61c7b55e48
Three coordinators (alice / bob / carol) with three contracts
(Milwaukee distribution / Indianapolis manufacturing / Chicago
construction). 7-phase scenario runner: baseline → surge → merge →
handover → split → reissue → analysis. Each coord has a separate
playbook namespace (playbook_{name}) so institutional memory stays
isolated by default but transferable on demand.
Phase 1 deliberately skips the 48-hour clock, email/SMS endpoints,
and Langfuse tracing — those are Phase 2/3.
Run #001 (52 events, 4 queries × 3 coords × 2 demand flavors):
Diversity:
Different-roles-same-contract Jaccard = 0.004 (n=18)
→ role-specific retrieval is working perfectly. Different
roles within one contract pull totally different worker
pools. System is NOT cycling; locks into per-role retrieval.
Same-role-across-contracts Jaccard = N/A (n=0)
→ TEST-DESIGN ISSUE: the 3 contracts use distinct role names
per industry (warehouse worker / production worker / general
laborer), so no exact-name overlaps exist. Phase 2 should
either share at least one role across contracts OR add a
skill-based diversity metric.
Determinism: Jaccard = 1.000 (n=12)
→ HNSW + Ollama retrieval is fully deterministic on identical
query text. coder/hnsw + nomic-embed-text are stable.
Learning: handover hit rate = 4/4 = 100%
→ Bob inherits Alice's recordings perfectly when bob runs
identical queries with alice's playbook namespace. CAVEAT:
this tests the trivial verbatim case, not paraphrase handover.
The harder test (bob runs paraphrased queries with alice's
playbook) is Phase 2 work.
Per-event capture in JSON: every matrix.search response is logged
with phase / coordinator / contract / role / query / top-K IDs +
distances + per-corpus counts + boosted/injected counts. Reviewable
via:
jq '.events[] | select(.phase == "merge")'
jq '.events[] | select(.coordinator == "alice")'
jq '.events[] | select(.role == "warehouse worker")'
Notable finding from per-event: carol's "general laborer" and "crane
operator" queries both surface w-1009 as top-1, with crane operator
at distance 0.098 (very tight) and general laborer at 0.297. The
system found a worker who legitimately covers both roles — realistic
for small construction crews.
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.