e9822f025d
Adds an opt-in Pass 3 to the lift driver: for each query whose Pass 1 recorded a playbook, ask the judge to rephrase the query, then re-query with playbook=true and check whether the recorded answer surfaces in top-K. This is the test the v1 report's caveat #3 explicitly flagged as the actual learning-property gate (not the cheap verbatim case). Implementation: - New flag --with-paraphrase on the driver (default off). - New WITH_PARAPHRASE env in the harness (default 1, on for prod runs). - New paraphrase_* fields on queryRun + summary, // 0 fallback in jq so re-rendering verbatim-only evidence stays clean. - generateParaphrase() calls the same judge model with format=json and a tight schema; temperature=0.5 for variance without domain drift. - Markdown report adds a paraphrase per-query table (only when the pass ran) and an honesty caveat about judge-also-rephrases coupling. Run #002 result (reports/reality-tests/playbook_lift_002.{json,md}): Verbatim lift 2/2 (100% — Q7 + Q13, both stable from v1) Paraphrase top-1 0/2 Paraphrase any-rank in K 0/2 Both paraphrases dropped the recorded answer OUT of top-K entirely (rank=-1). This isn't a paraphrase-quality problem — qwen2.5's outputs preserved intent ("Hazmat-certified warehouse worker comfortable with cold storage" → "Warehouse worker with Hazmat certification and experience in cold storage"). It's the v0 boost-only stance documented in internal/matrix/playbook.go:22-27: the boost only re-ranks results that ALREADY surfaced from regular retrieval. If paraphrase's cosine retrieval doesn't include the recorded answer in top-K, no boost can promote it. The "Shape B" upgrade mentioned in the playbook.go comment — inject playbook hits directly even when they weren't in the top-K — is what would close this gap. The reality test surfaced exactly the gap the docs warned about. Worth filing as the next product gate. Run-to-run variance also visible: v1 had 8 discoveries, v2 had 2. HNSW insertion order + judge variance both contribute. Stability of Q7 and Q13 across both runs (lifted in v1 AND v2) is the most reliable signal in the dataset. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
reports/reality-tests — does the 5-loop substrate actually work?
Reality tests measure product outcomes, not substrate health. The 21 smokes prove the system runs; the proof harness proves the system makes the claims it claims; reality tests answer: does the small-model pipeline + matrix indexer + playbook give measurably better results than raw cosine?
This is the gate from project_small_model_pipeline_vision.md: "the playbook + matrix indexer must give the results we're looking for." Single load-bearing criterion. Throughput, scaling, code elegance are secondary.
What lives here
Each reality test is a numbered run that produces:
<test>_<NNN>.json— raw structured evidence (per-query data, summary metrics)<test>_<NNN>.md— human-readable report with headline metrics, per-query table, honesty caveats, next moves
Runs are append-only. Earlier runs stay in tree as historical baseline.
Test catalog
playbook_lift_<NNN> — does the playbook actually lift the right answer?
Driver: scripts/playbook_lift.sh → bin/playbook_lift
Queries: tests/reality/playbook_lift_queries.txt
Pipeline: cold pass → LLM judge → playbook record → warm pass → measure ranking shift.
The headline question: when the LLM judge finds a better answer than cosine top-1, can the playbook boost it to top-1 on the next run? If yes, the learning loop closes; if no, the matrix layer + playbook is infrastructure for a thesis that doesn't pay rent.
See the run reports for honesty caveats — chiefly that the LLM judge IS the ground-truth proxy.
Running a reality test
# Defaults: judge resolved from lakehouse.toml [models].local_judge,
# workers limit 5000, run id 001
./scripts/playbook_lift.sh
# Re-run with a different judge to check inter-judge agreement
# (env JUDGE_MODEL overrides the config tier)
JUDGE_MODEL=qwen3:latest RUN_ID=002 ./scripts/playbook_lift.sh
# Smaller scale for fast iteration
WORKERS_LIMIT=1000 K=5 RUN_ID=dev ./scripts/playbook_lift.sh
Judge resolution priority (Phase 3, 2026-04-29):
-judgeflag on the Go driver (explicit override)JUDGE_MODELenv var (operator override)lakehouse.toml [models].local_judge(default)- Hardcoded
qwen3.5:latest(last-resort fallback if config missing)
This means model bumps land in lakehouse.toml, not in this script or
the Go driver. Bumping local_judge to a stronger local model (e.g.
when qwen4 ships) takes one line.
Requires: Ollama on :11434 with nomic-embed-text + the resolved judge
model loaded. Skips cleanly (exit 0) if Ollama is absent.
Interpreting results
Three thresholds matter on the playbook_lift tests:
| Lift rate (lifts / discoveries) | Verdict |
|---|---|
| ≥ 50% | Loop closes — playbook is doing real work, move to paraphrase queries |
| 20-50% | Lift exists but inconsistent — investigate boost math (score × 0.5) or judge variance |
| < 20% | Loop is not pulling its weight — diagnose before adding more components |
A separate concern: discovery rate (cold judge-best ≠ cold top-1). If discovery is itself rare (< 30% of queries), cosine is already close to optimal on this query distribution and the matrix+playbook layer has little headroom. That's not necessarily a bug — but it means the value gate has to come from somewhere else (multi-corpus retrieval, domain-specific tags, drift signal).
What this is not
- Not a benchmark. No comparison against external systems; only internal cold-vs-warm.
- Not a regression gate. Each run is a snapshot. Scores will drift with corpus changes, judge updates, and playbook math tuning. Don't wire
just verifyto demand a minimum lift. - Not human-validated. The LLM judge is the ground truth proxy. Sample 5-10 verdicts manually per run to sanity-check the judge isn't pathological.