golangLAKEHOUSE/reports/reality-tests/playbook_lift_real_003.md
root 3263254f1c reality_test real_003: 40-query paraphrase stress + extractor extension
Stress-tests the role gate with 40 queries (10 fill_events rows × 4
styles): need, client_first, looking, shorthand. Each row's role +
client + city stays the same; only the surface phrasing changes.

real_003 (original extractor) confirmed the shorthand-vs-shorthand
failure mode: CNC Operator shorthand recording leaked w-2404 onto
Forklift Operator shorthand query within the same Beacon Freight
Detroit cluster. Both record + query had empty role (extractor
returns "" for shorthand because there's no separator between role
and city), gate disabled, distance check passed, bleed fired.

Fix: extended extractRoleFromNeed to handle client_first
("{client} needs N {role} in...") and looking ("Looking for N
{role} at...") patterns. Shorthand left intentionally unmatched —
"Forklift Operator Detroit" is shape-indistinguishable from
"Forklift" + "Operator Detroit" without an LLM extractor or known-
cities lookup.

real_003b (extended extractor) verifies bleed closed across all 4
styles for this dataset. Forklift Operator queries keep w-2136 (the
cold-pass-correct match) regardless of which style the query came
in. Same-role boosts now fire correctly across styles — a CNC
Operator recording made in `looking` style boosts the CNC need-form
query.

scripts/cutover/gen_real_queries.go: added -styles flag with values
need|client_first|looking|shorthand|all (default need preserves
real_001/002 behavior). Tests/reality/real_coord_queries_v2.txt is
the 40-query stress file.

scripts/playbook_lift/main_test.go: 10 sub-tests lock the four
documented patterns + shorthand limitation + lift-suite-style
queries (no clean role, returns empty as expected).

Aggregate metrics:
- real_003  (original): disc=7,  lift=7,  boost=14, meanΔ=-0.108
- real_003b (extended): disc=11, lift=10, boost=31, meanΔ=-0.202
The growth reflects more LEGITIMATE same-role same-cluster transfer
firing across styles, not bleed (verified by per-cluster bleed
table — Forklift Operator queries unchanged across all 4 styles).

Known limitation documented in real_003_findings.md: same-cluster,
same-role queries in shorthand still embed close enough that a
shorthand recording could bleed onto a different-role shorthand
query if both record + query strip role. Closing this requires
LLM extraction or known-cities lookup at record + query time.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 21:42:02 -05:00

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Playbook-Lift Reality Test — Run real_003

Generated: 2026-05-01T02:27:31.394679694Z Judge: qwen2.5:latest (Ollama, resolved from config [models].local_judge) Corpora: workers,ethereal_workers Workers limit: 5000 Queries: tests/reality/real_coord_queries_v2.txt (40 executed) K per pass: 10 Paraphrase pass: disabled Re-judge pass: disabled Evidence: reports/reality-tests/playbook_lift_real_003.json


Headline

Metric Value
Total queries run 40
Cold-pass discoveries (judge-best ≠ top-1) 7
Warm-pass lifts (recorded playbook → top-1) 7
No change (judge-best already top-1, no playbook needed) 33
Playbook boosts triggered (warm pass) 14
Mean Δ top-1 distance (warm cold) -0.10771029

Verbatim lift rate: 7 of 7 discoveries became top-1 after warm pass.


Per-query results

# Query Cold top-1 Cold judge-best (rank/rating) Recorded? Warm top-1 Judge-best warm rank Lift
1 Need 5 Warehouse Associates in Kansas City MO starting at 09 e-9573 0/4 e-9573 0 no
2 Parallel Machining needs 5 Warehouse Associates in Kansas Ci e-7538 0/4 e-7538 0 no
3 Looking for 5 Warehouse Associates at Parallel Machining in e-9573 0/4 e-9573 0 no
4 5 Warehouse Associates Kansas City MO 09:00 Parallel Machini e-9573 0/4 e-9573 0 no
5 Need 1 Forklift Operator in Detroit MI starting at 15:00 for w-2136 0/5 w-2136 0 no
6 Beacon Freight needs 1 Forklift Operator in Detroit MI at 15 w-2136 0/5 w-2136 0 no
7 Looking for 1 Forklift Operator at Beacon Freight in Detroit w-2136 0/5 w-2136 0 no
8 1 Forklift Operator Detroit MI 15:00 Beacon Freight w-4766 0/5 w-2404 1 no
9 Need 4 Loaders in Indianapolis IN starting at 12:00 for Midw e-2820 4/4 ✓ e-4769 e-4769 0 YES
10 Midway Distribution needs 4 Loaders in Indianapolis IN at 12 e-6419 4/4 ✓ e-4769 e-4769 0 YES
11 Looking for 4 Loaders at Midway Distribution in Indianapolis e-2820 1/2 e-4769 2 no
12 4 Loaders Indianapolis IN 12:00 Midway Distribution e-2820 6/5 ✓ e-4769 e-4769 0 YES
13 Need 3 Warehouse Associates in Fort Wayne IN starting at 17: e-9237 0/4 w-565 1 no
14 Cornerstone Fabrication needs 3 Warehouse Associates in Fort e-9237 4/4 ✓ w-565 w-565 0 YES
15 Looking for 3 Warehouse Associates at Cornerstone Fabricatio e-9237 3/4 ✓ w-565 w-565 0 YES
16 3 Warehouse Associates Fort Wayne IN 17:30 Cornerstone Fabri e-9237 0/4 w-565 1 no
17 Need 4 Pickers in Detroit MI starting at 13:30 for Beacon Fr w-2136 0/2 w-2136 0 no
18 Beacon Freight needs 4 Pickers in Detroit MI at 13:30 w-2136 0/2 w-2136 0 no
19 Looking for 4 Pickers at Beacon Freight in Detroit MI for 13 e-7948 0/1 e-7948 0 no
20 4 Pickers Detroit MI 13:30 Beacon Freight e-7948 3/2 e-7948 3 no
21 Need 2 Packers in Joliet IL starting at 09:30 for Parallel M e-9191 0/2 e-9191 0 no
22 Parallel Machining needs 2 Packers in Joliet IL at 09:30 e-9191 7/3 e-9191 7 no
23 Looking for 2 Packers at Parallel Machining in Joliet IL for e-9191 0/2 e-9191 0 no
24 2 Packers Joliet IL 09:30 Parallel Machining e-9191 6/3 e-9191 6 no
25 Need 3 Assemblers in Flint MI starting at 08:30 for Heritage w-2582 4/3 w-2582 4 no
26 Heritage Foods needs 3 Assemblers in Flint MI at 08:30 w-2582 0/2 w-2582 0 no
27 Looking for 3 Assemblers at Heritage Foods in Flint MI for 0 w-4817 0/2 w-4817 0 no
28 3 Assemblers Flint MI 08:30 Heritage Foods w-4124 2/2 w-4124 2 no
29 Need 3 Packers in Flint MI starting at 12:30 for Parallel Ma e-6019 0/1 e-6019 0 no
30 Parallel Machining needs 3 Packers in Flint MI at 12:30 e-6019 4/2 e-6019 4 no
31 Looking for 3 Packers at Parallel Machining in Flint MI for e-6019 0/1 e-6019 0 no
32 3 Packers Flint MI 12:30 Parallel Machining e-6019 0/2 e-6019 0 no
33 Need 1 Shipping Clerk in Flint MI starting at 17:00 for Pion w-3988 1/3 w-122 2 no
34 Pioneer Assembly needs 1 Shipping Clerk in Flint MI at 17:00 w-3988 1/3 w-122 2 no
35 Looking for 1 Shipping Clerk at Pioneer Assembly in Flint MI w-3988 2/3 w-122 0 no
36 1 Shipping Clerk Flint MI 17:00 Pioneer Assembly w-2564 2/4 ✓ w-122 w-122 0 YES
37 Need 1 CNC Operator in Detroit MI starting at 17:30 for Beac w-2136 6/3 w-2404 7 no
38 Beacon Freight needs 1 CNC Operator in Detroit MI at 17:30 w-2404 0/5 w-2404 0 no
39 Looking for 1 CNC Operator at Beacon Freight in Detroit MI f e-9958 1/2 w-2404 2 no
40 1 CNC Operator Detroit MI 17:30 Beacon Freight e-5546 2/5 ✓ w-2404 w-2404 0 YES

Honesty caveats

  1. Judge IS the ground truth proxy. Without human-labeled relevance, the LLM judge's verdict is what defines "best." If `` rates badly, the lift number is meaningless. To validate the judge itself, sample 510 verdicts manually and check agreement.
  2. Score-1.0 boost = distance halved. Playbook math is distance' = distance × (1 - 0.5 × score). Lift requires the judge-best result's pre-boost distance to be ≤ 2× the cold top-1's distance, otherwise even halving doesn't promote it. Tight clusters → little visible lift.
  3. Verbatim vs paraphrase. The verbatim lift rate (above) is the cheap case — same query, recorded playbook, expected boost. The paraphrase pass (when enabled) is the actual learning property: similar-but-different queries hitting a recorded playbook. Compare verbatim and paraphrase lift rates — paraphrase should be lower (semantic-distance gates some playbook hits) but non-zero is the meaningful signal.
  4. Multi-corpus skew. Default corpora=workers,ethereal_workers — if all judge-best results land in one corpus, the matrix layer's purpose isn't being tested. Check per-corpus distribution in the JSON.
  5. Judge resolution. This run used qwen2.5:latest from config [models].local_judge. Bumping the judge for run #N+1 means editing one line in lakehouse.toml.
  6. Paraphrase generation also uses the judge. The same model that rates relevance also rephrases queries. A judge that's bad at rating staffing queries is probably also bad at rephrasing them. Worth sanity-checking a sample of paraphrase_query values in the JSON before trusting the paraphrase lift number.

Next moves

  • If lift rate ≥ 50% of discoveries: matrix layer + playbook is doing real work. Move to paraphrase queries + tag-based boost (currently ignored).
  • If lift rate < 20%: investigate why — judge variance, distance gap too wide, or playbook math too gentle. The score=1.0 / 0.5× formula may need retuning.
  • If discovery rate (cold judge-best ≠ top-1) is itself low: cosine is already close to optimal on this query distribution. Either the corpus is too narrow or the queries are too easy.