08a086779b
Runs #003-#009 surfaced the same finding: fresh workers added mid-run to the main 'workers' vectord index (5K items) reliably *absorbed* (HTTP 200) but failed to *surface* in semantic queries even with content-matching prompts. Distances on the verify queries sat at 0.25-0.65 against existing workers; fresh items were beyond top-K. Better embedder (v2-moe) didn't help — distances got TIGHTER on existing items, pushing fresh items further out of reach. Root cause: coder/hnsw incremental adds to a populated graph land in poorly-connected regions and disappear from search traversal. Known property of HNSW post-build adds; not a bug. Fix: two-tier index pattern (canonical NRT search architecture). Fresh content goes to a small "hot" corpus (fresh_workers); main queries include it in the corpora list and merge results. Hot corpus has no recall crowding because it's tiny; periodic batch job (post- G3) merges it into the main index. Implementation: - ensureFreshIndex(hc, gw, name, dim) — idempotent POST /v1/vectors/index. 409 from re-create treated as "already there." - ingestFreshWorker now takes idx parameter so callers can target fresh_workers instead of workers. - multi_coord_stress phase 1b creates fresh_workers index + ingests 3 fresh workers there + searches verifyCorpora=[workers, ethereal_workers, fresh_workers]. Run #010 result: fresh-001 (Senior tower crane rigger NCCCO Chicago) top-1: fresh-001 from fresh_workers, distance 0.143 fresh-002 (Bilingual Spanish/English OSHA trainer Indianapolis) top-1: fresh-002 from fresh_workers, distance 0.146 fresh-003 (FAA Part 107 drone surveyor Chicago) top-1: fresh-003 from fresh_workers, distance 0.129 3/3 fresh workers surface at top-1 — the absorption-but-not- findable issue from runs #003-#009 is closed. All other metrics held: diversity 0.007, determinism 1.000, verbatim handover 4/4, paraphrase handover 4/4, swap Jaccard 0.000, inbox burst all 6 events accepted + traced to Langfuse. This is the final structural fix for the multi-coord stress suite. Phase 3 is feature-complete. 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.