0331288641
real_003 left a known-weak hole: shorthand-style queries
("{count} {role} {city} {state} ...") have no separator between
role and city, so a regex can't reliably extract — leaving the
cross-role gate disabled when both record AND query are shorthand.
This commit adds a roleExtractor with regex-first + LLM fallback:
- Regex first (fast, deterministic) — handles need + client_first +
looking from real_003b. ~75% of styles, no LLM cost paid.
- LLM fallback when regex returns empty AND model is configured —
Ollama-shape /api/chat with format=json, schema-tight prompt,
temperature 0. ~1-3s on local qwen2.5.
- Per-process cache — paraphrase + rejudge passes reuse the same
query 4× per run; cache prevents 4× LLM cost.
- Off-by-default — opt-in via -llm-role-extract flag (CLI) and
LLM_ROLE_EXTRACT=1 env var (harness wrapper). real_003b shipping
config unchanged unless explicitly enabled.
8 new tests in scripts/playbook_lift/main_test.go:
- TestRoleExtractor_RegexFirst: LLM not called when regex matches
- TestRoleExtractor_LLMFallback: shorthand goes to LLM
- TestRoleExtractor_LLMOffLeavesEmpty: opt-in default preserved
- TestRoleExtractor_Cache: 3 calls = 1 LLM hit
- TestRoleExtractor_NilSafe: nil receiver runs regex only
- TestExtractRoleViaLLM_HTTPError + _BadJSON: failure paths
- TestRoleExtractor_ClosesCrossRoleShorthandBleed: synthetic
witness for the real_003 scenario — both record + query are
shorthand, regex returns "" for both, LLM produces DIFFERENT
role tokens for CNC vs Forklift, so matrix gate's cross-role
rejection (locked separately in
TestInjectPlaybookMisses_RoleGateRejectsCrossRole) fires
correctly. This is the load-bearing verification.
Reality test real_004 ran the same 40-query stress as real_003 with
LLM extraction on. Cross-style same-role boosts fired correctly
across all 4 styles for Loaders + Packers + Shipping Clerk clusters
(including shorthand → other-style transfer). No cross-role bleed
observed. The reality test alone can't be a clean "with vs without"
comparison (HNSW build is non-deterministic across runs, and
real_004 stochastics didn't trigger a shorthand recording at all),
which is why the unit-test witness exists.
Production note (in real_004_findings.md): LLM extraction is for
reality-test coverage of arbitrary query shapes. Production should
extract role at INGEST time (when the inbox parser already runs an
LLM) and pass already-resolved role through requests — same shape
as multi_coord_stress's existing Demand{Role: ...} model. The hot
path should never need the harness extractor's per-query LLM cost.
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.