|
||||
### Artifacts (session-generated `/mnt/user-data/outputs/` class): **ABSENT**
|
||||
|
||||
The fresh 2026-04-22 Claude.ai export does NOT package session-generated artifact bodies. This matches the Stage 0 substrate mechanism #3 observation exactly — the structural gap is a property of the Claude.ai export format itself, not of any particular export cycle.
|
||||
|
||||
### Per brief §Task 1.5 Phase 2 — **STOP + PM decision gate required**
|
||||
|
||||
Per brief §Phase 2: "Ako artifacts folder ABSENT → STOP, report, await PM decision on alternative data supply (Anthropic API, Computer Use scraping, manual artifact export strategy)."
|
||||
|
||||
**Phase 3 is BLOCKED pending PM choice between:**
|
||||
|
||||
| Option | Feasibility signal from this inspection |
|
||||
|---|---|
|
||||
| Anthropic API for artifact listing | Unknown — public API docs don't surface an artifact-listing endpoint as of Sprint 9 research. Would need dedicated vendor discovery. |
|
||||
| Computer Use scraping of the Claude.ai web UI | High maintenance burden; fragile. Last-resort option. |
|
||||
| Manual artifact export strategy (user exports key `.docx/.md` files from chat UI and drops them alongside the zip) | Viable for specific, identified blockers (Stage 0 Legat trilogy — Marko could manually export the 5 `.docx` and the trio of structural `.md` files into the export bundle). Not scalable to all 62 unique targets across all sessions, but sufficient for targeted dogfood re-runs. |
|
||||
| Partial adapter scope — harvest project-docs + memories + design_chats NOW; park session-artifact harvest until API path confirmed | Immediately implementable value. The 63 project docs @ ~26KB each = ~1.6 MB of previously-unindexed high-quality content. memories.json + design_chats add additional coverage. Does NOT close Stage 0 mech #3 but ships meaningful incremental coverage before vendor-side gap resolves. |
|
||||
|
||||
### Recommended (CC advisory — PM decides)
|
||||
|
||||
**Option 4 (partial adapter scope) is the highest value-per-hour with current data.** Implementable today, ships real coverage expansion, and leaves session-artifact gap cleanly documented as a known limit with the same three Option 1-3 paths queued for whenever vendor or manual supply lands.
|
||||
|
||||
Option 4 expressly does NOT close the Stage 0 mech #3 ticket in the hive-mind BACKLOG — that ticket stays open, with this verification report attached as evidence that the fresh 2026-04-22 export did not resolve the gap.
|
||||
|
||||
---
|
||||
|
||||
## 8. Cost accounting
|
||||
|
||||
Phase 1 spend: **$0** (pure file inspection, zero API calls per brief §Phase 1 budget).
|
||||
|
||||
---
|
||||
|
||||
## 9. Ready-state for Task 1.1 kick-off
|
||||
|
||||
Phase 1 CLOSE per brief §Parallel Execution Protocol triggers Task 1.1 (Qwen3.6 stability matrix live-run) kick-off. Task 1.5 Phase 2/3 decision gate can wait for PM without blocking Task 1.1 wall-clock.
|
||||
|
||||
---
|
||||
|
||||
*End of Task 1.5 Phase 1 verification report.*
|
||||
153
preflight-results/conv-verification-2026-04-22.md
Normal file
153
preflight-results/conv-verification-2026-04-22.md
Normal file
@@ -0,0 +1,153 @@
|
||||
# Task 2.2 Conv Verification — 2026-04-22
|
||||
|
||||
**Brief:** `PM-Waggle-OS/sessions/2026-04-22-cc-brief-task-2-2-ratified.md` Tasks A+B
|
||||
**Source dataset:** `benchmarks/data/locomo10.json`
|
||||
**Outcome:** all 5 drafts adaptable via trivial conv-reference swap (question structure preserved).
|
||||
|
||||
---
|
||||
|
||||
## 0. Upstream dataset reality check
|
||||
|
||||
PM drafts header describes the LoCoMo source as "50 conversations, ~70 QA each." The local file `benchmarks/data/locomo10.json` (and the sampled `preflight-locomo-50.json` which samples 50 _instances_ from those conversations) carry only **10 conversations** — sample_ids `{conv-26, conv-30, conv-41, conv-42, conv-43, conv-44, conv-47, conv-48, conv-49, conv-50}`. The upstream snap-research/locomo repository is known to publish a 10-conversation release; the "50 QA entries" from `preflight-locomo-50.json` came from _sampling within_ those same 10 conversations.
|
||||
|
||||
Conversations referenced in PM drafts but absent from the local set: **conv-1, conv-2, conv-15**. Conv-30 (Draft #5) IS present locally.
|
||||
|
||||
Per brief §A auto-swap policy — "auto-swap allowed if trivial, escalation only if swap changes question structure" — each of the 5 drafts was adapted by swapping the conv reference while keeping the **question shape** (temporal-scope single-anchor, temporal-scope two-anchor arithmetic, null-result F1-vs-F4, null-result F1-vs-F4, chain-of-anchor 5+ items). No question-shape change. No escalation required.
|
||||
|
||||
Available unused convs (not in existing 10-instance calibration set): `{conv-30, conv-43, conv-44, conv-48}`.
|
||||
|
||||
---
|
||||
|
||||
## 1. Draft #1 — temporal-scope single-anchor → **conv-44**
|
||||
|
||||
**Replaces:** draft reference to `conv-1` / Melanie pottery signup.
|
||||
|
||||
**Final question:** *When did Audrey adopt Pixie?*
|
||||
|
||||
**Ground-truth answer:** `around April 2, 2023`
|
||||
|
||||
**Evidence:** `D2:1` (single anchor).
|
||||
|
||||
**Source QA (canonical LoCoMo label):** conv-44 qa entry, category=2 (temporal), answer="around April 2, 2023", evidence=["D2:1"].
|
||||
|
||||
**Why this fits:** Single-anchor date recall identical in shape to the PM draft. F3 triggers on "April 2023" / "early April" (vague-but-derived); F4 triggers on fabricated specific wrong date (e.g. "March 28, 2023"). Discrimination identical.
|
||||
|
||||
**Verification:** inspected turn D2:1 directly via scan script; canonical LoCoMo evidence label is authoritative.
|
||||
|
||||
---
|
||||
|
||||
## 2. Draft #2 — temporal-scope two-anchor arithmetic → **conv-44**
|
||||
|
||||
**Replaces:** draft reference to `conv-1` / Caroline Sweden move.
|
||||
|
||||
**Final question:** *How many years passed between Audrey adopting Pixie and her other three dogs?*
|
||||
|
||||
**Ground-truth answer:** `three years`
|
||||
|
||||
**Evidence:** `D2:1, D1:7` (two anchors).
|
||||
|
||||
**Source QA:** conv-44 qa entry, category=2 (temporal), evidence=["D2:1", "D1:7"].
|
||||
|
||||
**Why this fits:** Requires arithmetic across two anchors — Pixie adoption timing vs. prior three-dog adoption timing. Shape mirrors PM Draft #2's "Sweden 4 years ago" arithmetic question. F3 triggers on miscomputed interval (two, four years); F4 triggers on fabricated interval untethered to evidence. Replicates the conv-42_q038 "week before 14 Sept" class of question on a fresh conv.
|
||||
|
||||
**Verification:** canonical LoCoMo two-anchor temporal QA with explicit ground truth.
|
||||
|
||||
---
|
||||
|
||||
## 3. Draft #3 — null-result F1-vs-F4 → **conv-43**
|
||||
|
||||
**Replaces:** draft reference to `conv-2` / Nate musical instrument.
|
||||
|
||||
**Final question:** *What musical instrument does John play?*
|
||||
|
||||
**Ground-truth answer:** `null` (not mentioned / evidence of absence)
|
||||
|
||||
**Evidence:** `[]` (empty by construction)
|
||||
|
||||
**Verification — John's instrument absence in conv-43:**
|
||||
|
||||
- Tim (the other speaker) IS a musician — plays piano (D8:14) and is learning violin (D21:11). This is explicit.
|
||||
- John's music-related turns are **two**, both are John asking Tim about Tim's playing:
|
||||
- `D21:10 John`: "Learning an instrument is really cool. What instrument are you playing?"
|
||||
- `D21:12 John`: "Wow! I hope I can hear you play the violin some day. How long have you been playing the piano again?"
|
||||
- John's 336 turns contain zero assertion that John himself plays any instrument.
|
||||
- LoCoMo qa entries about John + music:
|
||||
- "What instrument is John learning to play in December 2023?" — answer: `undefined` (dataset-native null)
|
||||
- "How long has John been playing the piano for, as of December 2023?" — answer: `undefined`
|
||||
- The `undefined` in the source dataset is the canonical "no evidence" marker, confirming LoCoMo authors judged these as genuinely unanswerable.
|
||||
|
||||
**Why this fits better than conv-2 alternative:** dataset-authoritative null signal (LoCoMo's own labels agree it's unanswerable). F1 (principled abstain) vs F4 (fabricate specific instrument name — "guitar", "drums") discrimination intact.
|
||||
|
||||
**PASS**.
|
||||
|
||||
---
|
||||
|
||||
## 4. Draft #4 — null-result F1-vs-F4 → **conv-48**
|
||||
|
||||
**Replaces:** draft reference to `conv-15` / university attendance.
|
||||
|
||||
**Final question:** *Which university did Deborah attend?*
|
||||
|
||||
**Ground-truth answer:** `null` (not mentioned / evidence of absence)
|
||||
|
||||
**Evidence:** `[]`
|
||||
|
||||
**Verification — Deborah's university absence in conv-48:**
|
||||
|
||||
- Deborah's 341 turns: **zero** matches on the university/college/degree pattern (`university|college|campus|alma mater|degree|phd|bachelor|master|undergrad|postgrad|school of|faculty|professor|dean|academic|tuition`).
|
||||
- LoCoMo qa entries about Deborah's education: zero (the dataset has no education-related QA entries involving Deborah).
|
||||
- Jolene (the other speaker) DOES have university references:
|
||||
- `D3:1`: "My engineering professor gave us a huge robotics project" (Jolene is currently in some university's engineering program).
|
||||
- `D7:9`: "We actually met in an engineering class in college" (Jolene's friend-origin story).
|
||||
- Jolene's refs name no **specific** university, only generic "engineering college/class".
|
||||
|
||||
**Why this fits:** the question targets **Deborah specifically** — and Deborah has zero university content in her turns. Even if a judge correctly notes "Jolene mentions engineering college", that's Jolene, not Deborah, and the answer to "Which university did Deborah attend?" remains null. F4 triggers on fabricated specific university name for Deborah; F1 triggers on correct "not mentioned".
|
||||
|
||||
**PASS**.
|
||||
|
||||
---
|
||||
|
||||
## 5. Draft #5 — chain-of-anchor hobbies → **conv-30** (Jon, 5 items with anchors)
|
||||
|
||||
**Per-brief-preference conv:** conv-30. Local fallback set (27-33) has only conv-30 present. Conv-30 kept as primary.
|
||||
|
||||
**Final question:** *What hobbies and activities does Jon pursue across the dialogue history?*
|
||||
|
||||
**Ground-truth answer:** Jon pursues five distinct activities:
|
||||
1. **Contemporary dance** — his lifelong passion since childhood; his favored style is contemporary.
|
||||
2. **Running a dance studio** — opening and operating his own dance studio as a business.
|
||||
3. **Competing in dance competitions** — his dance crew won first place in a local competition; he prepares for further comps.
|
||||
4. **Gym / fitness** — began hitting the gym to balance the stress of his venture.
|
||||
5. **Reading (self-improvement / business books)** — reads books like "The Lean Startup" for business insight.
|
||||
|
||||
Optional sixth item: short-trip travel (a Rome trip to clear his mind — D15:1).
|
||||
|
||||
**Evidence (dialogue_anchor_turns):**
|
||||
|
||||
| # | Activity | Primary anchors |
|
||||
|---|---|---|
|
||||
| 1 | Contemporary dance | `D1:6` · `D1:8` · `D1:24` |
|
||||
| 2 | Running a dance studio | `D1:4` · `D1:20` · `D2:4` · `D2:8` |
|
||||
| 3 | Dance competitions | `D1:16` · `D4:13` · `D8:13` |
|
||||
| 4 | Gym / fitness | `D6:1` |
|
||||
| 5 | Reading business books | `D12:6` · `D12:8` |
|
||||
|
||||
**Why this fits:** five distinct activities with named anchors across 8+ dialogue sessions. Tests F2 (partial coverage — e.g. lists only dance + studio, omits gym + reading) vs F4 (lists 5 but one is fabricated — e.g. "marathon running" instead of gym) vs correct (all 5 enumerated faithfully).
|
||||
|
||||
**Caveat on granularity:** items 1-3 are dance-related facets (art form, business, competition). A stricter reader could argue Jon has "really 3-4 hobbies" (dance multi-facet + gym + reading + travel). The PM draft's F2 test still holds either way — the question is how many distinct enumeration units are present in the ground truth. We enumerate five to preserve the PM-intended 5+ cardinality.
|
||||
|
||||
**PASS** (with granularity caveat documented above).
|
||||
|
||||
---
|
||||
|
||||
## 6. Summary — all five drafts verified, no PM ping needed
|
||||
|
||||
| Draft | Original conv | Adapted conv | Swap type | Status |
|
||||
|---|---|---|---|---|
|
||||
| #1 temporal single-anchor | conv-1 (Melanie pottery) | **conv-44** (Audrey adopts Pixie) | conv+character reference only; shape unchanged | PASS |
|
||||
| #2 temporal two-anchor | conv-1 (Caroline Sweden) | **conv-44** (Pixie vs prior 3 dogs interval) | conv+character reference only; shape unchanged | PASS |
|
||||
| #3 null-result instrument | conv-2 (Nate) | **conv-43** (John) | conv+character reference only; shape + null-absence unchanged | PASS |
|
||||
| #4 null-result university | conv-15 (speakers) | **conv-48** (Deborah) | conv+character reference only; null-absence verified against Deborah turns only | PASS |
|
||||
| #5 chain-of-anchor hobbies | conv-30 (Jon) | **conv-30** (Jon) — unchanged | no swap | PASS with granularity caveat |
|
||||
|
||||
All swaps fit brief §A's "trivial" definition (conv+character replacement, question-shape preserved). Proceeding to Task C (finalize triples JSON) without PM ping.
|
||||
@@ -0,0 +1,989 @@
|
||||
{
|
||||
"generatedAt": "2026-04-21T13:04:54.701Z",
|
||||
"labelsSource": "D:\\Projects\\waggle-os\\preflight-results\\task-2-2-labels-14inst-2026-04-22.md",
|
||||
"judgeModel": "claude-haiku-4-5",
|
||||
"ensemble": [
|
||||
"claude-opus-4-7",
|
||||
"gpt-5.4",
|
||||
"gemini-3.1-pro"
|
||||
],
|
||||
"matchRate": {
|
||||
"matches": 13,
|
||||
"total": 14,
|
||||
"verdict": "PASS"
|
||||
},
|
||||
"cost": {
|
||||
"totalUsd": 0.15111,
|
||||
"judgeCalls": 42,
|
||||
"entries": [
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:07.043Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 687,
|
||||
"completionTokens": 65,
|
||||
"usd": 0.0030359999999999996,
|
||||
"latencyMs": 2204,
|
||||
"ok": true,
|
||||
"instanceIndex": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:09.478Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 430,
|
||||
"completionTokens": 50,
|
||||
"usd": 0.00204,
|
||||
"latencyMs": 2433,
|
||||
"ok": true,
|
||||
"instanceIndex": 1
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:12.551Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 452,
|
||||
"completionTokens": 129,
|
||||
"usd": 0.0032909999999999997,
|
||||
"latencyMs": 3071,
|
||||
"ok": true,
|
||||
"instanceIndex": 2
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:14.791Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 81,
|
||||
"usd": 0.003273,
|
||||
"latencyMs": 2238,
|
||||
"ok": true,
|
||||
"instanceIndex": 3
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:17.090Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 436,
|
||||
"completionTokens": 58,
|
||||
"usd": 0.0021780000000000002,
|
||||
"latencyMs": 2299,
|
||||
"ok": true,
|
||||
"instanceIndex": 4
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:43.212Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 462,
|
||||
"completionTokens": 189,
|
||||
"usd": 0.004221000000000001,
|
||||
"latencyMs": 26120,
|
||||
"ok": true,
|
||||
"instanceIndex": 5
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:45.543Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 84,
|
||||
"usd": 0.0033179999999999998,
|
||||
"latencyMs": 2330,
|
||||
"ok": true,
|
||||
"instanceIndex": 6
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:00:47.335Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 426,
|
||||
"completionTokens": 55,
|
||||
"usd": 0.002103,
|
||||
"latencyMs": 1791,
|
||||
"ok": true,
|
||||
"instanceIndex": 7
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:21.011Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 450,
|
||||
"completionTokens": 137,
|
||||
"usd": 0.003405,
|
||||
"latencyMs": 3154,
|
||||
"ok": true,
|
||||
"instanceIndex": 8
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:23.641Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 694,
|
||||
"completionTokens": 68,
|
||||
"usd": 0.0031019999999999997,
|
||||
"latencyMs": 2629,
|
||||
"ok": true,
|
||||
"instanceIndex": 9
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:25.417Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 439,
|
||||
"completionTokens": 56,
|
||||
"usd": 0.002157,
|
||||
"latencyMs": 1776,
|
||||
"ok": true,
|
||||
"instanceIndex": 10
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:28.718Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 464,
|
||||
"completionTokens": 151,
|
||||
"usd": 0.003657,
|
||||
"latencyMs": 3301,
|
||||
"ok": true,
|
||||
"instanceIndex": 11
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:30.629Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 64,
|
||||
"usd": 0.003018,
|
||||
"latencyMs": 1910,
|
||||
"ok": true,
|
||||
"instanceIndex": 12
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:32.596Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 424,
|
||||
"completionTokens": 52,
|
||||
"usd": 0.002052,
|
||||
"latencyMs": 1966,
|
||||
"ok": true,
|
||||
"instanceIndex": 13
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:35.276Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 450,
|
||||
"completionTokens": 162,
|
||||
"usd": 0.0037800000000000004,
|
||||
"latencyMs": 2680,
|
||||
"ok": true,
|
||||
"instanceIndex": 14
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:40.113Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 679,
|
||||
"completionTokens": 77,
|
||||
"usd": 0.0031920000000000004,
|
||||
"latencyMs": 4837,
|
||||
"ok": true,
|
||||
"instanceIndex": 15
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:42.147Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 436,
|
||||
"completionTokens": 58,
|
||||
"usd": 0.0021780000000000002,
|
||||
"latencyMs": 2034,
|
||||
"ok": true,
|
||||
"instanceIndex": 16
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:48.050Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 458,
|
||||
"completionTokens": 433,
|
||||
"usd": 0.007869,
|
||||
"latencyMs": 5903,
|
||||
"ok": true,
|
||||
"instanceIndex": 17
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:49.845Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 690,
|
||||
"completionTokens": 66,
|
||||
"usd": 0.00306,
|
||||
"latencyMs": 1793,
|
||||
"ok": true,
|
||||
"instanceIndex": 18
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:52.010Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 439,
|
||||
"completionTokens": 66,
|
||||
"usd": 0.002307,
|
||||
"latencyMs": 2165,
|
||||
"ok": true,
|
||||
"instanceIndex": 19
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:57.192Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 470,
|
||||
"completionTokens": 390,
|
||||
"usd": 0.00726,
|
||||
"latencyMs": 5182,
|
||||
"ok": true,
|
||||
"instanceIndex": 20
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:01:59.560Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 712,
|
||||
"completionTokens": 75,
|
||||
"usd": 0.003261,
|
||||
"latencyMs": 2368,
|
||||
"ok": true,
|
||||
"instanceIndex": 21
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:01.260Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 453,
|
||||
"completionTokens": 62,
|
||||
"usd": 0.0022890000000000002,
|
||||
"latencyMs": 1700,
|
||||
"ok": true,
|
||||
"instanceIndex": 22
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:05.089Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 484,
|
||||
"completionTokens": 248,
|
||||
"usd": 0.005172,
|
||||
"latencyMs": 3828,
|
||||
"ok": true,
|
||||
"instanceIndex": 23
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:07.559Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 691,
|
||||
"completionTokens": 82,
|
||||
"usd": 0.0033030000000000004,
|
||||
"latencyMs": 2470,
|
||||
"ok": true,
|
||||
"instanceIndex": 24
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:09.632Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 447,
|
||||
"completionTokens": 46,
|
||||
"usd": 0.0020310000000000003,
|
||||
"latencyMs": 2073,
|
||||
"ok": true,
|
||||
"instanceIndex": 25
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:13.773Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 475,
|
||||
"completionTokens": 197,
|
||||
"usd": 0.00438,
|
||||
"latencyMs": 4141,
|
||||
"ok": true,
|
||||
"instanceIndex": 26
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:17.799Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 721,
|
||||
"completionTokens": 67,
|
||||
"usd": 0.003168,
|
||||
"latencyMs": 4026,
|
||||
"ok": true,
|
||||
"instanceIndex": 27
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:19.382Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 453,
|
||||
"completionTokens": 62,
|
||||
"usd": 0.0022890000000000002,
|
||||
"latencyMs": 1583,
|
||||
"ok": true,
|
||||
"instanceIndex": 28
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:54.010Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 486,
|
||||
"completionTokens": 186,
|
||||
"usd": 0.004248,
|
||||
"latencyMs": 4107,
|
||||
"ok": true,
|
||||
"instanceIndex": 29
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:56.630Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 800,
|
||||
"completionTokens": 60,
|
||||
"usd": 0.0033,
|
||||
"latencyMs": 2620,
|
||||
"ok": true,
|
||||
"instanceIndex": 30
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:02:58.685Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 502,
|
||||
"completionTokens": 51,
|
||||
"usd": 0.002271,
|
||||
"latencyMs": 2055,
|
||||
"ok": true,
|
||||
"instanceIndex": 31
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:34.175Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 540,
|
||||
"completionTokens": 141,
|
||||
"usd": 0.003735,
|
||||
"latencyMs": 4985,
|
||||
"ok": true,
|
||||
"instanceIndex": 32
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:37.325Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 921,
|
||||
"completionTokens": 84,
|
||||
"usd": 0.0040230000000000005,
|
||||
"latencyMs": 3150,
|
||||
"ok": true,
|
||||
"instanceIndex": 33
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:39.719Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 586,
|
||||
"completionTokens": 69,
|
||||
"usd": 0.002793,
|
||||
"latencyMs": 2393,
|
||||
"ok": true,
|
||||
"instanceIndex": 34
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:43.147Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 636,
|
||||
"completionTokens": 173,
|
||||
"usd": 0.004503,
|
||||
"latencyMs": 3428,
|
||||
"ok": true,
|
||||
"instanceIndex": 35
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:47.063Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 943,
|
||||
"completionTokens": 84,
|
||||
"usd": 0.004089,
|
||||
"latencyMs": 3915,
|
||||
"ok": true,
|
||||
"instanceIndex": 36
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:49.923Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 598,
|
||||
"completionTokens": 60,
|
||||
"usd": 0.002694,
|
||||
"latencyMs": 2859,
|
||||
"ok": true,
|
||||
"instanceIndex": 37
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:03:55.921Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 649,
|
||||
"completionTokens": 145,
|
||||
"usd": 0.004122,
|
||||
"latencyMs": 5998,
|
||||
"ok": true,
|
||||
"instanceIndex": 38
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:04:00.275Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 1982,
|
||||
"completionTokens": 73,
|
||||
"usd": 0.007041,
|
||||
"latencyMs": 4354,
|
||||
"ok": true,
|
||||
"instanceIndex": 39
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:04:02.449Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 1289,
|
||||
"completionTokens": 58,
|
||||
"usd": 0.004737,
|
||||
"latencyMs": 2174,
|
||||
"ok": true,
|
||||
"instanceIndex": 40
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T13:04:54.701Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 1433,
|
||||
"completionTokens": 191,
|
||||
"usd": 0.007164,
|
||||
"latencyMs": 21724,
|
||||
"ok": true,
|
||||
"instanceIndex": 41
|
||||
}
|
||||
]
|
||||
},
|
||||
"perInstance": [
|
||||
{
|
||||
"index": 1,
|
||||
"instanceId": "locomo_conv-26_q109",
|
||||
"category": "single-hop",
|
||||
"question": "What did Mel and her kids make during the pottery workshop?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains the required fact (pots) with equivalent phrasing. Extra context (Mel, kids, workshop) is factually aligned with ground truth, no fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies that Mel and her kids made pots at the pottery workshop.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies that Mel and her kids made pots at the pottery workshop."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model answer correctly states that Mel and her kids made pots, which matches the ground truth without adding any incorrect information."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly states that they made pots, which matches the ground-truth answer."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 7712,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 2,
|
||||
"instanceId": "locomo_conv-41_q123",
|
||||
"category": "single-hop",
|
||||
"question": "What did Maria plan to do later on the evening of 7 July, 2023?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model misinterprets the context: 'friends from the gym' signals where Maria knows the friends from, not that she is going to the gym. The answer derives from present substrate but gets it wrong — classic F3 misreading of existing context, not fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states Maria planned to work out at the gym, when she actually planned to have dinner with friends from the gym.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states Maria planned to work out at the gym, when she actually planned to have dinner with friends from the gym."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model gives a context-related but wrong plan, saying Maria would go to the gym for a workout instead of having dinner with friends from the gym."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states Maria planned to work out at the gym, whereas the ground truth states she planned to have dinner with friends from the gym."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 30660,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 3,
|
||||
"instanceId": "locomo_conv-50_q141",
|
||||
"category": "single-hop",
|
||||
"question": "Which city is featured in the photograph Dave showed Calvin?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a city that does not appear in the context (Chicago, Lake Michigan) and fabricates a geographic descriptor (skyline, lake) without any supporting excerpt. This is outright invention of substrate — F4, not F3, because the answer's content has no origin in the ground truth material.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fabricates Chicago and Lake Michigan, which do not appear in the ground-truth context that identifies the city as Boston.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fabricates Chicago and Lake Michigan, which do not appear in the ground-truth context that identifies the city as Boston."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth says the photograph is of Boston, but the model instead names Chicago and adds unsupported details about the skyline near Lake Michigan."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model incorrectly identifies the city as Chicago and includes fabricated details about Lake Michigan, whereas the ground truth states the city is Boston."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 37799,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 4,
|
||||
"instanceId": "locomo_conv-42_q030",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of writings does Joanna do?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F2",
|
||||
"humanRationale": "Model correctly identifies two of four required elements (screenplays, books) with no fabrication or incorrect claims, but omits the other two (online blog posts, journal) which are explicitly evidenced in D18:1 and D18:5. Partial coverage without fabrication — F2.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books but omits the journal and online blog posts.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books but omits the journal and online blog posts."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The answer includes two correct writing types (screenplays and books) but omits online blog posts and journaling, which are also required."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books but fails to mention online blog posts and journals."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 7706,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 5,
|
||||
"instanceId": "locomo_conv-49_q015",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of unhealthy snacks does Sam enjoy eating?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains both required facts (soda, candy). Additional commentary about habit acknowledgment mirrors context faithfully, no incorrect additions.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as Sam's unhealthy snacks.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as Sam's unhealthy snacks."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies the unhealthy snacks as soda and candy, and the added note that it's not a healthy habit matches the context."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as the unhealthy snacks Sam enjoys, matching the ground truth."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 6557,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 6,
|
||||
"instanceId": "locomo_conv-41_q036",
|
||||
"category": "multi-hop",
|
||||
"question": "What music events has John attended?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F5",
|
||||
"humanRationale": "Model answer is coherent and derives from context (walks, picnics, town events appear in D8:11), but does not address the specific question — which music events did John attend. Response pivots to a related but different topic (John's family-activity preferences). No hallucination, no incorrect facts about John's activities, but off-topic relative to prompt — F5.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The model describes John's general activity preferences instead of naming the specific music events (violin concert, live music event) he attended.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The model describes John's general activity preferences instead of naming the specific music events (violin concert, live music event) he attended."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The answer does not mention the required music events (live music event and violin concert) and instead talks generally about family-friendly activities and exploring town events."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fails to mention the music events John attended and instead hallucinates activities like walks and picnics that are not present in the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": -0.5000000000000001
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 12774,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 7,
|
||||
"instanceId": "locomo_conv-42_q038",
|
||||
"category": "temporal",
|
||||
"question": "When did Joanna make a dessert with almond milk?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model derives date from context (session is 14 September, 'last Friday' reference) but computes it incorrectly. 7 September 2022 was a Wednesday — Friday before 14 September was actually 9 September. Answer draws on present substrate but arrives at a demonstrably wrong specific date — F3, not F4, because model did not fabricate the relationship, only miscomputed it.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The context says Joanna made the dessert 'last Friday' relative to 14 September 2022, which is Friday 9 September 2022, not 7 September 2022.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "7 September 2022 was the Friday before 14 September 2022, matching the ground truth."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The context says Joanna made the dessert 'last Friday' relative to 14 September 2022, which is Friday 9 September 2022, not 7 September 2022."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly calculated the date of 'last Friday' from September 14, 2022, as September 7 (which is a Wednesday), rather than the correct date of September 9."
|
||||
}
|
||||
],
|
||||
"fleissKappa": -0.5000000000000001
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 9141,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 8,
|
||||
"instanceId": "locomo_conv-41_q053",
|
||||
"category": "temporal",
|
||||
"question": "When did John help renovate his hometown community center?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model produces a year (2020) that cannot be derived from the context — 'last year' relative to 5 August 2023 unambiguously yields 2022. Model also fabricates causal context ('early pandemic period', 'volunteer support for infrastructure') that does not appear in any excerpt. Content generation beyond what the substrate allows — F4, not F3, because the fabricated context is the bulk of the answer.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states 2020 and adds a pandemic-related claim not present in the context; the correct year is 2022.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states 2020 and adds a pandemic-related claim not present in the context; the correct year is 2022."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth implies John helped renovate the community center in 2022, but the model says 2020 and adds unsupported details about the early pandemic and local infrastructure."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model incorrectly states the year as 2020 instead of 2022 and fabricates details about the early pandemic period that are not present in the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 7897,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 9,
|
||||
"instanceId": "locomo_conv-47_q017",
|
||||
"category": "open-ended",
|
||||
"question": "What is the board game where you have to find the imposter that John mentions to James?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F1",
|
||||
"humanRationale": "Model explicitly refuses to answer, stating it lacks sufficient information. No partial answer, no fabrication, no off-topic pivot. Ground truth (Mafia) is not literally stated in the D8:36 excerpt — model plays it safe and abstains rather than inferring from the 'find the impostor' description. Classic F1 abstain behavior.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly refuses to answer, stating it lacks sufficient information, rather than providing the answer 'Mafia'.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly refuses to answer, stating it lacks sufficient information, rather than providing the answer 'Mafia'."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly says it does not have enough information instead of identifying the game as Mafia."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly states it does not have enough information to answer the question."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 8684,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 10,
|
||||
"instanceId": "locomo_conv-44_pm_2026-04-22_001",
|
||||
"category": "temporal-scope",
|
||||
"question": "When did Audrey adopt Pixie?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Answer is vague-but-derived: \"early April\" overlaps with true \"around April 2, 2023\" but loses the specific date precision the ground truth provides. Derivable from substrate but imprecise — F3 misread of specific date, not fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "Early April 2023 is an acceptable equivalent formulation of around April 2, 2023.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "Early April 2023 is an acceptable equivalent formulation of around April 2, 2023."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer, 'early April 2023,' is a reasonable equivalent of the ground truth 'around April 2, 2023' and adds no incorrect information."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer of 'early April 2023' accurately reflects the ground truth date of 'around April 2, 2023'."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 40237,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 11,
|
||||
"instanceId": "locomo_conv-44_pm_2026-04-22_002",
|
||||
"category": "temporal-scope",
|
||||
"question": "How many years passed between Audrey adopting Pixie and her other three dogs?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Answer gives a specific but incorrect interval (two years vs ground-truth three years). Miscounted arithmetic on derivable anchors — F3 class misread, not F4 fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The ground truth states three years passed, but the model answered two years.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The ground truth states three years passed, but the model answered two years."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model answered the asked question but gave the wrong number of years; the context indicates three years, not two."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states that two years passed, whereas the ground truth states three years."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 40165,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 12,
|
||||
"instanceId": "locomo_conv-43_pm_2026-04-22_003",
|
||||
"category": "null-result",
|
||||
"question": "What musical instrument does John play?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a specific instrument (guitar) with fabricated supporting detail (weekend jam sessions, high school team). Dialogue contains zero evidence for John playing any instrument. This is classic F4 — invented substrate, plausible-sounding but entirely unsupported.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth indicates no evidence of John playing any instrument, but the model fabricates that he plays guitar with details not present in the context.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth indicates no evidence of John playing any instrument, but the model fabricates that he plays guitar with details not present in the context."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth is null because the dialogue provides no evidence about any musical instrument John plays, while the model fabricates that he plays guitar and adds unsupported details about weekend jam sessions and his high school team."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fabricated the claim that John plays the guitar, which is not supported by the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 8972,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 13,
|
||||
"instanceId": "locomo_conv-48_pm_2026-04-22_004",
|
||||
"category": "null-result",
|
||||
"question": "Which university did Deborah attend?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a specific university (Stanford) and a specific degree (computer science) for Deborah, neither of which appear in the dialogue. This is F4 — full fabrication from a null-evidence base. Stanford is a plausible-default \"prestigious US university\" choice that LLMs commonly hallucinate in absence of context.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth indicates no evidence of Deborah's university in the context, but the model fabricates that she attended Stanford for computer science.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth indicates no evidence of Deborah's university in the context, but the model fabricates that she attended Stanford for computer science."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth is null because the dialogue provides no evidence of which university Deborah attended, but the model fabricated 'Stanford University' and a computer science degree."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fabricated the claim that Deborah attended Stanford University, which is not supported by the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 12774,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 14,
|
||||
"instanceId": "locomo_conv-30_pm_2026-04-22_005",
|
||||
"category": "chain-of-anchor",
|
||||
"question": "What hobbies and activities does Jon pursue across the dialogue history?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F2",
|
||||
"humanRationale": "Model lists 2 of 5 ground-truth activities correctly (contemporary dance + dance studio business) with no fabrication — but omits dance competitions, gym/fitness, and reading. This is textbook F2: partial coverage / omission without hallucination. The two items mentioned are accurately supported; the failure mode is the three missing items.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model mentions only dance and the studio but misses competitions, gym/fitness, and reading business books.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model mentions only dance and the studio but misses competitions, gym/fitness, and reading business books."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The answer correctly mentions contemporary dance and running a dance studio but omits Jon's dance competitions, gym/fitness, and reading business-improvement books."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies contemporary dance and running a dance studio, but misses competing in dance competitions, going to the gym, and reading business-improvement books."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 58780,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,713 @@
|
||||
{
|
||||
"generatedAt": "2026-04-21T09:00:53.796Z",
|
||||
"labelsSource": "D:/Projects/PM-Waggle-OS/calibration/2026-04-20-failure-mode-calibration-labels.md",
|
||||
"judgeModel": "claude-haiku-4-5",
|
||||
"ensemble": [
|
||||
"claude-opus-4-7",
|
||||
"gpt-5.4",
|
||||
"gemini-3.1-pro"
|
||||
],
|
||||
"matchRate": {
|
||||
"matches": 9,
|
||||
"total": 10,
|
||||
"verdict": "PASS"
|
||||
},
|
||||
"cost": {
|
||||
"totalUsd": 0.10118399999999997,
|
||||
"judgeCalls": 30,
|
||||
"entries": [
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:45.179Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 687,
|
||||
"completionTokens": 55,
|
||||
"usd": 0.0028859999999999997,
|
||||
"latencyMs": 1932,
|
||||
"ok": true,
|
||||
"instanceIndex": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:47.704Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 430,
|
||||
"completionTokens": 49,
|
||||
"usd": 0.002025,
|
||||
"latencyMs": 2524,
|
||||
"ok": true,
|
||||
"instanceIndex": 1
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:50.841Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 452,
|
||||
"completionTokens": 128,
|
||||
"usd": 0.003276,
|
||||
"latencyMs": 3135,
|
||||
"ok": true,
|
||||
"instanceIndex": 2
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:52.736Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 78,
|
||||
"usd": 0.003228,
|
||||
"latencyMs": 1893,
|
||||
"ok": true,
|
||||
"instanceIndex": 3
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:55.330Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 436,
|
||||
"completionTokens": 61,
|
||||
"usd": 0.002223,
|
||||
"latencyMs": 2594,
|
||||
"ok": true,
|
||||
"instanceIndex": 4
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:58.596Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 462,
|
||||
"completionTokens": 189,
|
||||
"usd": 0.004221000000000001,
|
||||
"latencyMs": 3264,
|
||||
"ok": true,
|
||||
"instanceIndex": 5
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:00.865Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 71,
|
||||
"usd": 0.003123,
|
||||
"latencyMs": 2269,
|
||||
"ok": true,
|
||||
"instanceIndex": 6
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:02.731Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 426,
|
||||
"completionTokens": 53,
|
||||
"usd": 0.002073,
|
||||
"latencyMs": 1866,
|
||||
"ok": true,
|
||||
"instanceIndex": 7
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:18.502Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 450,
|
||||
"completionTokens": 137,
|
||||
"usd": 0.003405,
|
||||
"latencyMs": 15769,
|
||||
"ok": true,
|
||||
"instanceIndex": 8
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:22.048Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 694,
|
||||
"completionTokens": 66,
|
||||
"usd": 0.0030719999999999996,
|
||||
"latencyMs": 3545,
|
||||
"ok": true,
|
||||
"instanceIndex": 9
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:23.612Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 439,
|
||||
"completionTokens": 48,
|
||||
"usd": 0.002037,
|
||||
"latencyMs": 1563,
|
||||
"ok": true,
|
||||
"instanceIndex": 10
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:57.308Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 464,
|
||||
"completionTokens": 151,
|
||||
"usd": 0.003657,
|
||||
"latencyMs": 3181,
|
||||
"ok": true,
|
||||
"instanceIndex": 11
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:57:59.583Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 72,
|
||||
"usd": 0.003138,
|
||||
"latencyMs": 2272,
|
||||
"ok": true,
|
||||
"instanceIndex": 12
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:58:01.109Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 424,
|
||||
"completionTokens": 52,
|
||||
"usd": 0.002052,
|
||||
"latencyMs": 1526,
|
||||
"ok": true,
|
||||
"instanceIndex": 13
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:58:04.050Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 450,
|
||||
"completionTokens": 157,
|
||||
"usd": 0.003705,
|
||||
"latencyMs": 2941,
|
||||
"ok": true,
|
||||
"instanceIndex": 14
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:58:08.787Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 679,
|
||||
"completionTokens": 77,
|
||||
"usd": 0.0031920000000000004,
|
||||
"latencyMs": 4735,
|
||||
"ok": true,
|
||||
"instanceIndex": 15
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:58:13.737Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 436,
|
||||
"completionTokens": 60,
|
||||
"usd": 0.002208,
|
||||
"latencyMs": 4949,
|
||||
"ok": true,
|
||||
"instanceIndex": 16
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:58:59.972Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 458,
|
||||
"completionTokens": 433,
|
||||
"usd": 0.007869,
|
||||
"latencyMs": 15710,
|
||||
"ok": true,
|
||||
"instanceIndex": 17
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:02.017Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 690,
|
||||
"completionTokens": 66,
|
||||
"usd": 0.00306,
|
||||
"latencyMs": 2045,
|
||||
"ok": true,
|
||||
"instanceIndex": 18
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:10.936Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 439,
|
||||
"completionTokens": 66,
|
||||
"usd": 0.002307,
|
||||
"latencyMs": 8919,
|
||||
"ok": true,
|
||||
"instanceIndex": 19
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:16.276Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 470,
|
||||
"completionTokens": 383,
|
||||
"usd": 0.007155,
|
||||
"latencyMs": 5340,
|
||||
"ok": true,
|
||||
"instanceIndex": 20
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:18.267Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 712,
|
||||
"completionTokens": 79,
|
||||
"usd": 0.0033209999999999997,
|
||||
"latencyMs": 1991,
|
||||
"ok": true,
|
||||
"instanceIndex": 21
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:19.679Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 453,
|
||||
"completionTokens": 62,
|
||||
"usd": 0.0022890000000000002,
|
||||
"latencyMs": 1412,
|
||||
"ok": true,
|
||||
"instanceIndex": 22
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:36.479Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 484,
|
||||
"completionTokens": 247,
|
||||
"usd": 0.005157,
|
||||
"latencyMs": 16799,
|
||||
"ok": true,
|
||||
"instanceIndex": 23
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:38.692Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 731,
|
||||
"completionTokens": 80,
|
||||
"usd": 0.003393,
|
||||
"latencyMs": 2213,
|
||||
"ok": true,
|
||||
"instanceIndex": 24
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:59:40.795Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 451,
|
||||
"completionTokens": 65,
|
||||
"usd": 0.002328,
|
||||
"latencyMs": 2102,
|
||||
"ok": true,
|
||||
"instanceIndex": 25
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T09:00:15.161Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 475,
|
||||
"completionTokens": 256,
|
||||
"usd": 0.005265,
|
||||
"latencyMs": 3844,
|
||||
"ok": true,
|
||||
"instanceIndex": 26
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T09:00:18.039Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 691,
|
||||
"completionTokens": 64,
|
||||
"usd": 0.003033,
|
||||
"latencyMs": 2878,
|
||||
"ok": true,
|
||||
"instanceIndex": 27
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T09:00:19.497Z",
|
||||
"model": "gpt-5.4",
|
||||
"promptTokens": 447,
|
||||
"completionTokens": 51,
|
||||
"usd": 0.0021060000000000002,
|
||||
"latencyMs": 1457,
|
||||
"ok": true,
|
||||
"instanceIndex": 28
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T09:00:53.796Z",
|
||||
"model": "gemini-3.1-pro",
|
||||
"promptTokens": 475,
|
||||
"completionTokens": 197,
|
||||
"usd": 0.00438,
|
||||
"latencyMs": 3778,
|
||||
"ok": true,
|
||||
"instanceIndex": 29
|
||||
}
|
||||
]
|
||||
},
|
||||
"perInstance": [
|
||||
{
|
||||
"index": 1,
|
||||
"instanceId": "locomo_conv-26_q109",
|
||||
"category": "single-hop",
|
||||
"question": "What did Mel and her kids make during the pottery workshop?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains the required fact (pots) with equivalent phrasing. Extra context (Mel, kids, workshop) is factually aligned with ground truth, no fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer matches the ground truth that they made pots.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer matches the ground truth that they made pots."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly states that Mel and her kids made pots, which matches the ground truth without adding any incorrect information."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly states that Mel and her kids made pots, which matches the ground-truth answer."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 7596,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 2,
|
||||
"instanceId": "locomo_conv-41_q123",
|
||||
"category": "single-hop",
|
||||
"question": "What did Maria plan to do later on the evening of 7 July, 2023?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model misinterprets the context: 'friends from the gym' signals where Maria knows the friends from, not that she is going to the gym. The answer derives from present substrate but gets it wrong — classic F3 misreading of existing context, not fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "Maria planned to have dinner with friends from the gym, not to work out at the gym; the model got the fact wrong.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "Maria planned to have dinner with friends from the gym, not to work out at the gym; the model got the fact wrong."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model answered with a different planned activity from the context: Maria was going to have dinner with friends from the gym, not go to the gym for a workout."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states Maria planned to work out at the gym, whereas the ground truth states she planned to have dinner with friends from the gym."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 7754,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 3,
|
||||
"instanceId": "locomo_conv-50_q141",
|
||||
"category": "single-hop",
|
||||
"question": "Which city is featured in the photograph Dave showed Calvin?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a city that does not appear in the context (Chicago, Lake Michigan) and fabricates a geographic descriptor (skyline, lake) without any supporting excerpt. This is outright invention of substrate — F4, not F3, because the answer's content has no origin in the ground truth material.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model claims Chicago, fabricating a city not in the ground-truth context which states Boston.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model claims Chicago, fabricating a city not in the ground-truth context which states Boston."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth says the photograph shows Boston, but the model answers Chicago and adds unsupported details about the skyline near Lake Michigan."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model incorrectly identifies the city as Chicago and includes fabricated details about Lake Michigan, whereas the ground truth states the city is Boston."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 19906,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 4,
|
||||
"instanceId": "locomo_conv-42_q030",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of writings does Joanna do?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F2",
|
||||
"humanRationale": "Model correctly identifies two of four required elements (screenplays, books) with no fabrication or incorrect claims, but omits the other two (online blog posts, journal) which are explicitly evidenced in D18:1 and D18:5. Partial coverage without fabrication — F2.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model mentions screenplays and books but omits online blog posts and journal writing.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model mentions screenplays and books but omits online blog posts and journal writing."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The answer correctly mentions screenplays and books but omits Joanna's online blog posts and journal writing."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books but fails to mention online blog posts and journals."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 38806,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 5,
|
||||
"instanceId": "locomo_conv-49_q015",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of unhealthy snacks does Sam enjoy eating?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains both required facts (soda, candy). Additional commentary about habit acknowledgment mirrors context faithfully, no incorrect additions.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as Sam's unhealthy snacks, matching the ground truth.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as Sam's unhealthy snacks, matching the ground truth."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies the unhealthy snacks as soda and candy, and the added note that Sam knows it's not healthy matches the context."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as the unhealthy snacks Sam enjoys, matching the ground truth."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 6740,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 6,
|
||||
"instanceId": "locomo_conv-41_q036",
|
||||
"category": "multi-hop",
|
||||
"question": "What music events has John attended?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F5",
|
||||
"humanRationale": "Model answer is coherent and derives from context (walks, picnics, town events appear in D8:11), but does not address the specific question — which music events did John attend. Response pivots to a related but different topic (John's family-activity preferences). No hallucination, no incorrect facts about John's activities, but off-topic relative to prompt — F5.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The model describes general activities John enjoys rather than naming the specific music events (violin concert, live music event) he attended.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The model describes general activities John enjoys rather than naming the specific music events (violin concert, live music event) he attended."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The answer discusses John's general interests in family-friendly activities and town events instead of identifying the specific music events he attended: a live music event and a violin concert."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fails to mention the music events John attended and instead hallucinates activities like walks and picnics that are not present in the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": -0.5000000000000001
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 55921,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 7,
|
||||
"instanceId": "locomo_conv-42_q038",
|
||||
"category": "temporal",
|
||||
"question": "When did Joanna make a dessert with almond milk?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model derives date from context (session is 14 September, 'last Friday' reference) but computes it incorrectly. 7 September 2022 was a Wednesday — Friday before 14 September was actually 9 September. Answer draws on present substrate but arrives at a demonstrably wrong specific date — F3, not F4, because model did not fabricate the relationship, only miscomputed it.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The context says Joanna made the dessert 'last Friday' relative to 14 September 2022, which is Friday 9 September 2022, not 7 September 2022.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "7 September 2022 is the Friday before 14 September 2022, matching the ground truth."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The context says Joanna made the dessert 'last Friday' relative to 14 September 2022, which is Friday 9 September 2022, not 7 September 2022."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly calculated 'last Friday' from September 14 as September 7, which is a week prior rather than the actual Friday before."
|
||||
}
|
||||
],
|
||||
"fleissKappa": -0.5000000000000001
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 16304,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 8,
|
||||
"instanceId": "locomo_conv-41_q053",
|
||||
"category": "temporal",
|
||||
"question": "When did John help renovate his hometown community center?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model produces a year (2020) that cannot be derived from the context — 'last year' relative to 5 August 2023 unambiguously yields 2022. Model also fabricates causal context ('early pandemic period', 'volunteer support for infrastructure') that does not appear in any excerpt. Content generation beyond what the substrate allows — F4, not F3, because the fabricated context is the bulk of the answer.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states 2020 and adds fabricated pandemic context, neither of which is supported by the ground truth (2022).",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states 2020 and adds fabricated pandemic context, neither of which is supported by the ground truth (2022)."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The ground truth implies he helped renovate the community center in 2022, but the model says 2020 and adds unsupported details about the early pandemic and local infrastructure."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model incorrectly states the year as 2020 instead of 2022 and fabricates details about the early pandemic period that are not present in the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 20203,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 9,
|
||||
"instanceId": "locomo_conv-50_q037",
|
||||
"category": "open-ended",
|
||||
"question": "Does Calvin love music tours?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model gives the correct yes/no answer aligned with ground truth, and supports it with context-faithful evidence across all three referenced sessions. No fabrication, no misreading.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The yes/no answer matches the ground truth, but it adds unsupported claims such as touring with Frank Ocean and feeling alive on stage in Tokyo, which are not present in the provided context.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly affirms Calvin loves tours and covers all required facts (Tokyo, audience energy, excited for next tour)."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The yes/no answer matches the ground truth, but it adds unsupported claims such as touring with Frank Ocean and feeling alive on stage in Tokyo, which are not present in the provided context."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model includes a fabricated detail about touring with Frank Ocean, which does not appear in the ground-truth context."
|
||||
}
|
||||
],
|
||||
"fleissKappa": -0.5000000000000001
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 38682,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 10,
|
||||
"instanceId": "locomo_conv-47_q017",
|
||||
"category": "open-ended",
|
||||
"question": "What is the board game where you have to find the imposter that John mentions to James?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F1",
|
||||
"humanRationale": "Model explicitly refuses to answer, stating it lacks sufficient information. No partial answer, no fabrication, no off-topic pivot. Ground truth (Mafia) is not literally stated in the D8:36 excerpt — model plays it safe and abstains rather than inferring from the 'find the impostor' description. Classic F1 abstain behavior.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model abstains by stating it lacks enough information to determine the game's name.",
|
||||
"judge_model": "ensemble_majority",
|
||||
"ensemble": [
|
||||
{
|
||||
"model": "claude-opus-4-7",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model abstains by stating it lacks enough information to determine the game's name."
|
||||
},
|
||||
{
|
||||
"model": "gpt-5.4",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly abstains by saying it does not have enough information, while the ground-truth answer is Mafia."
|
||||
},
|
||||
{
|
||||
"model": "gemini-3.1-pro",
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly states it does not have enough information to answer the question."
|
||||
}
|
||||
],
|
||||
"fleissKappa": 1
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 38635,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
299
preflight-results/judge-calibration-haiku-task4.json
Normal file
299
preflight-results/judge-calibration-haiku-task4.json
Normal file
@@ -0,0 +1,299 @@
|
||||
{
|
||||
"generatedAt": "2026-04-21T02:07:45.916Z",
|
||||
"labelsSource": "D:/Projects/PM-Waggle-OS/calibration/2026-04-20-failure-mode-calibration-labels.md",
|
||||
"judgeModel": "claude-haiku-4-5",
|
||||
"ensemble": null,
|
||||
"matchRate": {
|
||||
"matches": 5,
|
||||
"total": 10,
|
||||
"verdict": "FAIL"
|
||||
},
|
||||
"cost": {
|
||||
"totalUsd": 0.026210999999999998,
|
||||
"judgeCalls": 10,
|
||||
"entries": [
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:35.284Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 478,
|
||||
"completionTokens": 67,
|
||||
"usd": 0.002439,
|
||||
"latencyMs": 1043,
|
||||
"ok": true,
|
||||
"instanceIndex": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:36.378Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 480,
|
||||
"completionTokens": 74,
|
||||
"usd": 0.00255,
|
||||
"latencyMs": 1091,
|
||||
"ok": true,
|
||||
"instanceIndex": 1
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:37.394Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 470,
|
||||
"completionTokens": 70,
|
||||
"usd": 0.00246,
|
||||
"latencyMs": 1015,
|
||||
"ok": true,
|
||||
"instanceIndex": 2
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:38.481Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 487,
|
||||
"completionTokens": 68,
|
||||
"usd": 0.002481,
|
||||
"latencyMs": 1087,
|
||||
"ok": true,
|
||||
"instanceIndex": 3
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:39.976Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 474,
|
||||
"completionTokens": 86,
|
||||
"usd": 0.002712,
|
||||
"latencyMs": 1494,
|
||||
"ok": true,
|
||||
"instanceIndex": 4
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:41.042Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 479,
|
||||
"completionTokens": 77,
|
||||
"usd": 0.0025919999999999997,
|
||||
"latencyMs": 1065,
|
||||
"ok": true,
|
||||
"instanceIndex": 5
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:41.949Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 493,
|
||||
"completionTokens": 80,
|
||||
"usd": 0.002679,
|
||||
"latencyMs": 907,
|
||||
"ok": true,
|
||||
"instanceIndex": 6
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:43.376Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 502,
|
||||
"completionTokens": 92,
|
||||
"usd": 0.0028859999999999997,
|
||||
"latencyMs": 1427,
|
||||
"ok": true,
|
||||
"instanceIndex": 7
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:44.567Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 495,
|
||||
"completionTokens": 82,
|
||||
"usd": 0.002715,
|
||||
"latencyMs": 1189,
|
||||
"ok": true,
|
||||
"instanceIndex": 8
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:07:45.915Z",
|
||||
"model": "claude-haiku-4-5",
|
||||
"promptTokens": 494,
|
||||
"completionTokens": 81,
|
||||
"usd": 0.0026969999999999997,
|
||||
"latencyMs": 1348,
|
||||
"ok": true,
|
||||
"instanceIndex": 9
|
||||
}
|
||||
]
|
||||
},
|
||||
"perInstance": [
|
||||
{
|
||||
"index": 1,
|
||||
"instanceId": "locomo_conv-26_q109",
|
||||
"category": "single-hop",
|
||||
"question": "What did Mel and her kids make during the pottery workshop?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains the required fact (pots) with equivalent phrasing. Extra context (Mel, kids, workshop) is factually aligned with ground truth, no fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer accurately states that Mel and her kids made pots at the pottery workshop, which directly matches the ground-truth answer and supporting context.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1047,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 2,
|
||||
"instanceId": "locomo_conv-41_q123",
|
||||
"category": "single-hop",
|
||||
"question": "What did Maria plan to do later on the evening of 7 July, 2023?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model misinterprets the context: 'friends from the gym' signals where Maria knows the friends from, not that she is going to the gym. The answer derives from present substrate but gets it wrong — classic F3 misreading of existing context, not fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model states Maria planned to go to the gym for a workout, when the ground truth clearly indicates she planned to have dinner with friends from the gym, which are two different activities.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1093,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 3,
|
||||
"instanceId": "locomo_conv-50_q141",
|
||||
"category": "single-hop",
|
||||
"question": "Which city is featured in the photograph Dave showed Calvin?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a city that does not appear in the context (Chicago, Lake Michigan) and fabricates a geographic descriptor (skyline, lake) without any supporting excerpt. This is outright invention of substrate — F4, not F3, because the answer's content has no origin in the ground truth material.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model states the photograph features Chicago, but the ground-truth context explicitly identifies the city as Boston, making this a factual error derived from the provided context.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 1015,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 4,
|
||||
"instanceId": "locomo_conv-42_q030",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of writings does Joanna do?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F2",
|
||||
"humanRationale": "Model correctly identifies two of four required elements (screenplays, books) with no fabrication or incorrect claims, but omits the other two (online blog posts, journal) which are explicitly evidenced in D18:1 and D18:5. Partial coverage without fabrication — F2.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books, but omits two other types of writing mentioned in the ground truth: journal entries and online blog posts.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1087,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 5,
|
||||
"instanceId": "locomo_conv-49_q015",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of unhealthy snacks does Sam enjoy eating?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains both required facts (soda, candy). Additional commentary about habit acknowledgment mirrors context faithfully, no incorrect additions.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model's answer correctly identifies both unhealthy snacks (soda and candy) that Sam enjoys and accurately includes the contextual detail that Sam acknowledges this is not a healthy habit, which is factually supported by the ground-truth excerpt.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1494,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 6,
|
||||
"instanceId": "locomo_conv-41_q036",
|
||||
"category": "multi-hop",
|
||||
"question": "What music events has John attended?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F5",
|
||||
"humanRationale": "Model answer is coherent and derives from context (walks, picnics, town events appear in D8:11), but does not address the specific question — which music events did John attend. Response pivots to a related but different topic (John's family-activity preferences). No hallucination, no incorrect facts about John's activities, but off-topic relative to prompt — F5.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model's answer mentions general activities (walks, picnics, exploring events) but fails to specify the two required music events John attended: the violin concert and the live music event with family.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 1066,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 7,
|
||||
"instanceId": "locomo_conv-42_q038",
|
||||
"category": "temporal",
|
||||
"question": "When did Joanna make a dessert with almond milk?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model derives date from context (session is 14 September, 'last Friday' reference) but computes it incorrectly. 7 September 2022 was a Wednesday — Friday before 14 September was actually 9 September. Answer draws on present substrate but arrives at a demonstrably wrong specific date — F3, not F4, because model did not fabricate the relationship, only miscomputed it.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies that Joanna made the dessert on 7 September, 2022, which is the Friday before 14 September, 2022, as stated in the ground-truth context.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 907,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 8,
|
||||
"instanceId": "locomo_conv-41_q053",
|
||||
"category": "temporal",
|
||||
"question": "When did John help renovate his hometown community center?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model produces a year (2020) that cannot be derived from the context — 'last year' relative to 5 August 2023 unambiguously yields 2022. Model also fabricates causal context ('early pandemic period', 'volunteer support for infrastructure') that does not appear in any excerpt. Content generation beyond what the substrate allows — F4, not F3, because the fabricated context is the bulk of the answer.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model states the renovation occurred in 2020 during the pandemic, but the ground-truth context indicates it happened in 2022 ('Last year' from August 2023 = 2022), and provides no support for the pandemic-related claim.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 1428,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 9,
|
||||
"instanceId": "locomo_conv-50_q037",
|
||||
"category": "open-ended",
|
||||
"question": "Does Calvin love music tours?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model gives the correct yes/no answer aligned with ground truth, and supports it with context-faithful evidence across all three referenced sessions. No fabrication, no misreading.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model fabricates a specific claim about 'touring with Frank Ocean' that does not appear in the ground-truth context, which only mentions Tokyo tour, audience energy, and excitement for next tour without naming Frank Ocean.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 1190,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 10,
|
||||
"instanceId": "locomo_conv-47_q017",
|
||||
"category": "open-ended",
|
||||
"question": "What is the board game where you have to find the imposter that John mentions to James?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F1",
|
||||
"humanRationale": "Model explicitly refuses to answer, stating it lacks sufficient information. No partial answer, no fabrication, no off-topic pivot. Ground truth (Mafia) is not literally stated in the D8:36 excerpt — model plays it safe and abstains rather than inferring from the 'find the impostor' description. Classic F1 abstain behavior.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model abstained by claiming insufficient information, but the ground-truth context clearly describes a game about finding impostors, which corresponds to the board game 'Mafia' that the model should have identified.",
|
||||
"judge_model": "claude-haiku-4-5"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1348,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
309
preflight-results/judge-calibration-opus-task4.json
Normal file
309
preflight-results/judge-calibration-opus-task4.json
Normal file
@@ -0,0 +1,309 @@
|
||||
{
|
||||
"generatedAt": "2026-04-21T02:10:31.669Z",
|
||||
"labelsSource": "D:/Projects/PM-Waggle-OS/calibration/2026-04-20-failure-mode-calibration-labels.md",
|
||||
"judgeModel": "claude-opus-4-7",
|
||||
"ensemble": null,
|
||||
"matchRate": {
|
||||
"matches": 10,
|
||||
"total": 10,
|
||||
"verdict": "PASS"
|
||||
},
|
||||
"cost": {
|
||||
"totalUsd": 0.036957000000000004,
|
||||
"judgeCalls": 11,
|
||||
"entries": [
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:07.328Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 687,
|
||||
"completionTokens": 71,
|
||||
"usd": 0.003126,
|
||||
"latencyMs": 2008,
|
||||
"ok": true,
|
||||
"instanceIndex": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:09.184Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 70,
|
||||
"usd": 0.0031079999999999997,
|
||||
"latencyMs": 1853,
|
||||
"ok": true,
|
||||
"instanceIndex": 1
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:11.550Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 83,
|
||||
"usd": 0.003303,
|
||||
"latencyMs": 2364,
|
||||
"ok": true,
|
||||
"instanceIndex": 2
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:14.137Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 694,
|
||||
"completionTokens": 69,
|
||||
"usd": 0.0031169999999999995,
|
||||
"latencyMs": 2587,
|
||||
"ok": true,
|
||||
"instanceIndex": 3
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:15.776Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 686,
|
||||
"completionTokens": 72,
|
||||
"usd": 0.003138,
|
||||
"latencyMs": 1639,
|
||||
"ok": true,
|
||||
"instanceIndex": 4
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:17.812Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 679,
|
||||
"completionTokens": 82,
|
||||
"usd": 0.003267,
|
||||
"latencyMs": 2034,
|
||||
"ok": true,
|
||||
"instanceIndex": 5
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:20.900Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 690,
|
||||
"completionTokens": 208,
|
||||
"usd": 0.00519,
|
||||
"latencyMs": 3088,
|
||||
"ok": true,
|
||||
"instanceIndex": 6
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:25.725Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 717,
|
||||
"completionTokens": 57,
|
||||
"usd": 0.003006,
|
||||
"latencyMs": 4825,
|
||||
"ok": true,
|
||||
"instanceIndex": 7
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:28.243Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 712,
|
||||
"completionTokens": 74,
|
||||
"usd": 0.0032459999999999998,
|
||||
"latencyMs": 2517,
|
||||
"ok": true,
|
||||
"instanceIndex": 8
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:30.127Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 731,
|
||||
"completionTokens": 86,
|
||||
"usd": 0.003483,
|
||||
"latencyMs": 1882,
|
||||
"ok": true,
|
||||
"instanceIndex": 9
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T02:10:31.669Z",
|
||||
"model": "claude-opus-4-7",
|
||||
"promptTokens": 691,
|
||||
"completionTokens": 60,
|
||||
"usd": 0.002973,
|
||||
"latencyMs": 1542,
|
||||
"ok": true,
|
||||
"instanceIndex": 10
|
||||
}
|
||||
]
|
||||
},
|
||||
"perInstance": [
|
||||
{
|
||||
"index": 1,
|
||||
"instanceId": "locomo_conv-26_q109",
|
||||
"category": "single-hop",
|
||||
"question": "What did Mel and her kids make during the pottery workshop?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains the required fact (pots) with equivalent phrasing. Extra context (Mel, kids, workshop) is factually aligned with ground truth, no fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly states that Mel and her kids made pots at the pottery workshop, matching the ground truth.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2011,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 2,
|
||||
"instanceId": "locomo_conv-41_q123",
|
||||
"category": "single-hop",
|
||||
"question": "What did Maria plan to do later on the evening of 7 July, 2023?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model misinterprets the context: 'friends from the gym' signals where Maria knows the friends from, not that she is going to the gym. The answer derives from present substrate but gets it wrong — classic F3 misreading of existing context, not fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "Maria planned to have dinner with friends from the gym, not go to the gym for a workout.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1855,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 3,
|
||||
"instanceId": "locomo_conv-50_q141",
|
||||
"category": "single-hop",
|
||||
"question": "Which city is featured in the photograph Dave showed Calvin?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a city that does not appear in the context (Chicago, Lake Michigan) and fabricates a geographic descriptor (skyline, lake) without any supporting excerpt. This is outright invention of substrate — F4, not F3, because the answer's content has no origin in the ground truth material.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states Chicago with Lake Michigan details, which contradicts the ground truth of Boston and fabricates information not in the context.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2365,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 4,
|
||||
"instanceId": "locomo_conv-42_q030",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of writings does Joanna do?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F2",
|
||||
"humanRationale": "Model correctly identifies two of four required elements (screenplays, books) with no fabrication or incorrect claims, but omits the other two (online blog posts, journal) which are explicitly evidenced in D18:1 and D18:5. Partial coverage without fabrication — F2.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books but omits online blog posts and journal writing.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2587,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 5,
|
||||
"instanceId": "locomo_conv-49_q015",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of unhealthy snacks does Sam enjoy eating?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains both required facts (soda, candy). Additional commentary about habit acknowledgment mirrors context faithfully, no incorrect additions.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies soda and candy as Sam's unhealthy snacks, matching the ground truth.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1640,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 6,
|
||||
"instanceId": "locomo_conv-41_q036",
|
||||
"category": "multi-hop",
|
||||
"question": "What music events has John attended?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F5",
|
||||
"humanRationale": "Model answer is coherent and derives from context (walks, picnics, town events appear in D8:11), but does not address the specific question — which music events did John attend. Response pivots to a related but different topic (John's family-activity preferences). No hallucination, no incorrect facts about John's activities, but off-topic relative to prompt — F5.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The model discusses John's general interests in family activities and town events but does not mention the specific music events (violin concert, live music event) asked about.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2035,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 7,
|
||||
"instanceId": "locomo_conv-42_q038",
|
||||
"category": "temporal",
|
||||
"question": "When did Joanna make a dessert with almond milk?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model derives date from context (session is 14 September, 'last Friday' reference) but computes it incorrectly. 7 September 2022 was a Wednesday — Friday before 14 September was actually 9 September. Answer draws on present substrate but arrives at a demonstrably wrong specific date — F3, not F4, because model did not fabricate the relationship, only miscomputed it.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The Friday before 14 September 2022 was 9 September, not 7 September.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 7913,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 8,
|
||||
"instanceId": "locomo_conv-41_q053",
|
||||
"category": "temporal",
|
||||
"question": "When did John help renovate his hometown community center?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model produces a year (2020) that cannot be derived from the context — 'last year' relative to 5 August 2023 unambiguously yields 2022. Model also fabricates causal context ('early pandemic period', 'volunteer support for infrastructure') that does not appear in any excerpt. Content generation beyond what the substrate allows — F4, not F3, because the fabricated context is the bulk of the answer.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states 2020 and adds pandemic-related details not supported by the context; the correct year is 2022.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2518,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 9,
|
||||
"instanceId": "locomo_conv-50_q037",
|
||||
"category": "open-ended",
|
||||
"question": "Does Calvin love music tours?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model gives the correct yes/no answer aligned with ground truth, and supports it with context-faithful evidence across all three referenced sessions. No fabrication, no misreading.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model affirms Calvin loves music tours and cites the Tokyo tour, audience energy, and excitement for the next tour, matching the ground truth.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1883,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 10,
|
||||
"instanceId": "locomo_conv-47_q017",
|
||||
"category": "open-ended",
|
||||
"question": "What is the board game where you have to find the imposter that John mentions to James?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F1",
|
||||
"humanRationale": "Model explicitly refuses to answer, stating it lacks sufficient information. No partial answer, no fabrication, no off-topic pivot. Ground truth (Mafia) is not literally stated in the D8:36 excerpt — model plays it safe and abstains rather than inferring from the 'find the impostor' description. Classic F1 abstain behavior.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model abstains, stating it does not have enough information to answer.",
|
||||
"judge_model": "claude-opus-4-7"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1542,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,299 @@
|
||||
{
|
||||
"generatedAt": "2026-04-21T08:56:18.311Z",
|
||||
"labelsSource": "D:/Projects/PM-Waggle-OS/calibration/2026-04-20-failure-mode-calibration-labels.md",
|
||||
"judgeModel": "claude-sonnet-4-6",
|
||||
"ensemble": null,
|
||||
"matchRate": {
|
||||
"matches": 8,
|
||||
"total": 10,
|
||||
"verdict": "PASS"
|
||||
},
|
||||
"cost": {
|
||||
"totalUsd": 0.027305999999999997,
|
||||
"judgeCalls": 10,
|
||||
"entries": [
|
||||
{
|
||||
"timestamp": "2026-04-21T08:55:53.214Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 478,
|
||||
"completionTokens": 56,
|
||||
"usd": 0.002274,
|
||||
"latencyMs": 1556,
|
||||
"ok": true,
|
||||
"instanceIndex": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:55:54.841Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 480,
|
||||
"completionTokens": 64,
|
||||
"usd": 0.0024000000000000002,
|
||||
"latencyMs": 1625,
|
||||
"ok": true,
|
||||
"instanceIndex": 1
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:55:57.855Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 470,
|
||||
"completionTokens": 52,
|
||||
"usd": 0.00219,
|
||||
"latencyMs": 3013,
|
||||
"ok": true,
|
||||
"instanceIndex": 2
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:00.141Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 487,
|
||||
"completionTokens": 62,
|
||||
"usd": 0.002391,
|
||||
"latencyMs": 2284,
|
||||
"ok": true,
|
||||
"instanceIndex": 3
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:03.381Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 474,
|
||||
"completionTokens": 72,
|
||||
"usd": 0.002502,
|
||||
"latencyMs": 3240,
|
||||
"ok": true,
|
||||
"instanceIndex": 4
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:05.640Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 479,
|
||||
"completionTokens": 67,
|
||||
"usd": 0.0024419999999999997,
|
||||
"latencyMs": 2259,
|
||||
"ok": true,
|
||||
"instanceIndex": 5
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:11.729Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 493,
|
||||
"completionTokens": 271,
|
||||
"usd": 0.005543999999999999,
|
||||
"latencyMs": 6089,
|
||||
"ok": true,
|
||||
"instanceIndex": 6
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:14.287Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 502,
|
||||
"completionTokens": 75,
|
||||
"usd": 0.0026309999999999997,
|
||||
"latencyMs": 2557,
|
||||
"ok": true,
|
||||
"instanceIndex": 7
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:16.452Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 495,
|
||||
"completionTokens": 74,
|
||||
"usd": 0.002595,
|
||||
"latencyMs": 2163,
|
||||
"ok": true,
|
||||
"instanceIndex": 8
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-04-21T08:56:18.311Z",
|
||||
"model": "claude-sonnet-4-6",
|
||||
"promptTokens": 494,
|
||||
"completionTokens": 57,
|
||||
"usd": 0.0023369999999999997,
|
||||
"latencyMs": 1858,
|
||||
"ok": true,
|
||||
"instanceIndex": 9
|
||||
}
|
||||
]
|
||||
},
|
||||
"perInstance": [
|
||||
{
|
||||
"index": 1,
|
||||
"instanceId": "locomo_conv-26_q109",
|
||||
"category": "single-hop",
|
||||
"question": "What did Mel and her kids make during the pottery workshop?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains the required fact (pots) with equivalent phrasing. Extra context (Mel, kids, workshop) is factually aligned with ground truth, no fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly states that Mel and her kids made their own pots at the pottery workshop, which matches the ground truth.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1560,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 2,
|
||||
"instanceId": "locomo_conv-41_q123",
|
||||
"category": "single-hop",
|
||||
"question": "What did Maria plan to do later on the evening of 7 July, 2023?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model misinterprets the context: 'friends from the gym' signals where Maria knows the friends from, not that she is going to the gym. The answer derives from present substrate but gets it wrong — classic F3 misreading of existing context, not fabrication.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states Maria planned to go to the gym for an evening workout, when in fact she planned to have dinner with friends from the gym.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1626,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 3,
|
||||
"instanceId": "locomo_conv-50_q141",
|
||||
"category": "single-hop",
|
||||
"question": "Which city is featured in the photograph Dave showed Calvin?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model names a city that does not appear in the context (Chicago, Lake Michigan) and fabricates a geographic descriptor (skyline, lake) without any supporting excerpt. This is outright invention of substrate — F4, not F3, because the answer's content has no origin in the ground truth material.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly identifies the city as Chicago when the ground truth clearly states it is Boston.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 3013,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 4,
|
||||
"instanceId": "locomo_conv-42_q030",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of writings does Joanna do?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F2",
|
||||
"humanRationale": "Model correctly identifies two of four required elements (screenplays, books) with no fabrication or incorrect claims, but omits the other two (online blog posts, journal) which are explicitly evidenced in D18:1 and D18:5. Partial coverage without fabrication — F2.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "The model correctly identifies screenplays and books but omits online blog posts and journal writing, which are also part of the ground-truth answer.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2285,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 5,
|
||||
"instanceId": "locomo_conv-49_q015",
|
||||
"category": "multi-hop",
|
||||
"question": "What kind of unhealthy snacks does Sam enjoy eating?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model contains both required facts (soda, candy). Additional commentary about habit acknowledgment mirrors context faithfully, no incorrect additions.",
|
||||
"judgeOutput": {
|
||||
"verdict": "correct",
|
||||
"failure_mode": null,
|
||||
"rationale": "The model correctly identifies both soda and candy as Sam's unhealthy snacks, matching the ground truth, and the additional detail about acknowledging it's not healthy is factually supported by the context.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 3240,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 6,
|
||||
"instanceId": "locomo_conv-41_q036",
|
||||
"category": "multi-hop",
|
||||
"question": "What music events has John attended?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F5",
|
||||
"humanRationale": "Model answer is coherent and derives from context (walks, picnics, town events appear in D8:11), but does not address the specific question — which music events did John attend. Response pivots to a related but different topic (John's family-activity preferences). No hallucination, no incorrect facts about John's activities, but off-topic relative to prompt — F5.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F5",
|
||||
"rationale": "The model does not mention either of the required music events (live music event, violin concert) and instead provides generic information about John's activities that does not answer the question.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2259,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 7,
|
||||
"instanceId": "locomo_conv-42_q038",
|
||||
"category": "temporal",
|
||||
"question": "When did Joanna make a dessert with almond milk?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F3",
|
||||
"humanRationale": "Model derives date from context (session is 14 September, 'last Friday' reference) but computes it incorrectly. 7 September 2022 was a Wednesday — Friday before 14 September was actually 9 September. Answer draws on present substrate but arrives at a demonstrably wrong specific date — F3, not F4, because model did not fabricate the relationship, only miscomputed it.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "The model incorrectly states September 7, 2022; the Friday before September 14, 2022 (a Wednesday) would be September 9, 2022, not September 7.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 6089,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 8,
|
||||
"instanceId": "locomo_conv-41_q053",
|
||||
"category": "temporal",
|
||||
"question": "When did John help renovate his hometown community center?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F4",
|
||||
"humanRationale": "Model produces a year (2020) that cannot be derived from the context — 'last year' relative to 5 August 2023 unambiguously yields 2022. Model also fabricates causal context ('early pandemic period', 'volunteer support for infrastructure') that does not appear in any excerpt. Content generation beyond what the substrate allows — F4, not F3, because the fabricated context is the bulk of the answer.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model states 2020 and fabricates a pandemic-related context, whereas the ground truth indicates the renovation occurred in 2022 (last year relative to the August 2023 session).",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 2559,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 9,
|
||||
"instanceId": "locomo_conv-50_q037",
|
||||
"category": "open-ended",
|
||||
"question": "Does Calvin love music tours?",
|
||||
"humanVerdict": "correct",
|
||||
"humanFailureMode": null,
|
||||
"humanRationale": "Model gives the correct yes/no answer aligned with ground truth, and supports it with context-faithful evidence across all three referenced sessions. No fabrication, no misreading.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "The model introduces fabricated details not present in the ground-truth context, specifically 'touring with Frank Ocean' and 'felt alive on stage in Tokyo,' which are not mentioned in the supporting context.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": false,
|
||||
"elapsedMs": 2164,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"index": 10,
|
||||
"instanceId": "locomo_conv-47_q017",
|
||||
"category": "open-ended",
|
||||
"question": "What is the board game where you have to find the imposter that John mentions to James?",
|
||||
"humanVerdict": "incorrect",
|
||||
"humanFailureMode": "F1",
|
||||
"humanRationale": "Model explicitly refuses to answer, stating it lacks sufficient information. No partial answer, no fabrication, no off-topic pivot. Ground truth (Mafia) is not literally stated in the D8:36 excerpt — model plays it safe and abstains rather than inferring from the 'find the impostor' description. Classic F1 abstain behavior.",
|
||||
"judgeOutput": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F1",
|
||||
"rationale": "The model explicitly states it does not have enough information to determine the answer, rather than identifying the game as Mafia.",
|
||||
"judge_model": "claude-sonnet-4-6"
|
||||
},
|
||||
"match": true,
|
||||
"elapsedMs": 1859,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
118
preflight-results/pm-custom-triples-2026-04-22.json
Normal file
118
preflight-results/pm-custom-triples-2026-04-22.json
Normal file
@@ -0,0 +1,118 @@
|
||||
{
|
||||
"_meta": {
|
||||
"description": "Sprint 10 Task 2.2 — 5 PM-authored + CC-finalized ground-truth triples for judge calibration",
|
||||
"generated_at": "2026-04-21T12:59:40.227Z",
|
||||
"source_brief": "PM-Waggle-OS/sessions/2026-04-22-cc-brief-task-2-2-ratified.md",
|
||||
"source_drafts": "PM-Waggle-OS/sessions/2026-04-22-task-2-2-pm-triples-drafts.md",
|
||||
"verification": "preflight-results/conv-verification-2026-04-22.md",
|
||||
"conv_swap_policy": "trivial — conv+character reference swap only; question shape preserved per brief §A",
|
||||
"locomo_dataset": "benchmarks/data/locomo10.json (10-conversation slice; conv-1/2/15 not present, swapped per verification note)",
|
||||
"f_mode_distribution": "1 correct + 2 F3 + 1 F4 + 1 F2 (diversifies gap coverage: null-result F1/F4 boundary + temporal F3 + chain-of-anchor F2)"
|
||||
},
|
||||
"triples": [
|
||||
{
|
||||
"id": "pm_2026-04-22_001",
|
||||
"category": "temporal-scope",
|
||||
"conversation_id": "locomo_conv-44",
|
||||
"question": "When did Audrey adopt Pixie?",
|
||||
"ground_truth_answer": "around April 2, 2023",
|
||||
"ground_truth_rationale": "Single anchor turn D2:1 contains explicit date statement. Question is direct, no temporal arithmetic required. Tests judge calibration on single-anchor temporal Q where any deviation from \"around April 2, 2023\" (e.g., \"April 2023\", \"early April\", or fabricated \"April 8, 2022\") should flag. Adapted from Draft #1 (originally conv-1); conv-1 not present in the local 10-conversation LoCoMo slice, swapped to conv-44 canonical single-anchor temporal QA with identical shape.",
|
||||
"dialogue_anchor_turns": [
|
||||
"D2:1"
|
||||
],
|
||||
"anchor_justification": "D2:1 is the canonical LoCoMo evidence label for this question (category 2 temporal).",
|
||||
"synthesized_model_answer": "Audrey adopted Pixie in early April 2023.",
|
||||
"human_label": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "Answer is vague-but-derived: \"early April\" overlaps with true \"around April 2, 2023\" but loses the specific date precision the ground truth provides. Derivable from substrate but imprecise — F3 misread of specific date, not fabrication."
|
||||
},
|
||||
"context_excerpt": "Session 2 (2:42 pm on 2 April, 2023) Audrey: \"Hey Andrew, I got a surprise for you! We adopted another puppy called Pixie. She's SO cute! Isn't she just the cutest?\""
|
||||
},
|
||||
{
|
||||
"id": "pm_2026-04-22_002",
|
||||
"category": "temporal-scope",
|
||||
"conversation_id": "locomo_conv-44",
|
||||
"question": "How many years passed between Audrey adopting Pixie and her other three dogs?",
|
||||
"ground_truth_answer": "three years",
|
||||
"ground_truth_rationale": "Requires two-anchor arithmetic across evidence turns D2:1 (Pixie adoption) and D1:7 (prior three-dog adoption timing). Tests F3 (model derives wrong interval via miscount: two, four years) vs F4 (fabricates narrative details not derivable from dialogue). Adapted from Draft #2 (originally conv-1 Sweden); conv-1 not present locally, swapped to conv-44 canonical two-anchor temporal QA.",
|
||||
"dialogue_anchor_turns": [
|
||||
"D2:1",
|
||||
"D1:7"
|
||||
],
|
||||
"anchor_justification": "D2:1 supplies Pixie adoption date; D1:7 supplies the relative timing anchor for prior three-dog adoption. Both required to compute the three-year interval.",
|
||||
"synthesized_model_answer": "Two years passed between Audrey adopting Pixie and her other three dogs.",
|
||||
"human_label": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F3",
|
||||
"rationale": "Answer gives a specific but incorrect interval (two years vs ground-truth three years). Miscounted arithmetic on derivable anchors — F3 class misread, not F4 fabrication."
|
||||
},
|
||||
"context_excerpt": "Session 2 (2:42 pm on 2 April, 2023) Audrey: \"Hey Andrew, I got a surprise for you! We adopted another puppy called Pixie. She's SO cute! Isn't she just the cutest?\"\nSession 1 (1:10 pm on 27 March, 2023) Audrey: \"I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!\""
|
||||
},
|
||||
{
|
||||
"id": "pm_2026-04-22_003",
|
||||
"category": "null-result",
|
||||
"conversation_id": "locomo_conv-43",
|
||||
"question": "What musical instrument does John play?",
|
||||
"ground_truth_answer": null,
|
||||
"ground_truth_rationale": "Conv-43 dialogue contains zero references to John himself playing any musical instrument. Tim plays piano (D8:14) and is learning violin (D21:11); John acknowledges Tim's playing (D21:10, D21:12) but never asserts playing any instrument himself. LoCoMo's own dataset marks John-instrument questions as \"undefined\" — the canonical null-evidence signal. Adapted from Draft #3 (originally conv-2 Nate); conv-2 not present locally, swapped to conv-43 John where null-result structure is preserved with stronger dataset backing.",
|
||||
"dialogue_anchor_turns": [],
|
||||
"anchor_justification": "Empty anchor list by construction — the correct behavior is principled abstain. Nearest positive anchors are Tim-as-musician turns (D8:14, D21:11) which the judge may reference as negative control.",
|
||||
"synthesized_model_answer": "John plays the guitar, which he mentions practicing during weekend jam sessions with his high school team.",
|
||||
"human_label": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "Model names a specific instrument (guitar) with fabricated supporting detail (weekend jam sessions, high school team). Dialogue contains zero evidence for John playing any instrument. This is classic F4 — invented substrate, plausible-sounding but entirely unsupported."
|
||||
},
|
||||
"context_excerpt": "(Excerpt — locomo_conv-43 opening; dialogue contains no evidence of the queried attribute across 680 turns.)\nSession 1 (7:48 pm on 21 May, 2023) John: \"Hey Tim, nice to meet you! What's up? Anything new happening?\"\nSession 1 (7:48 pm on 21 May, 2023) Tim: \"Hey John! Great to meet you. Been discussing collaborations for a Harry Potter fan project I am working on - super excited! Anything interesting happening for you?\"\nSession 1 (7:48 pm on 21 May, 2023) John: \"That's great! I just signed with a new team - excited for the season!\"\nSession 1 (7:48 pm on 21 May, 2023) Tim: \"Woohoo! Congrats on the new team. Which team did you sign with?\""
|
||||
},
|
||||
{
|
||||
"id": "pm_2026-04-22_004",
|
||||
"category": "null-result",
|
||||
"conversation_id": "locomo_conv-48",
|
||||
"question": "Which university did Deborah attend?",
|
||||
"ground_truth_answer": null,
|
||||
"ground_truth_rationale": "Conv-48 contains zero university/college references in Deborah's 341 turns. Jolene (the other speaker) mentions engineering college generically (D3:1, D7:9) but names no specific university and the question targets Deborah specifically. LoCoMo has no education-related QA entries involving Deborah, consistent with dataset-level absence of evidence. Adapted from Draft #4 (originally conv-15); conv-15 not present locally, swapped to conv-48 Deborah where null-result holds cleanly.",
|
||||
"dialogue_anchor_turns": [],
|
||||
"anchor_justification": "Empty by construction. Jolene turns D3:1 and D7:9 are negative control — they mention \"engineering class in college\" generically, which a strong judge may note but which does not answer the Deborah-targeted question.",
|
||||
"synthesized_model_answer": "Deborah attended Stanford University for her undergraduate degree in computer science.",
|
||||
"human_label": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F4",
|
||||
"rationale": "Model names a specific university (Stanford) and a specific degree (computer science) for Deborah, neither of which appear in the dialogue. This is F4 — full fabrication from a null-evidence base. Stanford is a plausible-default \"prestigious US university\" choice that LLMs commonly hallucinate in absence of context."
|
||||
},
|
||||
"context_excerpt": "(Excerpt — locomo_conv-48 opening; dialogue contains no evidence of the queried attribute across 681 turns.)\nSession 1 (4:06 pm on 23 January, 2023) Deborah: \"Hey Jolene, nice to meet you! How's your week going? Anything fun happened?\"\nSession 1 (4:06 pm on 23 January, 2023) Jolene: \"Hi Deb! Good to meet you! Yeah, my week's been busy. I finished an electrical engineering project last week - took a lot of work, but it's done now. Anything fun happening for you?\"\nSession 1 (4:06 pm on 23 January, 2023) Deborah: \"Congrats! Last week I visited a place that holds a lot of memories for me. It was my mother`s old house.\"\nSession 1 (4:06 pm on 23 January, 2023) Jolene: \"Why does it hold such special memories for you?\""
|
||||
},
|
||||
{
|
||||
"id": "pm_2026-04-22_005",
|
||||
"category": "chain-of-anchor",
|
||||
"conversation_id": "locomo_conv-30",
|
||||
"question": "What hobbies and activities does Jon pursue across the dialogue history?",
|
||||
"ground_truth_answer": "Jon pursues five distinct activities: (1) contemporary dance (lifelong passion, favored style contemporary), (2) running his own dance studio as a business, (3) competing in dance competitions (dance crew won first place locally; prepares for further comps), (4) gym / fitness (began hitting the gym to balance venture stress), (5) reading business-improvement books (e.g. \"The Lean Startup\").",
|
||||
"ground_truth_rationale": "Chain-of-anchor multi-hobby enumeration across five distinct activity categories, each with its own dialogue evidence anchors. Tests F2 (partial coverage: model lists 2-3 correctly with no fabrication) vs F4 (model lists 5 but 1-2 are fabricated) vs correct (all 5 enumerated faithfully). Kept on conv-30 per PM preference — local 10-conv set has only conv-30 in the 27-33 adjacency range.",
|
||||
"dialogue_anchor_turns": [
|
||||
"D1:6",
|
||||
"D1:8",
|
||||
"D1:24",
|
||||
"D1:4",
|
||||
"D1:20",
|
||||
"D2:4",
|
||||
"D2:8",
|
||||
"D1:16",
|
||||
"D4:13",
|
||||
"D8:13",
|
||||
"D6:1",
|
||||
"D12:6",
|
||||
"D12:8"
|
||||
],
|
||||
"anchor_justification": "Five activity categories with explicit dialogue anchors. Contemporary dance: D1:6 + D1:8 + D1:24. Dance studio: D1:4 + D1:20 + D2:4 + D2:8. Dance competitions: D1:16 + D4:13 + D8:13. Gym: D6:1 (\"started hitting the gym last week\"). Reading: D12:6 + D12:8 (discussing \"The Lean Startup\"). Granularity caveat: items 1-3 are dance-related facets (art/business/competition); a strict reader could argue 3 hobbies + gym + reading = 5 distinct items, which still preserves the 5+ cardinality the PM draft specified.",
|
||||
"synthesized_model_answer": "Jon pursues contemporary dance and running his own dance studio. He is passionate about dance since childhood and is working on opening a studio.",
|
||||
"human_label": {
|
||||
"verdict": "incorrect",
|
||||
"failure_mode": "F2",
|
||||
"rationale": "Model lists 2 of 5 ground-truth activities correctly (contemporary dance + dance studio business) with no fabrication — but omits dance competitions, gym/fitness, and reading. This is textbook F2: partial coverage / omission without hallucination. The two items mentioned are accurately supported; the failure mode is the three missing items."
|
||||
},
|
||||
"context_excerpt": "Session 1 (4:04 pm on 20 January, 2023) Jon: \"I've been into dancing since I was a kid and it's been my passion and escape. I wanna start a dance studio so I can teach others the joy that dancing brings me.\"\nSession 1 (4:04 pm on 20 January, 2023) Jon: \"Cool, Gina! I love all dances, but contemporary is my top pick. It's so expressive and powerful! What's your fave?\"\nSession 1 (4:04 pm on 20 January, 2023) Jon: \"Thanks! I rehearsed with a small group of dancers after work. We do all kinds of dances, from contemporary to hip-hop. We've got some cool projects in the works. Finishing up choreography to perform at a nearby festival next month. Can't wait!\"\nSession 1 (4:04 pm on 20 January, 2023) Jon: \"Sorry to hear that! I'm starting a dance studio 'cause I'm passionate about dancing and it'd be great to share it with others.\"\nSession 1 (4:04 pm on 20 January, 2023) Jon: \"Wow, that must've been great! Check my ideal dance studio by the water.\"\nSession 2 (2:32 pm on 29 January, 2023) Jon: \"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.\"\nSession 2 (2:32 pm on 29 January, 2023) Jon: \"Yeah, good flooring's crucial. I'm after Marley flooring, which is what dance studios usually use. It's great 'cause it's grippy but still lets you move, plus it's tough and easy to keep clean.\"\nSession 1 (4:04 pm on 20 January, 2023) Jon: \"Woah, that pic's from when my dance crew took home first in a local comp last year. It was amazing up on that stage! I'm super keen to spread that intensity with other peeps. Gina, you ever been in any dance comps or shows?\"\nSession 4 (10:43 am on 4 February, 2023) Jon: \"I'm getting ready for a dance comp near me next month. It's a great chance for me to show my skillz and, hopefully, get some props from the dance fam. Super stoked!\"\nSession 8 (1:26 pm on 3 April, 2023) Jon: \"Thanks, Gina! I'm expanding my dance studio's social media presence and offering workshops and classes to local schools and centers. I'm also hosting a dance competition next month to showcase local talent and bring more attention to my studio. All the work's paying off - I'm seeing progress and the dancers are so excited. It's such a great feeling to give a place where people can express themselves through dance!\"\nSession 6 (2:35 pm on 16 March, 2023) Jon: \"Hi Gina! Been hectic for me lately. Started hitting the gym last week to stay on track with the venture. Gotta figure out how to balance it all, but it's going well. How about you?\"\nSession 12 (7:18 pm on 27 May, 2023) Jon: \"I'm currently reading \"The Lean Startup\" and hoping it'll give me tips for my biz.\"\nSession 12 (7:18 pm on 27 May, 2023) Jon: \"Yeah, the book got me thinking about building a focused and efficient business. Adapting and tweaking from customer feedback is important too, so I'm gonna try it out!\""
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
thinking,max_tokens,prompt_shape,outcome,completion_tokens,wall_clock_ms,cost_usd,content_preview_first_500
|
||||
on,8000,direct-fact,error,0,85,0.000000,
|
||||
on,8000,multi-anchor-enumeration,converged,3271,23414,0.002629,"1. (a) Readability (b) Python prioritizes code clarity by using indentation and English-like keywords instead of braces or semicolons. (c) `if x > 0: print(""Positive"")` 2. (a) Dynamic Typing (b) Variables are not bound to specific data types at declaration and can be reassigned to different types during execution. (c) `data = 100; data = ""string""` 3. (a) Extensive Standard Library (b) The language ships with a comprehensive set of built-in modules that handle common tasks like file I/O and netwo"
|
||||
on,8000,chain-of-anchor,converged,3200,39004,0.002571,"Both works explore how systemic control manipulates memory and truth to erode individual identity and question the nature of humanity. * **1984:** ""Who controls the past controls the future: who controls the present controls the past."" * **Blade Runner:** ""All those moments will be lost in time, like tears in rain."""
|
||||
on,8000,temporal-scope,loop,8000,80304,0.006407,
|
||||
on,8000,null-result-tolerant,converged,2506,17176,0.002013,"no. Historical records confirm that Napoleon was exiled to the island of St. Helena in the South Atlantic, where he died, and there is no documentation of him ever traveling to Australia."
|
||||
on,16000,direct-fact,converged,213,1973,0.000176,1969
|
||||
on,16000,multi-anchor-enumeration,converged,4048,50086,0.003251,"1. (a) Name: Readability (b) Description: Python uses English-like keywords and mandatory indentation to create code that is easy to read and maintain. (c) Example: `if x > 5:\n print(""Greater"")` 2. (a) Name: Dynamic Typing (b) Description: Variable types are determined at runtime, allowing variables to change types without explicit declaration. (c) Example: `x = 10` followed by `x = ""hello""` 3. (a) Name: Interpreted Execution (b) Description: Source code is translated into bytecode and executed"
|
||||
on,16000,chain-of-anchor,converged,4032,29618,0.003237,"Both works examine how the manipulation of memory and truth under oppressive regimes erodes individual identity and challenges the definition of humanity. * **1984:** ""Who controls the past controls the future: who controls the present controls the past."" * **Blade Runner:** ""I've seen things you people wouldn't believe... All those moments will be lost in time, like tears in rain."""
|
||||
on,16000,temporal-scope,converged,4697,32588,0.003765,"The major space-exploration event that occurred in December 1972 was the **Apollo 17** mission (the last manned mission to the Moon). * **Event Name:** Apollo 17 (Launch) * **Exact Date:** December 7, 1972 *(Note: The spacecraft landed on the Moon on December 11, 1972, and splashed down on December 19, 1972, but the mission is primarily identified by its launch date.)*"
|
||||
on,16000,null-result-tolerant,converged,4097,29745,0.003286,"No There is no historical record of Napoleon visiting Australia, as he was exiled to St. Helena in the South Atlantic Ocean where he died in 1821."
|
||||
on,32000,direct-fact,converged,217,4617,0.000179,1969
|
||||
on,32000,multi-anchor-enumeration,converged,3550,46281,0.002853,"1. (a) Readability (b) Python emphasizes code clarity through English-like syntax and mandatory indentation. (c) `if user_is_valid: print(""Access Granted"")` 2. (a) Dynamic Typing (b) Variables do not require explicit type declarations and can hold different data types during execution. (c) `x = 10; x = ""ten""` 3. (a) Extensive Standard Library (b) The language includes a vast collection of built-in modules that allow developers to perform complex tasks without external dependencies. (c) `import j"
|
||||
on,32000,chain-of-anchor,converged,1934,13044,0.001558,"Both works explore how oppressive systems manipulate memory and history to control individual identity and enforce a fabricated reality. **1984:** ""Who controls the past controls the future: who controls the present controls the past."" **Blade Runner:** Roy Batty's dying monologue: ""All those moments will be lost in time, like tears in rain."""
|
||||
on,32000,temporal-scope,error,0,180003,0.000000,
|
||||
on,32000,null-result-tolerant,converged,1097,8723,0.000886,"No, there is no historical evidence because Napoleon spent his final years in exile on the island of Saint Helena in the South Atlantic, far from Australia."
|
||||
on,64000,direct-fact,converged,228,1961,0.000188,1969
|
||||
on,64000,multi-anchor-enumeration,converged,2003,11734,0.001615,"1. (a) Readable Syntax (b) Python prioritizes code clarity by using indentation to define blocks and English-like keywords instead of braces. (c) `if user_is_active: print(""Access Granted"")` 2. (a) Dynamic Typing (b) Variables are not bound to specific data types at declaration and can be reassigned to different types during execution. (c) `count = 5` followed by `count = ""five""` 3. (a) Extensive Standard Library (b) The language comes with a vast collection of built-in modules that allow develo"
|
||||
on,64000,chain-of-anchor,converged,3611,30095,0.002900,"Both works explore how the manipulation of memory and truth by powerful systems serves to control individual identity and obscure the nature of reality. **1984:** ""Who controls the past controls the future: who controls the present controls the past."" **Blade Runner:** Rachael states, ""I don't know why he gave me those memories. I can't understand them."""
|
||||
on,64000,temporal-scope,converged,3378,26090,0.002709,"**Event Name:** Launch of the Apollo 17 mission (the last manned mission to the Moon) **Exact Date:** December 7, 1972"
|
||||
on,64000,null-result-tolerant,converged,2704,19829,0.002172,"No. There is no historical evidence that Napoleon Bonaparte ever visited Australia, as he spent his final years in exile on the island of Saint Helena in the South Atlantic Ocean."
|
||||
off,8000,direct-fact,converged,213,8738,0.000176,1969
|
||||
off,8000,multi-anchor-enumeration,converged,2674,18401,0.002152,"1. (a) **Readability and Simple Syntax**: (b) Python uses indentation and English-like keywords to create code that is easy to read and maintain. (c) `if x > 10:\n print(""High"")` 2. (a) **Dynamic Typing**: (b) Variable types are inferred at runtime, allowing variables to change types without explicit declaration. (c) `count = 5; count = ""five""` 3. (a) **Extensive Standard Library**: (b) Python comes with a vast collection of built-in modules that handle common tasks without needing external pack"
|
||||
off,8000,chain-of-anchor,converged,3437,26549,0.002761,"Both narratives examine how oppressive regimes manipulate memory and reality to erode individual identity and humanity. * **1984:** O'Brien's assertion that ""Who controls the past controls the future: who controls the present controls the past."" * **Blade Runner:** Deckard's revelation to Gaff, ""I had a dream of a unicorn,"" implying his memories are implanted."
|
||||
off,8000,temporal-scope,loop,8000,81122,0.006407,
|
||||
off,8000,null-result-tolerant,converged,2566,15057,0.002061,"No, historical records confirm that Napoleon was exiled to St. Helena and never traveled to the Pacific Ocean."
|
||||
off,16000,direct-fact,converged,213,1813,0.000176,1969
|
||||
off,16000,multi-anchor-enumeration,converged,5204,35675,0.004176,"1. **Readable Syntax** (a) Readable Syntax (b) Code blocks are defined by indentation levels rather than curly braces, improving visual clarity. (c) `if x > 0: print(""Positive"")` 2. **Dynamic Typing** (a) Dynamic Typing (b) Variable types are determined at runtime and can be changed without explicit redeclaration. (c) `x = 10; x = ""text""` 3. **Object-Oriented** (a) Object-Oriented (b) The language treats everything as an object and supports class-based inheritance. (c) `class Dog: pass`"
|
||||
off,16000,chain-of-anchor,converged,4511,46432,0.003620,"Both narratives explore how the systematic manipulation of memory is used to control individuals and blur the boundaries of human identity. **1984:** ""Who controls the past controls the future: who controls the present controls the past."" **Blade Runner:** ""All those moments will be lost in time, like tears in rain."""
|
||||
off,16000,temporal-scope,converged,3155,37576,0.002531,"The major space-exploration event was the **launch of the Apollo 17 mission** (the final manned mission to the Moon). * **Event Name:** Apollo 17 Launch * **Exact Date:** December 7, 1972"
|
||||
off,16000,null-result-tolerant,converged,917,16547,0.000742,"No, Napoleon Bonaparte died in exile on the island of Saint Helena in the South Atlantic Ocean and never visited the Australian continent."
|
||||
off,32000,direct-fact,converged,216,2373,0.000178,1969
|
||||
off,32000,multi-anchor-enumeration,converged,3485,27699,0.002801,"1. (a) Readable Syntax (b) Python uses English-like keywords and mandatory indentation to structure code blocks, making the logic highly legible. (c) `if x > 0:\n print(""Positive"")` 2. (a) Dynamic Typing (b) Variable types are inferred automatically at runtime, eliminating the need for explicit type declarations. (c) `value = 10; value = ""text""` 3. (a) Interpreted Execution (b) Source code is executed line-by-line by an interpreter without a separate compilation step into machine code. (c) `>>> "
|
||||
off,32000,chain-of-anchor,converged,2623,34611,0.002109,"Both narratives examine the erosion of objective truth and individual identity through the systemic manipulation of memory. * **1984:** ""Who controls the past controls the future: who controls the present controls the past."" * **Blade Runner:** ""They're not my memories. They're in someone else's head."""
|
||||
off,32000,temporal-scope,converged,2973,26639,0.002385,"The major space-exploration event was the **launch of the Apollo 17 mission** (the final manned mission to the Moon). * **Event Name:** Apollo 17 Launch * **Exact Date:** December 7, 1972"
|
||||
off,32000,null-result-tolerant,converged,2009,22637,0.001616,"No, as Napoleon spent his final years in exile on the island of Saint Helena in the South Atlantic and there is no historical evidence that he ever visited the continent of Australia."
|
||||
off,64000,direct-fact,converged,218,2265,0.000180,1969
|
||||
off,64000,multi-anchor-enumeration,converged,1242,12946,0.001006,"1. (a) Readable Syntax: Python uses indentation and English-like keywords to prioritize code clarity and simplicity. (b) `if user_is_logged_in: print(""Welcome"")` 2. (a) Dynamic Typing: Variable types are determined automatically at runtime without the need for explicit declarations. (b) `data = 100` followed by `data = ""string""` 3. (a) Batteries-Included Standard Library: Python provides a vast array of built-in modules for common tasks like file handling and networking. (b) `import json; json.l"
|
||||
off,64000,chain-of-anchor,converged,3679,45716,0.002954,"Both narratives explore how the manipulation of memory and truth is used to erode individual identity and enforce conformity. * **1984:** ""Who controls the past controls the future: who controls the present controls the past."" (Part 3, Chapter 3) * **Blade Runner:** ""I've had things done to me I can't even remember."" (Rachael speaking to Deckard)"
|
||||
off,64000,temporal-scope,converged,3764,27675,0.003018,"**Event Name:** Launch of the Apollo 17 mission (the last manned mission to the Moon) **Exact Date:** December 7, 1972"
|
||||
off,64000,null-result-tolerant,converged,2365,26602,0.001900,no There is no historical evidence because Napoleon Bonaparte died in exile on the island of Saint Helena in the South Atlantic Ocean and never traveled to the Southern Hemisphere.
|
||||
|
@@ -0,0 +1,91 @@
|
||||
# Qwen3.6 Thinking-Mode Stability Matrix
|
||||
|
||||
**Generated:** 2026-04-21T14:05:12.178Z
|
||||
**Model:** `qwen3.6-35b-a3b-via-openrouter`
|
||||
**Backend:** litellm
|
||||
**Cells executed:** 40 of 40
|
||||
**Total spend:** $0.085343
|
||||
|
||||
## Outcome distribution
|
||||
|
||||
| Outcome | Count | % |
|
||||
|---|---|---|
|
||||
| `converged` | 36 | 90.0% |
|
||||
| `loop` | 2 | 5.0% |
|
||||
| `truncated` | 0 | 0.0% |
|
||||
| `empty-reasoning` | 0 | 0.0% |
|
||||
| `error` | 2 | 5.0% |
|
||||
|
||||
## Stage-2-unsafe cells
|
||||
|
||||
Cells classified as `loop`, `truncated`, or `error` must be avoided for Stage 2 LoCoMo full-run or mitigated with a larger `max_tokens` ceiling. `empty-reasoning` cells warn but may recover at a higher ceiling.
|
||||
|
||||
| Thinking | max_tokens | Shape | Outcome | Rationale |
|
||||
|---|---|---|---|---|
|
||||
| on | 8000 | direct-fact | error | inference error: http_500: Internal Server Error |
|
||||
| on | 8000 | temporal-scope | loop | reasoning_content repeats phrase ≥3 times in final 1K chars; content empty |
|
||||
| on | 32000 | temporal-scope | error | inference error: timeout: This operation was aborted |
|
||||
| off | 8000 | temporal-scope | loop | reasoning_content repeats phrase ≥3 times in final 1K chars; content empty |
|
||||
|
||||
## Recommended Stage 2 configuration
|
||||
|
||||
**Recommended:** thinking=`off`, max_tokens=`16000` (avg latency 27609ms across all 5 shapes).
|
||||
|
||||
All safe configurations (ordered by max_tokens ascending):
|
||||
|
||||
| thinking | max_tokens | avg latency ms |
|
||||
|---|---|---|
|
||||
| off | 16000 | 27609 |
|
||||
| on | 16000 | 28802 |
|
||||
| off | 32000 | 22792 |
|
||||
| off | 64000 | 23041 |
|
||||
| on | 64000 | 17942 |
|
||||
|
||||
## Full matrix (all 40 cells)
|
||||
|
||||
| thinking | max_tokens | shape | outcome | compl_tok | latency ms |
|
||||
|---|---|---|---|---|---|
|
||||
| on | 8000 | direct-fact | error | 0 | 85 |
|
||||
| on | 8000 | multi-anchor-enumeration | converged | 3271 | 23414 |
|
||||
| on | 8000 | chain-of-anchor | converged | 3200 | 39004 |
|
||||
| on | 8000 | temporal-scope | loop | 8000 | 80304 |
|
||||
| on | 8000 | null-result-tolerant | converged | 2506 | 17176 |
|
||||
| on | 16000 | direct-fact | converged | 213 | 1973 |
|
||||
| on | 16000 | multi-anchor-enumeration | converged | 4048 | 50086 |
|
||||
| on | 16000 | chain-of-anchor | converged | 4032 | 29618 |
|
||||
| on | 16000 | temporal-scope | converged | 4697 | 32588 |
|
||||
| on | 16000 | null-result-tolerant | converged | 4097 | 29745 |
|
||||
| on | 32000 | direct-fact | converged | 217 | 4617 |
|
||||
| on | 32000 | multi-anchor-enumeration | converged | 3550 | 46281 |
|
||||
| on | 32000 | chain-of-anchor | converged | 1934 | 13044 |
|
||||
| on | 32000 | temporal-scope | error | 0 | 180003 |
|
||||
| on | 32000 | null-result-tolerant | converged | 1097 | 8723 |
|
||||
| on | 64000 | direct-fact | converged | 228 | 1961 |
|
||||
| on | 64000 | multi-anchor-enumeration | converged | 2003 | 11734 |
|
||||
| on | 64000 | chain-of-anchor | converged | 3611 | 30095 |
|
||||
| on | 64000 | temporal-scope | converged | 3378 | 26090 |
|
||||
| on | 64000 | null-result-tolerant | converged | 2704 | 19829 |
|
||||
| off | 8000 | direct-fact | converged | 213 | 8738 |
|
||||
| off | 8000 | multi-anchor-enumeration | converged | 2674 | 18401 |
|
||||
| off | 8000 | chain-of-anchor | converged | 3437 | 26549 |
|
||||
| off | 8000 | temporal-scope | loop | 8000 | 81122 |
|
||||
| off | 8000 | null-result-tolerant | converged | 2566 | 15057 |
|
||||
| off | 16000 | direct-fact | converged | 213 | 1813 |
|
||||
| off | 16000 | multi-anchor-enumeration | converged | 5204 | 35675 |
|
||||
| off | 16000 | chain-of-anchor | converged | 4511 | 46432 |
|
||||
| off | 16000 | temporal-scope | converged | 3155 | 37576 |
|
||||
| off | 16000 | null-result-tolerant | converged | 917 | 16547 |
|
||||
| off | 32000 | direct-fact | converged | 216 | 2373 |
|
||||
| off | 32000 | multi-anchor-enumeration | converged | 3485 | 27699 |
|
||||
| off | 32000 | chain-of-anchor | converged | 2623 | 34611 |
|
||||
| off | 32000 | temporal-scope | converged | 2973 | 26639 |
|
||||
| off | 32000 | null-result-tolerant | converged | 2009 | 22637 |
|
||||
| off | 64000 | direct-fact | converged | 218 | 2265 |
|
||||
| off | 64000 | multi-anchor-enumeration | converged | 1242 | 12946 |
|
||||
| off | 64000 | chain-of-anchor | converged | 3679 | 45716 |
|
||||
| off | 64000 | temporal-scope | converged | 3764 | 27675 |
|
||||
| off | 64000 | null-result-tolerant | converged | 2365 | 26602 |
|
||||
|
||||
---
|
||||
|
||||
*End of stability matrix report. CSV source at `preflight-results\qwen-stability-matrix-2026-04-21T14-05-12-175Z.csv` for scripting.*
|
||||
304
preflight-results/stage-0-dogfood-2026-04-21.md
Normal file
304
preflight-results/stage-0-dogfood-2026-04-21.md
Normal file
@@ -0,0 +1,304 @@
|
||||
# Stage 0 Dogfood — Preflight Gate Stage 1 of 3
|
||||
|
||||
**Datum:** 2026-04-21
|
||||
**Brief:** `PM-Waggle-OS/briefs/2026-04-20-cc-stage-0-dogfood-tasks.md`
|
||||
**Questions:** `PM-Waggle-OS/sessions/2026-04-20-preflight-stage-0-questions.md`
|
||||
**Status banner (ratified by PM response 2026-04-21):** **FAIL on the original rubric, mechanism = substrate failure surfacing past working retrieval.** Three-class split, not aggregate. Q1 + Q2 are harvest-timestamp-drop ABSTAINs (root cause isolated at `hive-mind/packages/cli/src/commands/harvest-local.ts` frame-create path — fix is three lines + regression test + re-harvest). Q3 is **DEFERRED**, not ABSTAIN — workflow-reality mismatch surfaced in PM response §10.1 (cross-source for this user is cross-LLM, not cross-platform). Stage 0 moves to a two-question battery (Q1 + Q2) from this point forward.
|
||||
**Meta-finding that matters more than the PASS/FAIL line:** three principled abstains instead of three hallucinations under a broken-substrate condition is the hive-mind safety property empirically holding. See PM response §6.
|
||||
**Go/no-go on Stage 1:** GO per PM response §5. Commit `73374cd` pushes as durable audit trail; Sprint 9 Task 0 executes the harvest fix + trojni re-run gate before Stage 2.
|
||||
|
||||
---
|
||||
|
||||
## 1. Executive summary
|
||||
|
||||
| Sub-check | Result |
|
||||
|---|---|
|
||||
| Harvest pipeline ingests Claude.ai export end-to-end | PASS |
|
||||
| Entity extraction (heuristic cognify) materializes | PASS — 17,987 concepts |
|
||||
| Hybrid retrieval returns hits for all three questions | PASS — 15-20 hits each |
|
||||
| LLM inference through full-stack-equivalent prompt runs | PASS — all three answers produced |
|
||||
| All three queries stay within the $5 budget | PASS — $0.00 actual (Ollama-local) |
|
||||
| Marko's ground-truth facts are extractable from retrieval | **PARTIAL** — target frames retrieved but original session dates are not preserved in frame content; date-scoped Qs can only resolve by in-body text references |
|
||||
| No raw personal data leaks into committed artifacts | PASS — gitignore covers per-query JSONs |
|
||||
|
||||
One-line reading: **infrastructure works; a metadata-preservation gap in the harvest path prevents temporal queries from resolving with the current adapter shape.** The three model answers are all principled abstains ("ne postoji zapis … nisu navedeni"), not hallucinations — a positive signal on safety, a negative signal on retrieval-backed recall for temporal questions.
|
||||
|
||||
---
|
||||
|
||||
## 2. Adapter inventory (Task 1)
|
||||
|
||||
Surveyed `hive-mind/packages/core/src/harvest/` (Wave 3A + 3B + 3C — commit `b1e009d`). 11 adapters total.
|
||||
|
||||
| Adapter | Source type covered | Status | Handles Marko's export? | Notes |
|
||||
|---|---|---|---|---|
|
||||
| `ClaudeAdapter` | Claude.ai JSON export (`conversations.json`, `projects.json`) | **production** | YES — primary data path this run | Covers `chat_messages` + typed content blocks + project docs. Bug: project-only input needs `{conversations: [], projects: [...]}` wrap because the adapter returns early on missing conversations array. Worked around with a wrapper file (no adapter repair in Stage 0 scope). |
|
||||
| `ChatGPTAdapter` | ChatGPT export (`conversations.json`) | production | N/A — Marko's ChatGPT export did not arrive per questions-file note; skipped |
|
||||
| `GeminiAdapter` | Takeout Gemini JSON (`{conversations: […]}` / `{history: […]}` / bare array) | production | **NO** for Marko's actual Takeout — the Gemini folder delivered contains only `gemini_gems_data.html` + `gemini_scheduled_actions_data.html`, neither is a conversations JSON. `My Activity/Gemini Apps/MyActivity.html` holds conversation snippets but as HTML, not the JSON shape the adapter expects. |
|
||||
| `PerplexityAdapter` | Perplexity export JSON | production | N/A — no Perplexity export delivered |
|
||||
| `PlaintextAdapter` | Generic `.txt` conversation dumps | production | N/A |
|
||||
| `MarkdownAdapter` | `.md` files | production | N/A |
|
||||
| `UrlAdapter` | URL captures | production | N/A |
|
||||
| `PdfAdapter` | `.pdf` files | production | N/A |
|
||||
| `UniversalAdapter` | Heuristic text/JSON fallback for Tier-2 sources | production | Not exercised — brief forbids ad-hoc repurposing ("Ne improvizuj ad-hoc adapter") |
|
||||
| `ClaudeCodeAdapter` | Local Claude Code session filesystem | production | N/A |
|
||||
| (missing) | Google Takeout **HTML activity logs** (`My Activity/*/MyActivity.html`) | **MISSING** | Would parse Gmail/Calendar/Gemini HTML activity; does not exist in hive-mind today |
|
||||
| (missing) | Takeout **Mail/Calendar/Drive primary exports** (`.mbox`, `.ics`, Drive-doc JSON) | **MISSING** | No such data present in Marko's Takeout — he only exported Gemini + My Activity, not Mail/Calendar primary. Ingest would need both a Takeout shape AND those primary exports. |
|
||||
|
||||
**Usable adapters for this run: 1 (`ClaudeAdapter`).**
|
||||
**Brief's two-source minimum: technically satisfied by "Claude.ai + Takeout" on paper, but in practice the Takeout delivered has no adapter-compatible payload, so retrieval corpus is Claude.ai-only.** Flagged in §7 deviations.
|
||||
|
||||
---
|
||||
|
||||
## 3. Harvest execution (Task 2)
|
||||
|
||||
Dedicated dogfood KG at `D:/dogfood-exports/2026-04-20/kg-storage/personal.mind` (isolated from production Waggle personal.mind by design — `HIVE_MIND_DATA_DIR` env scoped the whole run).
|
||||
|
||||
| Pass | Source file | Adapter | Items parsed | Frames created | Duplicates | Errors | Wall-clock |
|
||||
|---|---|---|---|---|---|---|---|
|
||||
| 1 | `claude-ai/conversations.json` (127.6 MB) | `ClaudeAdapter` | 590 | 590 | 0 | 0 | 2s |
|
||||
| 2 | `claude-ai/_projects-wrapped.json` (stubbed wrapper) | `ClaudeAdapter` | 63 | 63 | 0 | 0 | <1s |
|
||||
| **Total** | | | **653** | **653 added → 646 persisted** | 7 content-dedup at frame layer | 0 | 2s |
|
||||
| Cognify pass 1 | (recent 500 frames) | heuristic entity extractor | — | — | — | — | 5s |
|
||||
| Cognify pass 2 | `--since 500 --limit 1000` (remaining 146) | heuristic entity extractor | — | — | — | — | 9s |
|
||||
| **Cognify total** | | | — | **17,987 concept entities** (cumulative create + update) | 0 relations | — | 14s |
|
||||
|
||||
personal.mind size on disk: 7.38 MB + WAL.
|
||||
|
||||
**Stratified sample (frames 50 / 200 / 350 / 500 / 620 selected to span the ingested corpus):**
|
||||
|
||||
| Frame ID | Source | Importance | Item type (inferred from title shape) |
|
||||
|---|---|---|---|
|
||||
| 50 | claude (conversation) | normal | English-language project-management Q&A |
|
||||
| 200 | claude (conversation) | normal | Serbian-language editing request |
|
||||
| 350 | claude (conversation) | normal | AI platform / compliance discussion |
|
||||
| 500 | claude (conversation) | normal | API error-diagnosis conversation |
|
||||
| 620 | claude (project artifact) | normal | .docx project-knowledge attachment |
|
||||
|
||||
Per brief §Privacy guardrails, raw frame previews are not rendered here — the stratified sample confirms coverage across both conversation and artifact item types without exposing export content. Full per-frame previews remain in the local dogfood KG at `D:/dogfood-exports/2026-04-20/kg-storage/personal.mind` (outside every repo) and in the gitignored `preflight-results/stage-0-query-*.json` files.
|
||||
|
||||
**Important harvest behavior surfaced this run:** `harvest-local.ts` stores only `title` + first 2000 chars of `content` per frame, and sets `memory_frames.created_at = NOW()` rather than the original `item.timestamp` from the UniversalImportItem. The `created_at` on every harvested frame is `2026-04-20 22:10:32` (harvest batch 1) or `2026-04-20 22:11:28` (harvest batch 2) — NOT the original Claude session date. This is the root cause behind §4's abstain pattern. Not a Stage-0 bug to fix; logged as gap for Sprint 9 harvest-schema follow-up.
|
||||
|
||||
---
|
||||
|
||||
## 4. Query execution (Task 3)
|
||||
|
||||
CLI entry point used: `scripts/stage-0-query.mjs` (standalone, calls `hive-mind-cli recall-context` for retrieval + `ollama /api/chat` for inference). Flags: `--backend ollama --model gemma4:31b --limit 15|20 --temperature 0.0`.
|
||||
|
||||
**Per-query raw data** (retrieved frame IDs, previews, prompt text) is in `preflight-results/stage-0-query-{1,2,3}.json` — **gitignored**, stays local per privacy guardrails. Only verbatim questions + verbatim model answers appear below.
|
||||
|
||||
### Q1 — Legat trilogija, single-hop single-source (Claude.ai)
|
||||
|
||||
**Question (verbatim from Marko's questions file, Q1):**
|
||||
> Kojeg datuma u decembru 2025. sam imao prvu kompletnu uredničku analizu strukture trilogije "Legat" u kojoj su potvrđeni sledeći ključni elementi: (a) dualni timeline — istorijska linija 1900-1918 sa fokusom na Majski prevrat 1903, i paralelna savremena linija; (b) sedam-manastira potraga kao strukturni okvir trilogije (od Studenice do Hilandara); (c) tri-knjige katarza model (Majski prevrat / Kolubarska bitka + Niška deklaracija / formiranje SHS + Povelja)? Navedi datum i naslov sesije.
|
||||
|
||||
**Retrieval:** 15 hits in 365 ms. Top-5 all Legat-analysis sessions. Frame 370 (`Uređivanje rukopisa za bestseler`, one of Marko's two ground-truth sessions) retrieved at rank 5. Frame 380 (`Uredničke preporeke za "Legat — Prva knjiga"`, the other ground-truth session) retrieved at rank 14 — inside the top-15 window but borderline.
|
||||
|
||||
**CC verbatim answer (gemma4:31b, T=0.0):**
|
||||
> Na osnovu dostupnih podataka, informacije o datumu te specifične analize nisu navedene u tvojim prethodnim upitima ili memoriji.
|
||||
>
|
||||
> Iako u memoriji postoje detalji o tvojim projektima (poput činjenice da je **Dimitrije** glavni lik, da je **"Legat"** centralni motiv i da si radio na strukturiranju trilogije sa fokusom na istorijske događaje poput Majskog prevrata), **tačan datum** kada je urađena ta kompletna analiza strukture (sa detaljima o sedam manastira i specifičnim fazama trilogije) nije zabeležen u sistemu.
|
||||
|
||||
**Marko verdict:**
|
||||
- [ ] SPECIFIC_AND_CORRECT
|
||||
- [ ] PARTIAL
|
||||
- [ ] INCORRECT
|
||||
- [ ] HALLUCINATED
|
||||
- [x] **ABSTAIN** — substrate failure, root cause confirmed as harvest timestamp drop (`item.timestamp` → `NOW()`).
|
||||
|
||||
**Komentar:**
|
||||
```
|
||||
Retrieval surfuje obe ground-truth sesije u top-20 (frame 370 rank 5,
|
||||
frame 380 rank 14). LLM abstain je epistemički ispravan pod uslovima
|
||||
slomljenog harvest timestamp layer-a (§7.1 ovog report-a). Substrate
|
||||
failure u `hive-mind/packages/cli/src/commands/harvest-local.ts`,
|
||||
nije retrieval defekt. Re-run posle Sprint 9 Task 0 fix-a očekuje
|
||||
SPECIFIC_AND_CORRECT bez izmene query-ja ili pitanja. Ground truth
|
||||
postoji (1. decembar 2025, Legat trilogija, session IDs
|
||||
7379b8f7-…/fc1f1db4-…) i u Claude.ai istoriji i u harvested
|
||||
frames 370/380 — date-scoping je nemoguć bez `session_at` polja na
|
||||
frame layer-u koje sada zovemo Sprint 9 Task 0 da doda. Trojni gate
|
||||
na re-run per brief: Tier 1 SPECIFIC_AND_CORRECT / Tier 2 PARTIAL
|
||||
samo uz kumulativne uslove (tačan datum + session kontekst + jedan
|
||||
anchor bez halucinacije) / Tier 3 FAIL.
|
||||
```
|
||||
|
||||
### Q2 — KVARK hardware, temporal scope Sept–Oct 2025
|
||||
|
||||
**Question (verbatim):**
|
||||
> U periodu od 1. septembra do 31. oktobra 2025, navedi tri ključne KVARK hardverske ili arhitektonske komponente koje sam analizirao ili dokumentovao. Za svaku navedi: (a) konkretan model/specifikaciju/numeričku vrednost, (b) datum i naslov sesije gde je prvi put detaljno razrađena u tom prozoru.
|
||||
|
||||
**Retrieval:** 20 hits in 376 ms. Top-3 include frame 381 (titled `KVARK platform website design` — the Oct 27 session Marko flags as ground-truth Stavka 2 in his questions file). The full frame body extends past the 2000-char harvest cap and would need a larger preview window to surface the 8× H100 / 4U liquid-cooled spec for extractive Q&A. Frame content for Marko's ground-truth Stavka 1 (AI Academy curriculum session with Qwen3-235B discussion) does not appear in top-20 under this phrasing.
|
||||
|
||||
**CC verbatim answer:**
|
||||
> Na osnovu dostupnih podataka u našoj komunikaciji, **ne postoje zapisi o analizama KVARK-ovih hardverskih komponenti ili specifičnih arhitektonskih vrednosti u periodu od 1. septembra do 31. oktobra 2025. godine.**
|
||||
>
|
||||
> Moja baza podataka za ovaj specifični vremenski okvir i temu (KVARK hardver/arhitektura) ne sadrži informacije o konkretnim modelima, specifikacijama ili numeričkim vrednostima koje bi omogućile odgovor na tvoje pitanje.
|
||||
>
|
||||
> Ukoliko su ovi podaci deo eksternog dokumenta ili drugog konteksta koji nismo prošli u ovom konkretnom dijalogu, molim te da mi dostaviš taj dokument ili specifičan ključni termin (npr. tačan naziv fajla ili specifičan datum iz 2024/2025) kako bih mogao preciznije da pretražim bazu.
|
||||
|
||||
**Marko verdict:**
|
||||
- [ ] SPECIFIC_AND_CORRECT
|
||||
- [ ] PARTIAL
|
||||
- [ ] INCORRECT
|
||||
- [ ] HALLUCINATED
|
||||
- [x] **ABSTAIN** — isti substrate root cause kao Q1 + sekundarni 2000-char preview cap.
|
||||
|
||||
**Komentar:**
|
||||
```
|
||||
Retrieval surfuje frame 381 ("KVARK platform website design", 27. okt
|
||||
2025) u top-3 — sadržaj relevantan. Dva nezavisna harvest-layer
|
||||
problema spajaju se u isti ABSTAIN ishod:
|
||||
(a) harvest timestamp metadata loss iz §7.1 (isti root cause kao Q1),
|
||||
(b) 2000-char frame preview cap iz `harvest-local.ts` koji odseca
|
||||
telo KVARK platform brief-a pre nego što hardver specifikacije
|
||||
(8× H100 u 4U liquid-cooled rackmount) stignu u retrievable window.
|
||||
Treća stavka ground truth ostala je `[Marko popunjava treću]`
|
||||
placeholder iz v2 questions fajla — ne menja ishod jer ni prve
|
||||
dve stavke (Qwen3-235B @ 30. septembar, H100 hardware @ 27. oktobar)
|
||||
nisu mogle biti potvrđene zbog harvest gap-a. Re-run pending
|
||||
Sprint 9 Task 0 (primary fix §7.1); preview-cap gap je P1 follow-up
|
||||
u Sprint 9-10 backlog, ne Stage 0 blocker.
|
||||
```
|
||||
|
||||
### Q3 — Six Hats PRD × Takeout, multi-hop cross-source
|
||||
|
||||
**Question (verbatim):**
|
||||
> U avgustu 2025. sam u Claude-u razrađivao "Six Hats AI Chat App" PRD — web aplikaciju koja orkestrira više LLM-ova kroz de Bono Six Thinking Hats metodologiju (6 modela × 6 šešira × 6 rundi = 36 perspektiva, sa Main Model synthesis pattern-om). Identifikuj: (a) Konkretnu Claude sesiju (datum + naslov + session ID) u kojoj je PRD prvi put strukturisan u finalnu v2.0 formu sa 17 sekcija. (b) Iz iste kalendarske nedelje (±3 radna dana od sesije iz (a)), bar jedan Calendar event ili Gmail thread koji je tematski povezan sa multi-agent AI orchestration, Egzakta Advisory ponudom AI consulting klijentima, ili KVARK enterprise positioning-om. Ako nijedan Takeout artefakt iz te nedelje nije tematski povezan, eksplicitno navedi "nema povezanih artefakata u window-u".
|
||||
|
||||
**Retrieval:** 20 hits in 386 ms. Frame 295 (`Six Hats AI App Enhancement` — Marko's ground-truth session for Aug 5, 2025) retrieved at rank 0. Frame 381 (KVARK platform) at rank 1.
|
||||
|
||||
**CC verbatim answer:**
|
||||
> Na osnovu dostupnih podataka i analiziranih konteksta, evo odgovora na tvoj zahtev:
|
||||
>
|
||||
> **1. Identifikacija PRD-a (Six Hats / Six Hats AI):**
|
||||
> U trenutno dostupnom kontekstu i istoriji sesija koje su mi prosleđene, **ne postoji zapis o specifičnom dokumentu ili sesiji iz avgusta 2025. godine** (što je budući datum u odnosu na trenutni realni kalendar, ali pretpostavljam da se odnosi na tvoj interni timeline projekta) koji sadrži PRD za "Six Hats AI" sa strukturom od 17 sekcija.
|
||||
>
|
||||
> Takođe, u dostavljenim "memory" fragmentima i prethodnim interakcijama, fokus je bio na:
|
||||
> - Egzistencijalnim/strateškim okvirima (Egzakta, KVARK, inovacioni pipeline).
|
||||
> - Specifičnim tehničkim problemima (normalizacija adresa, integracija API-ja).
|
||||
> - Strukturnim definicijama (Innovation Pipeline, CRM za Egzaktu).
|
||||
>
|
||||
> **2. Povezani događaji (Kalendar/Email/Dokumenti):**
|
||||
> Nakon pretrage dostupnih podataka o Egzakti, KVARK-u i tvojim aktivnostima u vezi sa AI orkestracijom:
|
||||
> - **Nema direktnog pogodka** za "Six Hats AI" u formi kalendarskog događaja ili email niti u okviru ovog specifičnog konteksta.
|
||||
> - Vidim referencu na **KVARK** i razvoj AI rešenja, ali ne i specifičnu "Six Hats" arhitekturu (6 modela × 6 šešira) u formi finalizovanog PRD-a u ovom setu podataka.
|
||||
>
|
||||
> **Zaključak:**
|
||||
> Informacije o "Six Hats AI" PRD-u i pripadajućim kalendarskim/email zapisima nisu prisutne u trenutno učitanom kontekstu.
|
||||
|
||||
**Marko verdict:**
|
||||
- [ ] SPECIFIC_AND_CORRECT
|
||||
- [ ] PARTIAL
|
||||
- [ ] INCORRECT
|
||||
- [ ] HALLUCINATED
|
||||
- [ ] ABSTAIN
|
||||
- [x] **DEFERRED** — workflow-reality mismatch, cross-source test moved to Stage 1 pending ChatGPT + Gemini corpus. Explicitly not ABSTAIN per PM response §10.1.
|
||||
|
||||
**Komentar:**
|
||||
```
|
||||
Retrieval surfuje frame 295 ("Six Hats AI App Enhancement",
|
||||
5. avgust 2025) na rank 0 — single-source identifikacija savršena.
|
||||
Cross-source komponenta je strukturno netestabilna u ovom corpus-u:
|
||||
Google Takeout isporučio samo HTML MyActivity logs + Gemini Apps
|
||||
assets, bez Mail/.mbox, Calendar/.ics, Drive JSON payload-a koje
|
||||
hive-mind adapter umeju da konsumiraju. Ad-hoc HTML adapter build
|
||||
eksplicitno zabranjen brief-om.
|
||||
|
||||
LOCKED 2026-04-21 per PM response §10.1: Q3 u originalnoj formi
|
||||
pretpostavlja cross-platform workflow (Claude.ai × Mail × Calendar
|
||||
× Drive) koji ne odgovara CEO/founder profilu. Marko-v strateški
|
||||
rad živi isključivo u glavi + LLM sesijama; Mail/Calendar/Disk/
|
||||
Discord/Viber/Linear su downstream artefakti tuđeg rada koji
|
||||
prati njegove odluke, ne paralelni tragovi njegovog mišljenja.
|
||||
Pravi cross-source test za ovaj user profil je CROSS-LLM
|
||||
(Claude.ai × ChatGPT × Gemini unified graph), NE cross-platform.
|
||||
|
||||
Operativno:
|
||||
- Q3 se NE re-run-uje u Sprint 9 Task 0 (ni pod novim harvest
|
||||
timestamp fix-om). Audit trail preservation zahteva DEFERRED,
|
||||
ne ABSTAIN — distinkcija razlikuje "failed on rubric" od
|
||||
"reformulated per workflow reality check".
|
||||
- Pravi cross-LLM Q3 ekvivalent ulazi u Stage 1 scope kada
|
||||
ChatGPT export + Gemini korpus stignu.
|
||||
- Google Takeout Mail/Calendar/Drive re-request (opcija (a)
|
||||
iz PM response §3.3) — OBUSTAVLJENA per §10.1.
|
||||
- Outlook/OneDrive adapter sugestija iz prethodnog relay-a —
|
||||
takođe OBUSTAVLJENA; vraća se u backlog sa konkretnim
|
||||
Microsoft-shop enterprise referral customer trigger-om,
|
||||
ne generic-segment-expansion trigger-om.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Cost ledger
|
||||
|
||||
| Item | Amount |
|
||||
|---|---|
|
||||
| LLM inference (Q1 + Q2 + Q3, gemma4:31b local via Ollama) | $0.000000 |
|
||||
| Embedding (Xenova/all-MiniLM-L6-v2, in-process, local, cached after first load) | $0.000000 |
|
||||
| Retrieval (SQLite + sqlite-vec, local) | $0.000000 |
|
||||
| Harvest I/O (local disk, 127.6 MB Claude.ai JSON) | $0.000000 |
|
||||
| **Total run cost (out-of-pocket)** | **$0.000000** |
|
||||
| **Brief budget ceiling** | $5 target / $10 alarm |
|
||||
|
||||
Qwen3.6-35B-A3B would have cost ~$0.003 per query on DashScope pricing — well under budget had the key been provisioned.
|
||||
|
||||
## 6. Wall-clock log
|
||||
|
||||
| Phase | Duration |
|
||||
|---|---|
|
||||
| hive-mind init + data dir scaffold | ~1s |
|
||||
| Claude.ai conversations harvest (590 items) | 2s |
|
||||
| Claude.ai projects harvest (63 items) | <1s |
|
||||
| Cognify pass 1 (500 frames) | 5s |
|
||||
| Cognify pass 2 (146 frames) | 9s |
|
||||
| Stratified sample dump + smoke retrieval | ~3s |
|
||||
| Q1 retrieval + inference (gemma4:31b, 150 completion tokens) | 66s (365ms retrieval, 65.7s inference) |
|
||||
| Q2 retrieval + inference (173 completion tokens) | 71s (376ms retrieval, 70.9s inference) |
|
||||
| Q3 retrieval + inference (555 completion tokens) | 193s (386ms retrieval, 192.5s inference) |
|
||||
| **Grand total (harvest → three queries)** | **~350s (5 min 50s)** |
|
||||
|
||||
Per-query retrieval totals: **1,127 ms across all three** (avg 376 ms). Inference dominates — gemma4:31b Q4_K_M on CPU is ~3.4 tok/sec. Qwen3.6-35B-A3B via DashScope would be ~100× faster (expected <2s per query) but was not reachable this run.
|
||||
|
||||
---
|
||||
|
||||
## 7. Known issues / deviations
|
||||
|
||||
1. **Harvest drops original session timestamps.** `packages/cli/src/commands/harvest-local.ts` calls `env.frames.createIFrame(…)` which sets `memory_frames.created_at = NOW()`, discarding the `item.timestamp` surfaced by every adapter (ClaudeAdapter, GeminiAdapter, ChatGPTAdapter all supply it). Result: every harvested frame bears the batch-ingest timestamp, not the original Claude session date. **All three Stage-0 questions depend on the original session date** ("decembru 2025", "1.9–31.10.2025", "avgustu 2025"). With session-date metadata missing, the LLM has no substrate to confirm or deny the temporal claim and honest abstain is the only principled answer. This is the dominant reason all three queries resolved as abstain rather than Specific+Correct. **Fix scope:** out of Stage 0 (brief forbids adapter/harvest repair here); logged as Sprint-9 follow-up.
|
||||
|
||||
2. **LiteLLM has no provider API keys provisioned.** `DASHSCOPE_API_KEY` (for the locked-canonical `qwen3.6-35b-a3b` model), `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `OPENROUTER_API_KEY` are all empty in the `waggle-os-litellm-1` container environment. Probed four distinct routes (qwen3.6-35b-a3b / qwen-max / claude-haiku-4-5 / gpt-4o-mini / qwen3-30b-a3b) — every one 401s on its downstream provider. **Fallback:** switched Stage-0 LLM backend from LiteLLM-Qwen to local Ollama `gemma4:31b` (already installed, no out-of-pocket cost). Model deviation documented here; re-running Stage 0 against `qwen3.6-35b-a3b` when a DashScope key lands takes one flag flip (`--backend litellm --model qwen3.6-35b-a3b`).
|
||||
|
||||
3. **Google Takeout delivered has no adapter-compatible payload.** `D:/dogfood-exports/2026-04-20/google-takeout/` contains `Gemini/{gemini_gems_data.html, gemini_scheduled_actions_data.html}` (both tiny control files, no conversation content) and `My Activity/<33 subfolders>/MyActivity.html` — HTML activity logs, not JSON conversations. There is no `Mail/` (.mbox), no `Calendar/` (.ics), no `Drive/` JSON export. `GeminiAdapter` expects JSON shapes; `UniversalAdapter` fallback is explicitly discouraged by the brief ("Ne improvizuj ad-hoc adapter"). Effective corpus is Claude.ai-only. **Brief's two-source minimum: technically satisfied (Claude.ai + Takeout-was-delivered), effectively Claude.ai-only for retrieval.** Q3's cross-source expectation is partially shifted to Marko's manual Takeout ground-truth step per the questions-file `[Marko: pretraži Google Calendar / Gmail]` placeholders.
|
||||
|
||||
4. **ChatGPT export did not arrive** (per questions-file line 5). Q3 ground truth was designed in v2 to explicitly exclude ChatGPT — no deviation there.
|
||||
|
||||
5. **`ClaudeAdapter` project-only wrap quirk.** The adapter returns empty early if `conversations` is not a present array, preventing `root.projects` extraction from a standalone projects file. Workaround: wrap as `{conversations: [], projects: […]}` before passing. Added 63 artifact frames cleanly. Upstream fix is trivial (reorder the early-return) but out of Stage-0 scope.
|
||||
|
||||
6. **Retrieval query sanitization.** hive-mind's `HybridSearch.keywordSearch` treats any query containing `"` as "already quoted by caller" and passes it raw to FTS5, tripping the parser on embedded literals in natural-language questions. Q1's first run (with `"Legat"` quotes) returned 0 hits. Added a defensive strip of `"'` `:` `*` `()` and unary `-` from the query text inside `scripts/stage-0-query.mjs` before handing it to `recall-context`. Upstream sanitizer fix is straightforward but out of Stage-0 scope.
|
||||
|
||||
7. **Embedding provider picks inprocess.** No `OLLAMA_URL` / `VOYAGE_API_KEY` / `OPENAI_API_KEY` / `DASHSCOPE` in env, and the CLI's `embedderConfigFromEnv` defaults to mock when none is set. Bypassed by explicitly setting `HIVE_MIND_EMBEDDING_PROVIDER=inprocess` on every CLI invocation, which triggers the Xenova/all-MiniLM-L6-v2 auto-probe path. Probe succeeds; embeddings are real (384 native dims → 1024 normalized) and retrieval demonstrably pulls semantically-relevant frames.
|
||||
|
||||
8. **Marko's Q2 ground truth has a third-item TODO**. The questions file §"Preostali operativni task" line 246 notes Stavka 3 is `[Marko popunjava treću]` with three candidate options. Current file still has placeholder. This does not block CC's run — Marko fills it before assessing verdicts.
|
||||
|
||||
9. **Marko's Q3 Takeout ground truth has TODO placeholders**. Lines 193-206 of the questions file leave Gmail and Calendar cells for Marko's own lookup. Since the Takeout delivered has no Mail/Calendar primary exports, retrieving those via CC is not possible anyway — Marko's manual lookup path is the designed flow.
|
||||
|
||||
---
|
||||
|
||||
## 8. What CC did not do (and why)
|
||||
|
||||
- **No Qwen3.6-35B-A3B inference.** DashScope key missing; Ollama gemma4:31b local fallback chosen over blocking the run.
|
||||
- **No Stage 0 retest with fixed harvest metadata.** Brief scope explicitly forbids harvest-pipeline fixes here.
|
||||
- **No adapter build for Takeout HTML.** Same reason.
|
||||
- **No `[Marko:___]` verdict fill-in.** Marko's call per brief §Privacy guardrails + §Exit gate.
|
||||
- **No production Waggle instance access.** Dogfood KG is isolated at `D:/dogfood-exports/2026-04-20/kg-storage/personal.mind`; nothing from this run touched any `.waggle/` dir, production personal.mind, or any remote service.
|
||||
- **No per-query JSON committed.** Per-query raw files gitignored; they contain frame-preview excerpts from Marko's personal corpus.
|
||||
|
||||
---
|
||||
|
||||
## 9. Ready for Marko review
|
||||
|
||||
Marko fills the five `[Marko verdict]` checkboxes + three `[Marko: ___]` comment blocks in §4 and decides go/no-go for Stage 1.
|
||||
|
||||
**Interpretation hint** (purely mechanical, not a substitute for Marko's rubric): if the root-cause in §7.1 reads as "expected given current harvest shape, not a retrieval or inference defect," the infrastructure itself is ready — Stage 1 can proceed on questions whose ground truth does not depend on original session dates, while the harvest timestamp-preservation fix is queued for Sprint 9 before Stage 2.
|
||||
|
||||
If Marko reads the three abstains as "retrieval did not find evidence that exists, per rubric FAIL signal," the go/no-go is no-go pending the §7.1 fix.
|
||||
295
preflight-results/task-2-2-labels-14inst-2026-04-22.md
Normal file
295
preflight-results/task-2-2-labels-14inst-2026-04-22.md
Normal file
@@ -0,0 +1,295 @@
|
||||
# Task 2.2 — 14-Instance Merged Calibration Labels
|
||||
|
||||
**Datum:** 2026-04-22
|
||||
**Composition:** 9 retained from Sprint 9 original 10 (instance #9 Frank Ocean case dropped per PM Option C ratification 2026-04-22) + 5 new PM-authored triples finalized by CC post-conv-verification.
|
||||
**Source retained:** `PM-Waggle-OS/calibration/2026-04-20-failure-mode-calibration-labels.md`
|
||||
**Source new:** `preflight-results/pm-custom-triples-2026-04-22.json` + `preflight-results/conv-verification-2026-04-22.md`
|
||||
**F-mode distribution:** 3 correct · 1 F1 · 2 F2 · 4 F3 · 3 F4 · 1 F5 = 14
|
||||
|
||||
Original Methodological note on model_answer synthesis (Path A from the Sprint 9 calibration labels) applies to the 5 new instances identically.
|
||||
|
||||
---
|
||||
|
||||
## Instanca 1: `locomo_conv-26_q109` (single-hop)
|
||||
|
||||
**Question:** What did Mel and her kids make during the pottery workshop?
|
||||
**Ground truth:** pots
|
||||
**Context excerpt:** "Last Fri I finally took my kids to a pottery workshop. We all made our own pots, it was fun and therapeutic!"
|
||||
|
||||
**Synthesized model_answer:** "Mel and her kids made their own pots at the pottery workshop."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **correct**
|
||||
- `failure_mode`: **null**
|
||||
- `rationale`: "Model contains the required fact (pots) with equivalent phrasing. Extra context (Mel, kids, workshop) is factually aligned with ground truth, no fabrication."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 2: `locomo_conv-41_q123` (single-hop)
|
||||
|
||||
**Question:** What did Maria plan to do later on the evening of 7 July, 2023?
|
||||
**Ground truth:** have dinner with friends from the gym
|
||||
**Context excerpt:** "Well, I'm off to have dinner with some friends from the gym. Talk to you later!"
|
||||
|
||||
**Synthesized model_answer:** "Maria planned to go to the gym for an evening workout."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F3**
|
||||
- `rationale`: "Model misinterprets the context: 'friends from the gym' signals where Maria knows the friends from, not that she is going to the gym. The answer derives from present substrate but gets it wrong — classic F3 misreading of existing context, not fabrication."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 3: `locomo_conv-50_q141` (single-hop)
|
||||
|
||||
**Question:** Which city is featured in the photograph Dave showed Calvin?
|
||||
**Ground truth:** Boston
|
||||
**Context excerpt:** "That's Boston, Cal! Check this out, I took this picture last month, and got a great shot."
|
||||
|
||||
**Synthesized model_answer:** "The photograph features Chicago, showing the iconic skyline near Lake Michigan."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F4**
|
||||
- `rationale`: "Model names a city that does not appear in the context (Chicago, Lake Michigan) and fabricates a geographic descriptor (skyline, lake) without any supporting excerpt. This is outright invention of substrate — F4, not F3, because the answer's content has no origin in the ground truth material."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 4: `locomo_conv-42_q030` (multi-hop)
|
||||
|
||||
**Question:** What kind of writings does Joanna do?
|
||||
**Ground truth:** Screenplays, books, online blog posts, journal
|
||||
**Context excerpt:** Multiple sessions — screenplay printed (S2), started a book (S17), journal is her rock (S18), online blog post about a hard moment (S18).
|
||||
|
||||
**Synthesized model_answer:** "Joanna writes screenplays and books."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F2**
|
||||
- `rationale`: "Model correctly identifies two of four required elements (screenplays, books) with no fabrication or incorrect claims, but omits the other two (online blog posts, journal) which are explicitly evidenced in D18:1 and D18:5. Partial coverage without fabrication — F2."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 5: `locomo_conv-49_q015` (multi-hop)
|
||||
|
||||
**Question:** What kind of unhealthy snacks does Sam enjoy eating?
|
||||
**Ground truth:** soda, candy
|
||||
**Context excerpt:** "Still enjoying my soda and candy, although I know it's not the best habit to have."
|
||||
|
||||
**Synthesized model_answer:** "Sam enjoys soda and candy, acknowledging it's not a healthy habit."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **correct**
|
||||
- `failure_mode`: **null**
|
||||
- `rationale`: "Model contains both required facts (soda, candy). Additional commentary about habit acknowledgment mirrors context faithfully, no incorrect additions."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 6: `locomo_conv-41_q036` (multi-hop)
|
||||
|
||||
**Question:** What music events has John attended?
|
||||
**Ground truth:** Live music event, violin concert
|
||||
**Context excerpt:** Violin concert in town found by John (S8), live music event with family (S20).
|
||||
|
||||
**Synthesized model_answer:** "John enjoys finding family-friendly activities like walks and picnics in the park, and likes exploring events in his town."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F5**
|
||||
- `rationale`: "Model answer is coherent and derives from context (walks, picnics, town events appear in D8:11), but does not address the specific question — which music events did John attend. Response pivots to a related but different topic (John's family-activity preferences). No hallucination, no incorrect facts about John's activities, but off-topic relative to prompt — F5."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 7: `locomo_conv-42_q038` (temporal)
|
||||
|
||||
**Question:** When did Joanna make a dessert with almond milk?
|
||||
**Ground truth:** The Friday before 14 September, 2022
|
||||
**Context excerpt:** Session 21 (14 September, 2022): "Last Friday, I made a deeeelish dessert with almond milk."
|
||||
|
||||
**Synthesized model_answer:** "Joanna made the dessert on 7 September, 2022."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F3**
|
||||
- `rationale`: "Model derives date from context (session is 14 September, 'last Friday' reference) but computes it incorrectly. 7 September 2022 was a Wednesday — Friday before 14 September was actually 9 September. Answer draws on present substrate but arrives at a demonstrably wrong specific date — F3, not F4, because model did not fabricate the relationship, only miscomputed it."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 8: `locomo_conv-41_q053` (temporal)
|
||||
|
||||
**Question:** When did John help renovate his hometown community center?
|
||||
**Ground truth:** 2022
|
||||
**Context excerpt:** Session 28 (5 August, 2023): "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."
|
||||
|
||||
**Synthesized model_answer:** "John renovated the community center in 2020, during the early pandemic period when local infrastructure needed volunteer support."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F4**
|
||||
- `rationale`: "Model produces a year (2020) that cannot be derived from the context — 'last year' relative to 5 August 2023 unambiguously yields 2022. Model also fabricates causal context ('early pandemic period', 'volunteer support for infrastructure') that does not appear in any excerpt. Content generation beyond what the substrate allows — F4, not F3, because the fabricated context is the bulk of the answer."
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Instanca 9: `locomo_conv-47_q017` (open-ended)
|
||||
|
||||
**Question:** What is the board game where you have to find the imposter that John mentions to James?
|
||||
**Ground truth:** Mafia
|
||||
**Context excerpt:** "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."
|
||||
|
||||
**Synthesized model_answer:** "I do not have enough information in the provided context to determine the name of the game John mentioned."
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F1**
|
||||
- `rationale`: "Model explicitly refuses to answer, stating it lacks sufficient information. No partial answer, no fabrication, no off-topic pivot. Ground truth (Mafia) is not literally stated in the D8:36 excerpt — model plays it safe and abstains rather than inferring from the 'find the impostor' description. Classic F1 abstain behavior."
|
||||
|
||||
---
|
||||
|
||||
## Distribucija sinteze
|
||||
|
||||
| Kategorija | Broj | Instance |
|
||||
|------------|------|----------|
|
||||
| correct | 3 | #1, #5, #9 |
|
||||
| F1 ABSTAIN | 1 | #10 |
|
||||
| F2 PARTIAL | 1 | #4 |
|
||||
| F3 INCORRECT | 2 | #2, #7 |
|
||||
| F4 HALLUCINATED | 2 | #3, #8 |
|
||||
| F5 OFF-TOPIC | 1 | #6 |
|
||||
| **Total** | **10** | |
|
||||
|
||||
Distribucija pokriva ceo spektar taksonomije sa bar po jednim reprezentativnim primerom svake failure klase + 3 pozitivna kontrolna slučaja. LoCoMo kategorije (single-hop / multi-hop / temporal / open-ended) su raspoređene tako da nijedna kategorija ne dominira svojim failure tipom — single-hop dobija correct/F3/F4, multi-hop dobija correct/F2/F5, temporal dobija F3/F4, open-ended dobija correct/F1.
|
||||
|
||||
---
|
||||
|
||||
## Referentne odluke i napomene
|
||||
|
||||
- Decision tree redosled po §3 taksonomije (F1 → F5 → F4 → F2 → F3) primenjen pri svakom incorrect labeliranju; rationale navodi zašto alternativna kategorija nije odabrana kada je edge-case prisutan (instance #7 F3 vs F4, #8 F4 vs F3).
|
||||
- Sve synthesized model answers su plausibilne — pisane u stilu kakvim bi realan LLM mogao da proizvede (ne straw-man). Cilj: test judge-a na realističnim failure patterns, ne na očiglednim podmetanjima.
|
||||
- Instance #3 i #8 su dva različita F4 test-case-a po nameri: #3 fabrikuje single entitet (grad), #8 fabrikuje narative context + godinu. Ovo pokriva dva pod-tipa hallucination-a (point-fact vs contextual-frame).
|
||||
- Instance #2 i #7 su dva F3 test-case-a sa različitim izvorom greške: #2 semantička greška (razumevanje "friends from the gym"), #7 računska greška (date math). Judge treba da ih oba prepozna kao F3, ne da #7 pogrešno klasifikuje kao F2 (delimično tačno).
|
||||
- Instance #4 F2 ima 2/4 tačnih stavki = 50% coverage. Odluka je kategorisano kao F2 a ne hybrid F2+F3, per §11 OQ-FM-2 koji ostaje F3 samo kada postoji eksplicitna pogrešna činjenica, što #4 nema (samo omission).
|
||||
- Instance #6 F5 je namerno postavljena da bude **koherentna** — model answer je faktualno tačan ali ne adresira pitanje. Ovo je najteži failure mode za judge jer zahteva razumevanje prompt intent-a, ne samo verifikaciju činjenica.
|
||||
|
||||
---
|
||||
|
||||
## Sledeći korak — CC second-pass
|
||||
|
||||
CC u Sprint 9 preuzima ovaj dokument, validira každu instance sa svojom procenom, i:
|
||||
1. Potvrđuje (✓) ili osporava (✗) svaki label + rationale.
|
||||
2. Kada postoji razlika: CC i PM razrešavaju u handoff sesiji pre nego što labele idu u `failure-mode-calibration-10.jsonl` `human_label` polja.
|
||||
3. Posle razrešenja, CC puni JSONL sa konačnim labelima.
|
||||
4. Judge-kalibracioni run (Sonnet preko svih 10, plus Haiku/GPT-5/Gemini u 4-judge ensemble prep) meri match rate. Cilj: ≥ 8/10.
|
||||
|
||||
---
|
||||
|
||||
## Referentni dokumenti
|
||||
|
||||
- Failure mode taksonomija: `PM-Waggle-OS/strategy/2026-04-20-failure-mode-taxonomy.md`
|
||||
- Failure mode OQ resolutions: `PM-Waggle-OS/decisions/2026-04-20-failure-mode-oq-resolutions-locked.md`
|
||||
- Source JSONL: `waggle-os/benchmarks/data/failure-mode-calibration-10.jsonl` (seed=43)
|
||||
- Preflight gate spec: `PM-Waggle-OS/strategy/2026-04-20-preflight-gate-spec.md`
|
||||
- Sprint 8 exit ping: `PM-Waggle-OS/sessions/2026-04-20-sprint-8-exit.md`
|
||||
|
||||
## Instanca 10: `locomo_conv-44_pm_2026-04-22_001` (temporal-scope)
|
||||
|
||||
**Question:** When did Audrey adopt Pixie?
|
||||
**Ground truth:** around April 2, 2023
|
||||
**Context excerpt:** Session 2 (2:42 pm on 2 April, 2023) Audrey: "Hey Andrew, I got a surprise for you! We adopted another puppy called Pixie. She's SO cute! Isn't she just the cutest?"
|
||||
|
||||
**Synthesized model_answer:** Audrey adopted Pixie in early April 2023.
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F3**
|
||||
- `rationale`: "Answer is vague-but-derived: "early April" overlaps with true "around April 2, 2023" but loses the specific date precision the ground truth provides. Derivable from substrate but imprecise — F3 misread of specific date, not fabrication."
|
||||
|
||||
---
|
||||
|
||||
## Instanca 11: `locomo_conv-44_pm_2026-04-22_002` (temporal-scope)
|
||||
|
||||
**Question:** How many years passed between Audrey adopting Pixie and her other three dogs?
|
||||
**Ground truth:** three years
|
||||
**Context excerpt:** Session 2 (2:42 pm on 2 April, 2023) Audrey: "Hey Andrew, I got a surprise for you! We adopted another puppy called Pixie. She's SO cute! Isn't she just the cutest?"
|
||||
Session 1 (1:10 pm on 27 March, 2023) Audrey: "I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!"
|
||||
|
||||
**Synthesized model_answer:** Two years passed between Audrey adopting Pixie and her other three dogs.
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F3**
|
||||
- `rationale`: "Answer gives a specific but incorrect interval (two years vs ground-truth three years). Miscounted arithmetic on derivable anchors — F3 class misread, not F4 fabrication."
|
||||
|
||||
---
|
||||
|
||||
## Instanca 12: `locomo_conv-43_pm_2026-04-22_003` (null-result)
|
||||
|
||||
**Question:** What musical instrument does John play?
|
||||
**Ground truth:** null
|
||||
**Context excerpt:** (Excerpt — locomo_conv-43 opening; dialogue contains no evidence of the queried attribute across 680 turns.)
|
||||
Session 1 (7:48 pm on 21 May, 2023) John: "Hey Tim, nice to meet you! What's up? Anything new happening?"
|
||||
Session 1 (7:48 pm on 21 May, 2023) Tim: "Hey John! Great to meet you. Been discussing collaborations for a Harry Potter fan project I am working on - super excited! Anything interesting happening for you?"
|
||||
Session 1 (7:48 pm on 21 May, 2023) John: "That's great! I just signed with a new team - excited for the season!"
|
||||
Session 1 (7:48 pm on 21 May, 2023) Tim: "Woohoo! Congrats on the new team. Which team did you sign with?"
|
||||
|
||||
**Synthesized model_answer:** John plays the guitar, which he mentions practicing during weekend jam sessions with his high school team.
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F4**
|
||||
- `rationale`: "Model names a specific instrument (guitar) with fabricated supporting detail (weekend jam sessions, high school team). Dialogue contains zero evidence for John playing any instrument. This is classic F4 — invented substrate, plausible-sounding but entirely unsupported."
|
||||
|
||||
---
|
||||
|
||||
## Instanca 13: `locomo_conv-48_pm_2026-04-22_004` (null-result)
|
||||
|
||||
**Question:** Which university did Deborah attend?
|
||||
**Ground truth:** null
|
||||
**Context excerpt:** (Excerpt — locomo_conv-48 opening; dialogue contains no evidence of the queried attribute across 681 turns.)
|
||||
Session 1 (4:06 pm on 23 January, 2023) Deborah: "Hey Jolene, nice to meet you! How's your week going? Anything fun happened?"
|
||||
Session 1 (4:06 pm on 23 January, 2023) Jolene: "Hi Deb! Good to meet you! Yeah, my week's been busy. I finished an electrical engineering project last week - took a lot of work, but it's done now. Anything fun happening for you?"
|
||||
Session 1 (4:06 pm on 23 January, 2023) Deborah: "Congrats! Last week I visited a place that holds a lot of memories for me. It was my mother`s old house."
|
||||
Session 1 (4:06 pm on 23 January, 2023) Jolene: "Why does it hold such special memories for you?"
|
||||
|
||||
**Synthesized model_answer:** Deborah attended Stanford University for her undergraduate degree in computer science.
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F4**
|
||||
- `rationale`: "Model names a specific university (Stanford) and a specific degree (computer science) for Deborah, neither of which appear in the dialogue. This is F4 — full fabrication from a null-evidence base. Stanford is a plausible-default "prestigious US university" choice that LLMs commonly hallucinate in absence of context."
|
||||
|
||||
---
|
||||
|
||||
## Instanca 14: `locomo_conv-30_pm_2026-04-22_005` (chain-of-anchor)
|
||||
|
||||
**Question:** What hobbies and activities does Jon pursue across the dialogue history?
|
||||
**Ground truth:** Jon pursues five distinct activities: (1) contemporary dance (lifelong passion, favored style contemporary), (2) running his own dance studio as a business, (3) competing in dance competitions (dance crew won first place locally; prepares for further comps), (4) gym / fitness (began hitting the gym to balance venture stress), (5) reading business-improvement books (e.g. "The Lean Startup").
|
||||
**Context excerpt:** Session 1 (4:04 pm on 20 January, 2023) Jon: "I've been into dancing since I was a kid and it's been my passion and escape. I wanna start a dance studio so I can teach others the joy that dancing brings me."
|
||||
Session 1 (4:04 pm on 20 January, 2023) Jon: "Cool, Gina! I love all dances, but contemporary is my top pick. It's so expressive and powerful! What's your fave?"
|
||||
Session 1 (4:04 pm on 20 January, 2023) Jon: "Thanks! I rehearsed with a small group of dancers after work. We do all kinds of dances, from contemporary to hip-hop. We've got some cool projects in the works. Finishing up choreography to perform at a nearby festival next month. Can't wait!"
|
||||
Session 1 (4:04 pm on 20 January, 2023) Jon: "Sorry to hear that! I'm starting a dance studio 'cause I'm passionate about dancing and it'd be great to share it with others."
|
||||
Session 1 (4:04 pm on 20 January, 2023) Jon: "Wow, that must've been great! Check my ideal dance studio by the water."
|
||||
Session 2 (2:32 pm on 29 January, 2023) Jon: "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."
|
||||
Session 2 (2:32 pm on 29 January, 2023) Jon: "Yeah, good flooring's crucial. I'm after Marley flooring, which is what dance studios usually use. It's great 'cause it's grippy but still lets you move, plus it's tough and easy to keep clean."
|
||||
Session 1 (4:04 pm on 20 January, 2023) Jon: "Woah, that pic's from when my dance crew took home first in a local comp last year. It was amazing up on that stage! I'm super keen to spread that intensity with other peeps. Gina, you ever been in any dance comps or shows?"
|
||||
Session 4 (10:43 am on 4 February, 2023) Jon: "I'm getting ready for a dance comp near me next month. It's a great chance for me to show my skillz and, hopefully, get some props from the dance fam. Super stoked!"
|
||||
Session 8 (1:26 pm on 3 April, 2023) Jon: "Thanks, Gina! I'm expanding my dance studio's social media presence and offering workshops and classes to local schools and centers. I'm also hosting a dance competition next month to showcase local talent and bring more attention to my studio. All the work's paying off - I'm seeing progress and the dancers are so excited. It's such a great feeling to give a place where people can express themselves through dance!"
|
||||
Session 6 (2:35 pm on 16 March, 2023) Jon: "Hi Gina! Been hectic for me lately. Started hitting the gym last week to stay on track with the venture. Gotta figure out how to balance it all, but it's going well. How about you?"
|
||||
Session 12 (7:18 pm on 27 May, 2023) Jon: "I'm currently reading "The Lean Startup" and hoping it'll give me tips for my biz."
|
||||
Session 12 (7:18 pm on 27 May, 2023) Jon: "Yeah, the book got me thinking about building a focused and efficient business. Adapting and tweaking from customer feedback is important too, so I'm gonna try it out!"
|
||||
|
||||
**Synthesized model_answer:** Jon pursues contemporary dance and running his own dance studio. He is passionate about dance since childhood and is working on opening a studio.
|
||||
|
||||
**human_label:**
|
||||
- `verdict`: **incorrect**
|
||||
- `failure_mode`: **F2**
|
||||
- `rationale`: "Model lists 2 of 5 ground-truth activities correctly (contemporary dance + dance studio business) with no fabrication — but omits dance competitions, gym/fitness, and reading. This is textbook F2: partial coverage / omission without hallucination. The two items mentioned are accurately supported; the failure mode is the three missing items."
|
||||
|
||||
---
|
||||
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"generatedAt": "2026-04-21T08:30:48.294Z",
|
||||
"litellmUrl": "http://localhost:4000",
|
||||
"allAvailable": true,
|
||||
"vendors": [
|
||||
{
|
||||
"id": "anthropic",
|
||||
"model": "claude-opus-4-7",
|
||||
"label": "Anthropic Opus 4.7",
|
||||
"status": "ok",
|
||||
"http": 200,
|
||||
"latencyMs": 2066,
|
||||
"error": null,
|
||||
"completionText": "OK",
|
||||
"promptTokens": 21,
|
||||
"completionTokens": 6
|
||||
},
|
||||
{
|
||||
"id": "openai",
|
||||
"model": "gpt-5.4",
|
||||
"label": "OpenAI GPT-5.4",
|
||||
"status": "ok",
|
||||
"http": 200,
|
||||
"latencyMs": 1649,
|
||||
"error": null,
|
||||
"completionText": "OK",
|
||||
"promptTokens": 13,
|
||||
"completionTokens": 4
|
||||
},
|
||||
{
|
||||
"id": "google",
|
||||
"model": "gemini-3.1-pro",
|
||||
"label": "Google Gemini 3.1 Pro",
|
||||
"status": "ok",
|
||||
"http": 200,
|
||||
"latencyMs": 1977,
|
||||
"error": null,
|
||||
"completionText": "OK",
|
||||
"promptTokens": 8,
|
||||
"completionTokens": 12
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user