b13b5cd7a1
The rank-based "lift" metric (warm-top-1 == cold-judge-best) doesn't distinguish "Shape B surfaced a strictly-better answer" from "Shape B shuffled ranks but quality is unchanged" from "Shape B replaced a good answer with a wrong one." This commit adds Pass 4: judge warm top-1 with the same prompt as cold ratings, then bucket the comparison. Implementation: - New --with-rejudge driver flag (default off). - New WITH_REJUDGE harness env (default 1, on for prod runs). - queryRun gains WarmTop1Metadata (cached during Pass 2 for the rejudge call) + WarmTop1Rating *int (nil-distinguishable; nil = no rejudge, 0..5 = rating). - summary gains RejudgeAttempted, QualityLifted, QualityNeutral, QualityRegressed (counts of warm-rating > / == / < cold-rating). - Markdown headline gains a Quality block when rejudge ran. - ~21 extra judge calls (~30s on qwen2.5). Run #005 result (split inject threshold 0.20 + paraphrase + rejudge): Quality lifted 5 / 21 (24%) — 3× +2 rating, 2× +1 rating Quality neutral 13 / 21 (62%) — includes OOD queries holding 1 Quality regressed 3 / 21 (14%) Net rating delta +3 across 21 queries (+0.14 average) The 5 lifts were all rating-2 cold replaced with rating-3 or rating-4 warm — Shape B took mediocre matches and substituted substantively better ones. The 3 regressions were small (-1, -1, -3). Q11 is the cautionary tale: cold top-1 "production line worker" (rating 4) got replaced by Q1's recorded "forklift OSHA-30 operator" e-5729 (rating 1). Adjacent-domain cross-pollination — production worker and forklift operator embed within 0.20 cosine because both are warehouse-adjacent staffing queries, even though the judge correctly distinguishes them. The split-threshold defense (0.5 boost / 0.20 inject) catches OOD cross-pollination (Q19/Q20/Q21 all stayed neutral at rating 1) but not adjacent-domain cross-pollination. Net product verdict: working, net-positive on quality, but the worst case (Q11 4→1) is customer-visible and warrants a tighter inject threshold OR an additional gate beyond cosine distance. Filed in STATE_OF_PLAY OPEN as a follow-up. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
121 lines
7.4 KiB
Markdown
121 lines
7.4 KiB
Markdown
# Playbook-Lift Reality Test — Run 005
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**Generated:** 2026-04-30T12:40:48.475901847Z
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**Judge:** `qwen2.5:latest` (Ollama, resolved from env JUDGE_MODEL=qwen2.5:latest)
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**Corpora:** `workers,ethereal_workers`
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**Workers limit:** 5000
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**Queries:** `tests/reality/playbook_lift_queries.txt` (21 executed)
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**K per pass:** 10
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**Paraphrase pass:** ENABLED
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**Re-judge pass:** ENABLED
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**Evidence:** `reports/reality-tests/playbook_lift_005.json`
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---
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## Headline
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| Metric | Value |
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|---|---:|
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| Total queries run | 21 |
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| Cold-pass discoveries (judge-best ≠ top-1) | 7 |
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| Warm-pass lifts (recorded playbook → top-1) | 5 |
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| No change (judge-best already top-1, no playbook needed) | 16 |
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| Playbook boosts triggered (warm pass) | 9 |
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| Mean Δ top-1 distance (warm − cold) | -0.076170966 |
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| **Paraphrase pass — recorded answer at rank 0 (top-1)** | **5 / 7** |
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| Paraphrase pass — recorded answer at any rank in top-K | 5 / 7 |
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| **Quality lift** (warm top-1 rating > cold top-1 rating) | **5 / 21** |
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| Quality neutral (warm top-1 rating = cold top-1 rating) | 13 / 21 |
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| Quality regressed (warm top-1 rating < cold top-1 rating) | 3 / 21 |
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**Verbatim lift rate:** 5 of 7 discoveries became top-1 after warm pass.
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---
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## Per-query results
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| # | Query | Cold top-1 | Cold judge-best (rank/rating) | Recorded? | Warm top-1 | Judge-best warm rank | Lift |
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|---|---|---|---|---|---|---|---|
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| 1 | Forklift operator with OSHA-30, warehouse experience, day sh | e-5670 | 2/4 | ✓ e-5729 | e-5729 | 0 | **YES** |
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| 2 | OSHA-30 certified forklift operator in Wisconsin, cold stora | e-6293 | 7/3 | — | w-1566 | 8 | no |
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| 3 | Production worker with confined-space cert and hazmat traini | w-602 | 0/2 | — | w-3575 | 1 | no |
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| 4 | CDL Class A driver, clean record, willing to do regional 4-d | w-3854 | 0/1 | — | w-3854 | 0 | no |
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| 5 | Warehouse lead with current OSHA-30 certification, NOT OSHA- | w-1807 | 6/3 | — | w-1807 | 6 | no |
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| 6 | Forklift-certified loader, certification must be active, dis | w-1807 | 3/4 | ✓ w-205 | w-4257 | 1 | no |
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| 7 | Hazmat-certified warehouse worker comfortable with cold stor | e-4910 | 2/4 | ✓ w-4257 | w-205 | 1 | no |
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| 8 | Bilingual production worker with team-lead experience and tr | w-4988 | 0/4 | — | w-4988 | 0 | no |
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| 9 | Inventory specialist with confined-space cert and compliance | w-388 | 3/4 | ✓ w-3575 | w-3575 | 0 | **YES** |
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| 10 | Warehouse worker who can run inventory cycles and lead a sma | e-3011 | 0/4 | — | e-3011 | 0 | no |
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| 11 | Production line worker comfortable filling in as line superv | w-1387 | 0/4 | — | e-5729 | 1 | no |
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| 12 | Customer service rep willing to cross-train into dispatch or | w-1451 | 0/2 | — | w-1451 | 0 | no |
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| 13 | Reliable production line lead with strong attendance and lea | e-7360 | 5/4 | ✓ w-2886 | w-2886 | 0 | **YES** |
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| 14 | Highly responsive forklift operator available for last-minut | e-6108 | 5/4 | ✓ w-1566 | w-1566 | 0 | **YES** |
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| 15 | Engaged warehouse associate with strong safety compliance re | e-2743 | 2/4 | ✓ w-49 | w-49 | 0 | **YES** |
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| 16 | CDL-A driver based in IL or WI, willing to run regional 4-da | w-2486 | 5/2 | — | w-2486 | 5 | no |
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| 17 | Bilingual customer service rep in Indianapolis or Cincinnati | e-9749 | 9/2 | — | e-9749 | 9 | no |
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| 18 | Production supervisor open to Midwest relocation for permane | w-379 | 6/3 | — | w-379 | 6 | no |
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| 19 | Dental hygienist with three years experience, Indianapolis a | e-6772 | 0/1 | — | w-3575 | 1 | no |
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| 20 | Registered nurse with ICU experience, willing to take per-di | w-379 | 0/1 | — | w-379 | 0 | no |
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| 21 | Software engineer with React and TypeScript, three years exp | w-1773 | 0/1 | — | w-1773 | 0 | no |
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---
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## Paraphrase pass — does the playbook help similar-but-different queries?
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For each query whose Pass 1 cold pass recorded a playbook entry, the
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judge model rephrased the query, and the rephrased version was sent
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through warm matrix.search. The recorded answer ID's rank in those
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results tests whether cosine on the embedded paraphrase finds the
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recorded query's vector.
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| # | Original (≤40c) | Paraphrase (≤60c) | Recorded answer | Paraphrase top-1 | Recorded rank | Paraphrase lift |
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|---|---|---|---|---|---|---|
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| 1 | Forklift operator with OSHA-30, warehous | Seeking forklift operator certified in OSHA-30, looking for | e-5729 | e-5729 | 0 | **YES** |
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| 6 | Forklift-certified loader, certification | Loader requiring active forklift certification, this must no | w-205 | w-205 | 0 | **YES** |
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| 7 | Hazmat-certified warehouse worker comfor | Warehouse worker with Hazmat certification and experience in | w-4257 | w-4257 | 0 | **YES** |
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| 9 | Inventory specialist with confined-space | Specialist in inventory management requiring certified confi | w-3575 | w-49 | -1 | no |
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| 13 | Reliable production line lead with stron | Experienced production line supervisor with excellent punctu | w-2886 | w-2886 | 0 | **YES** |
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| 14 | Highly responsive forklift operator avai | Available forklift operator ready for urgent shift coverage | w-1566 | w-1566 | 0 | **YES** |
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| 15 | Engaged warehouse associate with strong | Warehouse associate dedicated to engagement and boasting a r | w-49 | w-984 | -1 | no |
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---
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## Honesty caveats
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1. **Judge IS the ground truth proxy.** Without human-labeled relevance, the LLM
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judge's verdict is what defines "best." If `qwen2.5:latest` rates badly,
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the lift number is meaningless. To validate the judge itself, sample 5–10
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verdicts manually and check agreement.
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2. **Score-1.0 boost = distance halved.** Playbook math is
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`distance' = distance × (1 - 0.5 × score)`. Lift requires the judge-best
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result's pre-boost distance to be ≤ 2× the cold top-1's distance, otherwise
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even halving doesn't promote it. Tight clusters → little visible lift.
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3. **Verbatim vs paraphrase.** The verbatim lift rate (above) is the cheap
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case — same query, recorded playbook, expected boost. The paraphrase
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pass (when enabled) is the actual learning property: similar-but-different
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queries hitting a recorded playbook. Compare verbatim and paraphrase
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lift rates — paraphrase should be lower (semantic-distance gates some
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playbook hits) but non-zero is the meaningful signal.
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4. **Multi-corpus skew.** Default corpora=`workers,ethereal_workers` — if all judge-best
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results land in one corpus, the matrix layer's purpose isn't being tested.
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Check per-corpus distribution in the JSON.
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5. **Judge resolution.** This run used `qwen2.5:latest` from
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env JUDGE_MODEL=qwen2.5:latest.
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Bumping the judge for run #N+1 means editing one line in lakehouse.toml.
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6. **Paraphrase generation also uses the judge.** The same model that rates
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relevance also rephrases queries. A judge that's bad at rating staffing
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queries is probably also bad at rephrasing them. Worth sanity-checking
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a sample of `paraphrase_query` values in the JSON before trusting the
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paraphrase lift number.
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## Next moves
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- If lift rate ≥ 50% of discoveries: matrix layer + playbook is doing real
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work. Move to paraphrase queries + tag-based boost (currently ignored).
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- If lift rate < 20%: investigate why — judge variance, distance gap too
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wide, or playbook math too gentle. The score=1.0 / 0.5× formula may need
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retuning.
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- If discovery rate (cold judge-best ≠ top-1) is itself low: cosine is
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already close to optimal on this query distribution. Either the corpus
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is too narrow or the queries are too easy.
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