root 08a086779b multi_coord_stress: fresh_workers two-tier index — fresh-resume now top-1

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>

2026-04-30 16:31:45 -05:00

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Multi-Coordinator Stress Test — Run 010

Generated: 2026-04-30T21:30:38.434794788Z Coordinators: alice / bob / carol (each with own playbook namespace: playbook_alice / playbook_bob / playbook_carol) Contracts: alpha_milwaukee_distribution / beta_indianapolis_manufacturing / gamma_chicago_construction Corpora: workers,ethereal_workers K per query: 8 Total events captured: 67 Evidence: reports/reality-tests/multi_coord_stress_010.json

Diversity — is the system locking into scenarios or cycling?

Metric	Mean Jaccard	n pairs	Interpretation
Same role across different contracts	0.007407407407407408	9	Lower = more diverse (different region/cert mix → different workers)
Different roles within same contract	0.026455026455026454	18	Should be near-zero (different roles = different worker pools)

Healthy ranges:

Same role across contracts: < 0.30 means the system is genuinely picking different workers per region/contract.
Different roles same contract: < 0.10 means role-specific retrieval is working.
If either is > 0.50, the system is "cycling" the same handful of workers regardless of query intent.

Determinism — same query reissued, top-K stability

Metric	Value
Mean Jaccard on retrieval-only reissue	1
Number of reissue pairs	12

Interpretation:

≥ 0.95: HNSW retrieval is highly deterministic; reissues land on near-identical top-K. Good — system locks into a stable view of "best workers for this query."
0.80 – 0.95: Some HNSW or embed variance, acceptable.
< 0.80: Retrieval is unstable — reissues see substantially different results, suggesting either embed nondeterminism (Ollama returning slightly different vectors) or vectord nondeterminism (HNSW insertion order affecting recall).

Learning — handover hit rate

Bob takes Alice's contract using Alice's playbook namespace. Did Alice's recorded answers surface in Bob's results?

Metric	Value
Verbatim handover queries run	4
Alice's recorded answer at Bob's top-1 (verbatim)	4
Alice's recorded answer in Bob's top-K (verbatim)	4
Verbatim handover hit rate (top-1)	1
Paraphrase handover queries run	4
Alice's recorded answer at Bob's top-1 (paraphrase)	4
Alice's recorded answer in Bob's top-K (paraphrase)	4
Paraphrase handover hit rate (top-1)	1

Interpretation:

Verbatim hit rate ≈ 1.0: trivial case — Bob runs identical queries; should always hit.
Paraphrase hit rate ≥ 0.5: institutional memory survives wording change — the harder learning property.
Paraphrase hit rate ≈ 0.0: Bob's paraphrases drift past the inject threshold, so Alice's recordings don't activate. Same caveat as the playbook_lift paraphrase pass.

Per-event capture

All matrix.search responses live in the JSON — top-K with worker IDs, distances, and per-corpus counts. Search by phase:

jq '.events[] | select(.phase == "merge")' reports/reality-tests/multi_coord_stress_010.json
jq '.events[] | select(.coordinator == "alice" and .phase == "baseline")' reports/reality-tests/multi_coord_stress_010.json
jq '.events[] | select(.role == "warehouse worker") | {phase, contract, top_k_ids: [.top_k[].id]}' reports/reality-tests/multi_coord_stress_010.json

What's NOT in this run (Phase 1 deliberately defers)

48-hour clock. Events fire as discrete steps, not on a timeline.
Email / SMS ingest. No endpoints exist on the Go side yet.
New-resume injection mid-run. The corpus is fixed at the start.
Langfuse traces. Need Go-side wiring.

These are Phase 2/3. The Phase 1 substrate is what the time-based runner will mount on top of.

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