3dd7d9fe30
First reality test driver. Two-pass design:
- Pass 1 (cold): matrix.search use_playbook=false → small-model judge
rates top-K → record playbook entry pointing at the highest-rated
result (which may NOT be top-1 by distance — that's the discovery).
- Pass 2 (warm): same queries with use_playbook=true → measure
ranking shift. Lift = real if recorded answer becomes top-1.
Files:
- scripts/playbook_lift/main.go driver (391 LoC)
- scripts/playbook_lift.sh stack-bring-up + report gen
- tests/reality/playbook_lift_queries.txt query corpus (5 placeholders;
J writes real 20+)
- reports/reality-tests/README.md framework + interpretation
- .gitignore track reports/reality-tests/
but ignore per-run JSON evidence
This answers the gate from project_small_model_pipeline_vision.md:
"the playbook + matrix indexer must give the results we're looking
for." Without ground-truth labels, the LLM judge is the proxy — the
same small-model thesis applied to evaluation. Honest about that
limitation in the generated reports.
Driver compiles clean; full run requires Ollama + workers/candidates
ingest. Skips cleanly if Ollama absent.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
70 lines
3.3 KiB
Markdown
70 lines
3.3 KiB
Markdown
# reports/reality-tests — does the 5-loop substrate actually work?
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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?**
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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.
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---
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## What lives here
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Each reality test is a numbered run that produces:
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- `<test>_<NNN>.json` — raw structured evidence (per-query data, summary metrics)
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- `<test>_<NNN>.md` — human-readable report with headline metrics, per-query table, honesty caveats, next moves
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Runs are append-only. Earlier runs stay in tree as historical baseline.
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---
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## Test catalog
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### `playbook_lift_<NNN>` — does the playbook actually lift the right answer?
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**Driver:** `scripts/playbook_lift.sh` → `bin/playbook_lift`
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**Queries:** `tests/reality/playbook_lift_queries.txt`
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**Pipeline:** cold pass → LLM judge → playbook record → warm pass → measure ranking shift.
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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.
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See the run reports for honesty caveats — chiefly that the LLM judge IS the ground-truth proxy.
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---
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## Running a reality test
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```bash
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# Defaults: judge=qwen3.5:latest, workers limit 5000, run id 001
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./scripts/playbook_lift.sh
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# Re-run with a different judge to check inter-judge agreement
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JUDGE_MODEL=qwen2.5:latest RUN_ID=002 ./scripts/playbook_lift.sh
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# Smaller scale for fast iteration
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WORKERS_LIMIT=1000 K=5 RUN_ID=dev ./scripts/playbook_lift.sh
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```
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Requires: Ollama on `:11434` with `nomic-embed-text` + the chosen judge model loaded. Skips cleanly (exit 0) if Ollama is absent.
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---
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## Interpreting results
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Three thresholds matter on the `playbook_lift` tests:
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| Lift rate (lifts / discoveries) | Verdict |
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|---|---|
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| ≥ 50% | Loop closes — playbook is doing real work, move to paraphrase queries |
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| 20-50% | Lift exists but inconsistent — investigate boost math (`score × 0.5`) or judge variance |
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| < 20% | Loop is not pulling its weight — diagnose before adding more components |
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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).
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---
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## What this is not
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- **Not a benchmark.** No comparison against external systems; only internal cold-vs-warm.
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- **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 verify` to demand a minimum lift.
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- **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.
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