186d209aaef860c6045b5809fc5c9dffe8aae4ea
6
Commits
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 rootandClaude Opus 4.7
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186d209aae |
multi_coord_stress: LLM-parsed inbox demands (qwen2.5)
Replaced the hard-coded DemandQuery on inbox events with an actual
LLM call: each email/SMS body is parsed by qwen2.5 (format=json,
schema-anchored) into structured {role, count, location, certs,
skills, shift}. The driver then composes a query string from those
fields and runs matrix.search.
This is the real-product flow that the Phase 3 stress test was
asking for: real bodies → real LLM parsing → real search. Before
this commit, the DemandQuery was my hand-crafted string, which
made the inbox phase trivial.
Run #007 result vs #006 (same bodies, parser swapped):
All 6 inbox events parsed cleanly — qwen2.5 nailed:
"Need 50 forklift operators in Cleveland OH for Monday day
shift. OSHA-30 + active forklift cert required."
→ {role:"forklift operator", count:50, location:"Cleveland, OH",
certs:["OSHA-30","active forklift cert"], skills:[], shift:"day"}
Other 5 similarly faithful (indy stayed as "indy", count
defaulted to 1 when unspecified, no hallucinated fields).
LLM-parsed queries produced TIGHTER matches than hard-coded:
Demand #006 dist #007 dist Δ
Crane Chicago 0.499 0.093 -82%
Drone Chicago 0.707 0.073 -90%
Bilingual safety 0.240 0.048 -80%
Forklift Cleveland 0.330 0.273 -17%
Production Indy 0.260 0.399 +53%
Warehouse Milwaukee 0.458 0.420 -8%
Three matches landed at distance < 0.10 — verbatim-replay-tight
territory. Structured queries embed sharper than conversational
hand-crafted strings.
Other metrics unchanged: diversity 0.000, determinism 1.000,
verbatim handover 4/4, paraphrase handover 4/4.
Tradeoff worth flagging: the drone-Chicago case dropped from
distance 0.71 (clear "we don't have one") to 0.07 (confident match
returned). The OOD honesty signal weakens when LLM-parsed structure
makes any closest-neighbor look tight. Future Phase 4 work: judge
re-rates the top match before surfacing, so coordinators see "your
demand was for X but the closest match scored 2/5" rather than just
the worker ID + distance.
Substrate cost: +6 LLM calls per inbox burst (~9s on qwen2.5).
Production would amortize via a small dedicated parser model.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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rootandClaude Opus 4.7
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e7fc63b216 |
observerd: /observer/inbox + multi-coord stress phase 1c (priority-ordered events)
Phase 3 ask: real-world inbox-style event injection during the stress
test. Coordinators in production receive emails + SMS that trigger
contract responses; the substrate has to RECORD these signals AND
react with a search using the embedded demand. This commit lands the
endpoint and exercises it end-to-end in the stress harness.
observerd surface:
- New POST /observer/inbox route — accepts {type, sender, subject,
body, priority, tag} and records as ObservedOp with
Source=SourceInbox. Type must be email|sms; body required;
priority defaults to medium. The handler ONLY records — downstream
triggers (search, ingest, etc.) are the caller's concern, recorded
separately. Keeps the witness role pure.
- New observer.SourceInbox = "inbox" alongside SourceMCP /
SourceScenario / SourceWorkflow.
- Three contract tests on the new route (happy path / bad type / empty
body), router-mount test extended, all green.
Stress harness phase 1c (Hour 9):
- 6 inbox events fire in priority order (urgent → high → medium):
2 urgent emails (forklift Cleveland, production Indianapolis)
1 high email (crane Chicago)
1 high sms (bilingual safety Indianapolis)
1 medium sms (drone Chicago)
1 medium email (warehouse Milwaukee FYI)
- Each event:
1. POSTs to /v1/observer/inbox (recorded by observerd)
2. Triggers matrix.search using a parsed demand (the demand
extraction is hard-coded for now; production needs a small
LLM to parse from body)
3. Captures both as events in the run JSON
Run #006 result (with v2-moe embedder + all phases including inbox):
Diversity:
Same-role-across-contracts Jaccard = 0.000 (n=9)
Different-roles-same-contract Jaccard = 0.046 (n=18)
Determinism: 1.000
Verbatim handover: 4/4 (100%)
Paraphrase handover: 4/4 (100%)
Inbox burst:
6/6 events accepted by observerd (200 status, all recorded)
6/6 triggered searches produced distinct top-1 worker IDs
distance distribution: 0.24 (Indy production) → 0.71 (Chicago
drone surveyor — honest stretch since drones aren't in the
5K-worker corpus, system surfaces closest neighbor at high
distance rather than fabricating)
The drone-Chicago case is the architectural-honesty signal: when
the demand asks for a specialist NOT in the roster, the system
returns the closest semantic neighbor with a distance that flags
"this is a stretch." Coordinators reading distances see "we don't
have a great match here" rather than a confident wrong answer.
Total events captured: 67 (was 61 pre-inbox).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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rootandClaude Opus 4.7
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4da32ad102 |
embedd: bump default to nomic-embed-text-v2-moe (475M MoE, 768d drop-in)
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> |
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rootandClaude Opus 4.7
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84a32f0d29 |
multi-coord stress Phase 2: ExcludeIDs + fresh-resume + 200-worker swap
Three Phase 2 additions land in this commit: 1. matrix.SearchRequest gains ExcludeIDs ([]string) — filters specific worker IDs out of results post-retrieval, AND skips them at the playbook boost+inject step (so excluded answers can't sneak back via Shape B). Real-world driver: coordinator placed N workers, client asks for replacements, system needs alternatives, not the same N. Threaded through retrieve.go after merge but before metadata filter so excluded IDs don't waste post-filter top-K slots. 2. New harness phase 2b: 200-worker swap simulation. Captures the top-K from alpha's warehouse query, then re-issues with exclude_ids=<placed>. Result Jaccard(orig, swap) measures whether the substrate finds genuine alternatives. 3. New harness phase 1b: fresh-resume mid-run injection. Three new workers ingested via /v1/embed + /v1/vectors/index/workers/add, then verified findable via semantic queries matching resume content. Plus Hour labels on every event (operational narrative: 0/6/12/18/ 24/30/36/42/48) and a refactor of captureEvent to take hour as a param. Run #003 + #004 results (5K workers + 10K ethereal): Diversity (#004): Same-role-across-contracts Jaccard = 0.080 (n=9) Different-roles-same-contract Jaccard = 0.013 (n=18) Determinism: 1.000 (#004 unchanged) Verbatim handover: 4/4 = 100% Paraphrase handover: 4/4 = 100% Phase 2b — 200-worker swap (Jaccard 0.000): 8 originally-placed workers fully replaced by 8 alternatives. ExcludeIDs substrate change works end-to-end — boost AND inject both honor the exclusion, so excluded workers don't return via the playbook either. Phase 1b — fresh-resume injection: REAL PRODUCT FINDING. Substrate ABSORPTION is fine — 3 /v1/vectors/index/workers/add calls at 200 status, 3 vectors persisted. But none of the 3 fresh workers surfaced in top-8 even with semantic queries matching their resume content (e.g. "Senior tower crane rigger NCCCO Chicago" vs fresh-001's resume "Senior rigger with 12 years tower-crane signaling..." NCCCO + Chicago). Top-1 came from existing workers at distance ~0.25; fresh workers' distances must be > 0.25, pushing them past rank 8. Cause: dense retrieval at 5000+ workers means many existing profiles cluster near any specific query in cosine space; nomic-embed-text-v2 (137M) introduces enough noise that a fresh worker doesn't reliably outrank them just because the text content overlaps. Workarounds (Phase 3 work): (a) hybrid retrieval (keyword + semantic), (b) playbook-layer score boost for fresh adds, (c) larger embedder. Documented in run #004 report. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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rootandClaude Opus 4.7
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0fa42a0cc3 |
multi-coord stress Phase 1.5: shared-role contracts + paraphrase handover
Phase 1 had two known gaps: (1) the 3 contracts had zero shared role names, so same-role-across-contracts Jaccard was vacuous (n=0); (2) the verbatim handover at 100% was the trivial case, not the hard learning test (paraphrased queries against another coord's playbook). Both fixed in this commit. Contract redesign — all 3 contracts now share warehouse worker / admin assistant / heavy equipment operator roles, plus a unique specialist per contract (industrial electrician / bilingual safety coord / drone surveyor — the "specialist not on the standard roster" case from J's spec). Counts and skill mixes vary per region. New driver phase 4b — paraphrase handover. Bob runs qwen2.5-paraphrased versions of Alice's contract queries against Alice's playbook namespace. Tests whether institutional memory propagates across coordinators AND across natural wording variation that Bob would introduce when running Alice's contract. Run #002 result (5K workers + 10K ethereal_workers, 4 demand × 3 coords + paraphrase handover): Diversity (the question J asked: locking or cycling?): Same-role-across-contracts Jaccard = 0.119 (n=9) → 88% of workers DIFFER across regions for the same role name. Milwaukee warehouse vs Indianapolis warehouse vs Chicago warehouse pull mostly distinct top-K from the same population. The system locks into geo+cert+skill context, not cycling. Different-roles-same-contract Jaccard = 0.004 (n=18) → role-specific retrieval works (unchanged from Phase 1). Determinism: Jaccard = 1.000 (n=12) — unchanged. Learning: Verbatim handover 4/4 = 100% (trivial case, expected) Paraphrase handover 4/4 = 100% (HARD case — passes!) Of those 4 paraphrase recoveries: - 2 used boost (Alice's recording was already in Bob's paraphrase top-K; ApplyPlaybookBoost re-ranked to top-1) - 2 used Shape B inject (recording wasn't in Bob's paraphrase top-K; InjectPlaybookMisses brought it in) The boost/inject mix is healthy — both paths are used and both produce correct top-1s. Multi-coord institutional memory propagation is empirically working under wording variation. Sample warehouse worker top-1s across contracts (proves diversity): alice / Milwaukee → w-713 bob / Indianapolis → e-8447 carol / Chicago → e-7145 Three different workers from the same 15K-person population, selected on geo+cert+skill context. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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rootandClaude Opus 4.7
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61c7b55e48 |
multi-coord stress harness — Phase 1 of 48-hour mock
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>
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