# 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

```bash
# 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):
1. `-judge` flag on the Go driver (explicit override)
2. `JUDGE_MODEL` env var (operator override)
3. `lakehouse.toml [models].local_judge` (default)
4. 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 verify` to 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.