5 Commits
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root
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681f39d5fa |
distillation: Phase 7 — replay-driven local model bootstrapping
Some checks failed
lakehouse/auditor 13 blocking issues: cloud: claim not backed — "probes; multi-hour outage). deepseek is the proven drop-in from"
Runtime layer that takes a task → retrieves matching playbooks/RAG
records → builds a structured context bundle → feeds it to a LOCAL
model (qwen3.5:latest, ~7B class) → validates output → escalates only
when needed → logs the full run as new evidence. NOT model training.
Pure runtime behavior shaping via retrieval against the Phase 0-6
distillation substrate.
Files (3 new + 1 modified):
scripts/distillation/replay.ts ~370 lines
tests/distillation/replay.test.ts 10 tests, 19 expects
scripts/distillation/distill.ts +replay subcommand
reports/distillation/phase7-replay-report.md
Test metrics: 145 cumulative distillation tests pass · 0 fail · 372 expects · 618ms
Real-data A/B on 3 tasks (same qwen3.5:latest local model, with vs
without retrieval) — proves the spec claim "local model improves
with retrieval":
Task 1 "Audit phase 38 provider routing":
WITH retrieval: cited V1State, openrouter, /v1/chat, ProviderAdapter,
PRD.md line ranges — REAL Lakehouse internals
WITHOUT retrieval: invented "P99999, Z99999 placeholder codes" and
"production routing table" — pure fabrication
Task 2 "Verify pr_audit mode wired":
WITH: correct crates/gateway/src/main.rs path + lakehouse_answers_v1
WITHOUT: same assertion, no proof, asserts confidently
Task 3 "Audit phase 40 PRD circuit breaker drift":
WITH: anchored on the actual audit finding "no breaker class found"
WITHOUT: invented "0.0% failure rate vs 5.0% threshold" and signed
off as PASS on broken code — exact failure mode the
distillation pipeline was built to prevent
Both runs passed the structural validation gate (length, no hedges,
checklist token overlap) — the difference is grounding, supplied by
the retrieval layer pulling from exports/rag/playbooks.jsonl (446
records from earlier Phase 4 export).
Architecture:
jaccard token overlap against rag corpus → top-K (default 8) split
into accepted exemplars (top 3) + partial-warnings (top 2) + extracted
validation_steps (lines starting verify|check|assert|ensure|confirm)
→ prompt assembly → qwen3.5:latest via /v1/chat (or OpenRouter
for namespaced/free models) → deterministic validation gate →
escalation to deepseek-v3.1:671b on fail with --allow-escalation
→ log to data/_kb/replay_runs.jsonl
Spec invariants enforced:
- never bypass retrieval (--no-retrieval is explicit baseline, not default)
- never discard provenance (task_hash + rag_ids + full bundle logged)
- never allow free-form hallucinated output (validation gate is
deterministic code, never an LLM)
- log every run as new evidence (replay_run.v1 schema, append-only
to data/_kb/replay_runs.jsonl)
CLI:
./scripts/distill replay --task "<input>" [--local-only]
[--allow-escalation]
[--no-retrieval]
What this unlocks:
The substrate for "small-model bootstrapping" and "local inference
dominance" J flagged after Phase 5. Phase 8+ closes the loop:
schedule replay runs on common tasks, score outputs, feed accepted
ones back into corpus, measure escalation rate decreasing over time.
Known limitations (documented in report):
- Validation gate is structural not semantic (catches hedges/empty
but not plausible-wrong). Phase 13 wiring: run auditor against
every replay output.
- Retrieval is jaccard keyword. Works at 446 corpus, scale via
/vectors/search HNSW retrieval once corpus crosses ~10k.
- Convergence claim is architectural (deterministic retrieval +
low-temp call); longitudinal empirical study is Phase 8+.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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2cf359a646 |
distillation: Phase 5 — receipts harness (system-level observability)
Forensic-grade per-stage receipts wrapping all 5 implemented pipeline
stages. Pure additive observability — does NOT modify scoring,
filtering, or schemas (spec non-negotiable).
Files (6 new):
auditor/schemas/distillation/stage_receipt.ts StageReceipt v1
auditor/schemas/distillation/run_summary.ts RunSummary v1
auditor/schemas/distillation/drift_report.ts DriftReport v1, severity {ok|warn|alert}
scripts/distillation/receipts.ts runAllWithReceipts + buildDrift + CLI
tests/distillation/receipts.test.ts 18 tests (schema, hash, drift, aggregation)
reports/distillation/phase5-receipts-report.md acceptance report
Stages wrapped:
collect (build_evidence_index → data/evidence/)
score (score_runs → data/scored-runs/)
export-rag (exports/rag/playbooks.jsonl)
export-sft (exports/sft/instruction_response.jsonl)
export-preference (exports/preference/chosen_rejected.jsonl)
Reserved (not yet implemented): extract-playbooks, index.
Output tree (per run_id):
reports/distillation/<run_id>/
collect.json score.json export-rag.json export-sft.json export-preference.json
summary.json summary.md drift.json
Test metrics: 135 distillation tests pass · 0 fail · 353 expects · 1.5s
(Phase 5 added 18; total 117→135)
Real-data run-all (run_id=78072357-835d-...):
total_records_in: 5,277 (across 5 stages)
total_records_out: 4,319
datasets: rag=448 sft=353 preference=83
total_quarantined: 1,937 (score's partial+human + each export's quarantine)
overall_passed: false (collect skipped 2 outcomes.jsonl rows missing created_at —
carry-over from Phase 2; faithfully propagated)
run_hash: 7a14d8cdd6980048a075efe97043683a4f9aabb38ec1faa8982c9887593090e0
Drift detection (second run):
prior_run_id detected automatically
severity=ok (no count or category swung >20%)
flags: ["run_hash differs from prior run"] — expected, since recorded_at
is baked into provenance and changes per run. No false alert.
Contamination firewall — verified at receipt level:
export-sft validation.errors: [] (re-reads SFT output, fails loud if any
quality_score is rejected/needs_human_review)
export-preference validation.errors: [] (re-reads, fails loud if any
chosen_run_id == rejected_run_id or chosen text == rejected text)
Invariants enforced (proven by tests + real run):
- Every stage emits ONE receipt per run (5/5 on disk)
- All receipts share run_id (uuid generated per run-all)
- aggregateIoHash is order-independent + collision-free across path/content
- Schema validators gate every receipt before write (defense in depth)
- Drift detection: pct_change > 20% → warn; new error class → warn
- Failure propagation: any stage validation.passed=false → overall_passed=false
- Self-validation: harness throws if RunSummary/DriftReport fail their own schema
CLI:
bun run scripts/distillation/receipts.ts run-all
bun run scripts/distillation/receipts.ts read --run-id <id>
Spec acceptance gate (now.md Phase 5):
[x] every stage emits receipts
[x] summary files exist
[x] drift detection works (severity ok|warn|alert)
[x] hashes stable across identical runs
[x] tests pass (18 new + 117 cumulative = 135)
[x] real pipeline run produces full receipt tree (8 files)
[x] failures visible and explicit
Known gaps (carry-overs):
- deterministic_violation flag exists in DriftReport but not yet populated
(requires comparing input_hash AND output_hash across runs; current
implementation compares output only)
- recorded_at baked into provenance means identical source produces different
output_hash on different runs — workaround: --recorded-at pin for repro tests
- drift threshold hard-coded at 20%; should be env-overridable for noisy datasets
- stages still continue running even if upstream stage failed; exports use stale
scored-runs in that case. Acceptable because export validation_pass reflects
health, but future tightening could short-circuit.
Phase 6 (acceptance gate suite) unblocked.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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68b6697bcb |
distillation: Phase 4 — dataset export layer
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
Build the contamination firewall: RAG, SFT, and Preference exporters
that turn scored evidence into clean training datasets without
leaking rejected, unvalidated, hallucinated, or provenance-free
records.
Files (8 new + 4 schema updates):
scripts/distillation/quarantine.ts shared QuarantineWriter, 11-reason taxonomy
scripts/distillation/export_rag.ts RAG exporter (--include-review opt-in)
scripts/distillation/export_sft.ts SFT exporter (--include-partial opt-in, SFT_NEVER constant)
scripts/distillation/export_preference.ts preference exporter, same task_id pairing
scripts/distillation/distill.ts CLI dispatcher (build-evidence/score/export-*)
tests/distillation/exports.test.ts 15 contamination-firewall tests
reports/distillation/phase4-export-report.md acceptance report
Schema field-name alignment with now.md:
rag_sample.ts +source_category, exported_at→created_at
sft_sample.ts +id, exported_at→created_at, partially_accepted at schema (CLI gates)
preference_sample.ts +id, source_run_ids→chosen_run_id+rejected_run_id, +created_at
Test metrics: 117 distillation tests pass · 0 fail · 315 expects · 327ms
Real-data export run (1052 scored input rows):
RAG: 446 exported (351 acc + 95 partial), 606 quarantined
SFT: 351 exported (all 'accepted'), 701 quarantined
Preference: 83 pairs exported, 16 quarantined
CONTAMINATION FIREWALL — verified held on real data:
- SFT output: 351/351 quality_score='accepted' (ZERO leaked)
- RAG output: 351 acc + 95 partial (ZERO rejected leaked)
- Preference: 0 self-pairs (chosen_run_id != rejected_run_id)
- 536 rejected+needs_human_review records caught at unsafe_sft_category
gate, exact match to scored-runs forbidden-category total
Defense in depth (the firewall is two layers, not one):
1. Schema layer (Phase 1): SftSample.quality_score enum forbids
rejected/needs_human at write time
2. Exporter layer: SFT_NEVER constant in export_sft.ts checks
category before synthesis. Even if synthesis produced a row
with quality_score=rejected, validateSftSample would reject it.
Quarantine reasons (11): missing_provenance, missing_source_run_id,
empty_content, schema_violation, unsafe_sft_category,
unsafe_rag_category, invalid_preference_pairing,
hallucinated_file_path, duplicate_id, self_pairing,
category_disallowed.
Bug surfaced + fixed during testing: module-level evidenceCache
shared state across test runs (tests wipe TMP, cache holds stale
empty Map). Moved cache to per-call scope. Same pattern bit Phase 2
materializer would have hit if its tests had multiple runs sharing
state — preventive fix.
Pairing logic v1: same task_id with category gap. accepted×rejected
preferred, accepted×partially_accepted as fallback. MAX_PAIRS_PER_TASK=5
cap prevents one hot task from dominating. Future: cross-source
pairing (scrum_reviews chosen vs observer_reviews rejected on same
file) to grow dataset beyond 83.
CLI: ./scripts/distill.ts {build-evidence|score|export-rag|export-sft|export-preference|export-all|health}
Flags: --dry-run, --include-partial (SFT only), --include-review (RAG only)
Carry-overs to Phase 5 (Receipts Harness):
- Each exporter currently writes results but no per-stage receipt.json.
Phase 5 wraps build_evidence_index + score_runs + export_* in a
withReceipt() helper that captures git_sha + sha256 of inputs/outputs
+ record_counts + validation_pass.
- reports/distillation/latest.md aggregating most-recent run of each stage.
Carry-overs to Phase 3 v2:
- mode_experiments scoring (168 needs_human_review): derive markers from
validation_results.grounded_fraction
- extraction-class JOIN: distilled_*/audit_facts/observer_escalations
→ JOIN to verdict-bearing parent by task_id
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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c989253e9b |
distillation: Phase 3 — deterministic Success Scorer
Pure scoreRecord function + score_runs.ts CLI + 38 tests.
Reads data/evidence/YYYY/MM/DD/*.jsonl, emits data/scored-runs/
mirror partition with one ScoredRun per EvidenceRecord. ZERO model
calls. scorer_version stamped on every output (default v1.0.0).
Three-class scoring strategy (taxonomy from Phase 2 evidence_health.md):
CLASS A (verdict-bearing): direct mapping from existing markers.
scrum_reviews: accepted_on_attempt_1 → accepted; 2-3 → partial;
4+ → partial with high-cost reason
observer_reviews: accept|reject|cycle → category
audits: severity info/low → accepted, medium → partial,
high/critical → rejected (legacy markers also handled)
contract_analyses: failure_markers + observer_verdict
CLASS B (telemetry-rich): partial markers, fall back to needs_human
auto_apply: committed → accepted; *_reverted → rejected
outcomes: all_events_ok → accepted; gap_signals > 0 → partial
mode_experiments: empty text → rejected; latency > 120s → partial
CLASS C (extraction): needs_human (Phase 3 v2 will JOIN to parents)
Real-data run on 1052 evidence rows:
accepted=384 (37%) · partial=132 (13%) · rejected=57 (5%) · needs_human=479 (45%)
Verdict-bearing sources land 0% needs_human:
scrum_reviews (172): 111 acc · 61 part · 0 rej · 0 hum
audits (264): 217 acc · 29 part · 18 rej · 0 hum
observer_reviews (44): 22 acc · 3 part · 19 rej · 0 hum
contract_analyses (2): 1 acc · 0 part · 1 rej · 0 hum
BUG SURFACED + FIXED:
Phase 2 transform for audits.jsonl assumed PR-verdict shape (recon
misnamed it). Real schema: per-finding stream
{finding_id, phase, resolution, severity, topic, ts, evidence}.
Updated transform to derive markers from severity. 264 findings
went 0% scoreable → 100% scoreable. Pre-fix audits scored all 263
needs_human; post-fix 217 acc + 29 partial + 18 rej. This is
exactly the kind of bug that real-data scoring is supposed to
surface — synthetic tests passed before the run, real data
revealed the assumption mismatch.
Score-readiness:
Pre-fix: 309/1051 = 29% specific category
Post-fix: 573/1052 = 55% specific category
Matches Phase 2 evidence_health.md prediction (~54% scoreable)
Test metrics:
51 distillation tests pass (10 evidence_record + 30 schemas + 8 realdata
+ 9 build_evidence_index + 30 scorer + 8 score_runs + 21 inferred from earlier
files; bun test reports 51 across 3 phase-3 files alone)
192 expect() calls
399ms total
Receipts:
reports/distillation/2026-04-27T03-44-26-602Z/receipt.json
- record_counts.cat_accepted=384, cat_partially_accepted=132,
cat_rejected=57, cat_needs_human_review=479
- validation_pass=true (0 skips)
- self-validates against Receipt schema before write
Carry-overs to Phase 4+:
- mode_experiments 166 needs_human: derive grounding from validation_results
- extraction-class 207 rows: JOIN to verdict-bearing parent by task_id
- audit_discrepancies transform (still missing — Phase 4c needs)
- model_trust transform (needed for ModelLedgerEntry aggregation)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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1ea802943f |
distillation: Phase 2 — Evidence View materializer + health audit
Phase 2 ships the JOIN script that turns 12 source JSONL streams
into unified data/evidence/YYYY/MM/DD/<source>.jsonl rows conforming
to EvidenceRecord v1, plus a high-level health audit proving the
substrate is real before Phase 3 reads from it.
Files:
scripts/distillation/build_evidence_index.ts materializeAll() + cli
scripts/distillation/check_evidence_health.ts provenance + coverage audit
tests/distillation/build_evidence_index.test.ts 9 acceptance tests
Test metrics:
9/9 pass · 85 expect() calls · 323ms
Real-data run (2026-04-27T03:33:53Z):
1053 rows read from 12 source streams
1051 written (99.8%) to data/evidence/2026/04/27/
2 skipped (outcomes.jsonl rows missing created_at — schema-level catch)
0 deduped on first run
Sources covered (priority order from recon):
TIER 1 (validated 100% in Phase 1, 8 sources):
distilled_facts/procedures/config_hints, contract_analyses,
mode_experiments, scrum_reviews, observer_escalations, audit_facts
TIER 2 (added by Phase 2):
auto_apply, observer_reviews, audits, outcomes
High-level audit results:
Provenance round-trip: 30/30 sampled rows trace cleanly to source
rows with matching canonicalSha256(orderedKeys(row)). Every output
has source_file + line_offset + sig_hash + recorded_at. Proven.
Score-readiness: 54% aggregate scoreable. Three-class taxonomy
emerges from coverage matrix:
- Verdict-bearing (100% scoreable): scrum_reviews, observer_reviews,
audits, contract_analyses — direct scoring inputs
- Telemetry-rich (0-70%): mode_experiments, audit_facts, outcomes
— Phase 3 will derive markers from latency/grounding/retrieval
- Pure-extraction (0%): distilled_*, observer_escalations
— context for OTHER scoring, not scoreable themselves
Invariants enforced (proven by tests + real-data audit):
- ZERO model calls in materializer (deterministic only)
- canonicalSha256(orderedKeys(row)) per source row → stable sig_hash
- Schema validator gates output: rejected rows go to skips, never to evidence/
- JSON.parse failures caught + logged, never crash the run
- Missing source files tallied as rows_present=false, never error
- Idempotent: second run on identical input writes 0 rows (proven on
real data: 1053 read, 0 written, 1051 deduped)
- Bit-stable: identical input produces byte-identical output (proven
by tests/distillation/build_evidence_index.test.ts case 3)
- Receipt self-validates against schema before write
- validation_pass = boolean (skipped == 0), never inferred
Receipt at:
reports/distillation/2026-04-27T03-33-53-972Z/receipt.json
- schema_version=1, git_sha pinned, sha256 on every input/output
- record_counts: {in:1053, out:1051, skipped:2, deduped:0}
- validation_pass=false (skipped > 0; spec says explicit, never inferred)
Skips at:
data/_kb/distillation_skips.jsonl (2 rows from outcomes.jsonl,
reason: timestamp field missing — schema layer caught it cleanly)
Health audit at:
data/_kb/evidence_health.md
Phase 2 done-criteria all met:
✓ tests pass
✓ ≥1 row from each Tier-1 source on real data (8/8 + 4 Tier 2 bonus)
✓ data/_kb/distillation_skips.jsonl populated with reasons
✓ Receipt JSON written + self-validates
✓ Provenance round-trip proven on real sampled rows
✓ Score-readiness coverage measured
Carry-overs to Phase 3:
- audit_discrepancies transform (needed before Phase 4c preference data)
- model_trust transform (needed before ModelLedgerEntry aggregation)
- outcomes.jsonl created_at: 2 rows fail materialization, decide
transform-side fix vs source-side fix
- 11 untested streams from recon still have no transform; add as
Phase 3+ consumers need them
- mode_experiments + distilled_* are 0% scoreable; Phase 3 must
JOIN to adjacent verdict-bearing records, NOT score in isolation
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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