4da32ad102
Local Ollama has three embedding models loaded: nomic-embed-text:latest 137M 768d (previous default) nomic-embed-text-v2-moe:latest 475M 768d (this commit's default) qwen3-embedding:latest 7.6B 4096d (would require dim change) v2-moe is a drop-in upgrade — same 768 dim, 3.5× more params, MoE architecture. Workers index doesn't need rebuilding, just future ingests embed with the stronger model. Run #005 result on the multi-coord stress suite: Diversity (same-role-across-contracts): 0.080 → 0.000 (n=9) → MoE is more discriminating: zero worker overlap across Milwaukee / Indianapolis / Chicago for shared role names. The geo + cert + skill context fully separates worker pools. Different-roles-same-contract: 0.013 → 0.036 (still ~96% diff) Determinism: 1.000 (unchanged) Verbatim handover: 4/4 (100%) Paraphrase handover: 4/4 (100%) 200-worker swap: Jaccard 0.000 (unchanged — still perfect) Fresh-resume verify: STILL doesn't surface fresh workers in top-8. With v2-moe, distances increased (top-1 = 0.43–0.65 vs v1's 0.25–0.39) — the embedder is MORE discriminating, but the fresh worker's vector still doesn't outrank the 8th-best existing worker. Now suspect of being an HNSW post-build add issue (coder/hnsw incremental adds can land in hard-to-reach graph regions, not an embedder problem). Better embedder didn't fix it; needs a different strategy: full index rebuild after fresh adds, or explicit playbook-layer score boost for fresh workers, or hybrid (keyword + semantic) retrieval. Phase 3 investigation. Cost: ingest is ~5× slower (workers 20s→100s; ethereal 35s→112s). Acceptable for the quality jump on diversity. Real production with incremental ingest won't pay this once-per-deploy. 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.