Merge remote-tracking branch 'origin/main' into test/enrich-prd-pipeline

# Conflicts:
#	auditor/audit.ts
#	auditor/checks/inference.ts
#	auditor/checks/kb_query.ts
#	auditor/claim_parser.ts
#	tests/real-world/scrum_master_pipeline.ts

auditor/README.md

@@ -33,6 +33,13 @@ Defaults: polls every 90s, stops on `auditor.paused` file present.
 
 - `data/_auditor/state.json` — last-audited head SHA per PR
 - `data/_auditor/verdicts/{pr}-{sha}.json` — per-run verdict record
+- `data/_kb/audit_lessons.jsonl` — one row per block/warn finding,
+  path-agnostic signature for dedup. Tailed by kb_query on each audit
+  to surface recurring patterns (2+ distinct PRs with same signature
+  → info, 3-4 → warn, 5+ → block). This is how the auditor learns.
+- `data/_kb/scrum_reviews.jsonl` — scrum-master per-file reviews. If
+  a file in the current PR has been scrum-reviewed, kb_query surfaces
+  the review as a finding with the accepted model and attempt count.
 
 ## Where YOU edit
 

auditor/audit.ts

@@ -12,7 +12,8 @@
 // review — reviews have self-review restrictions on Gitea and the
 // auditor currently uses the same PAT as the PR author).
 
-import { readFile, writeFile, mkdir } from "node:fs/promises";
+import { readFile, writeFile, mkdir, appendFile } from "node:fs/promises";
+import { createHash } from "node:crypto";
 import { join } from "node:path";
 import type { PrSnapshot, Verdict, Finding } from "./types.ts";
 import { getPrDiff, postCommitStatus, postIssueComment } from "./gitea.ts";
@@ -24,6 +25,10 @@ import { runInferenceCheck } from "./checks/inference.ts";
 import { runKbCheck } from "./checks/kb_query.ts";
 
 const VERDICTS_DIR = "/home/profit/lakehouse/data/_auditor/verdicts";
+// Playbook for audit findings — one row per block/warn finding from a
+// verdict. kb_query tails this next audit and escalates recurrences.
+// Structured as JSONL so it's cheap to append and cheap to tail.
+const AUDIT_LESSONS_JSONL = "/home/profit/lakehouse/data/_kb/audit_lessons.jsonl";
 
 export interface AuditOptions {
   // Skip the cloud inference call (fast path for iteration). Default false.
@@ -51,7 +56,7 @@ export async function auditPr(pr: PrSnapshot, opts: AuditOptions = {}): Promise<
   const [staticFindings, dynamicFindings, inferenceFindings, kbFindings] = await Promise.all([
     runStaticCheck(diff),
     opts.skip_dynamic ? Promise.resolve(stubFinding("dynamic", "skipped by options")) : runDynamicCheck(),
-    opts.skip_inference ? Promise.resolve(stubFinding("inference", "skipped by options")) : runInferenceCheck(claims, diff),
+    opts.skip_inference ? Promise.resolve(stubFinding("inference", "skipped by options")) : runInferenceCheck(claims, diff, { pr_number: pr.number, head_sha: pr.head_sha }),
     runKbCheck(claims, pr.files.map(f => f.path)),
   ]);
 
@@ -72,6 +77,7 @@ export async function auditPr(pr: PrSnapshot, opts: AuditOptions = {}): Promise<
     claims_strong: claims.filter(c => c.strength === "strong").length,
     claims_moderate: claims.filter(c => c.strength === "moderate").length,
     claims_weak: claims.filter(c => c.strength === "weak").length,
+    claims_empirical: claims.filter(c => c.strength === "empirical").length,
     claims_total: claims.length,
     diff_bytes: diff.length,
   };
@@ -80,6 +86,15 @@ export async function auditPr(pr: PrSnapshot, opts: AuditOptions = {}): Promise<
 
   await persistVerdict(verdict);
 
+  // Feedback loop — every block/warn finding becomes a row in
+  // audit_lessons.jsonl, dedup-keyed by (check, normalized-summary).
+  // The next audit's kb_query reads these and escalates recurring
+  // findings so we don't lose the "this pattern has been flagged
+  // before" signal across runs. Fire-and-forget; failure here must
+  // not break the audit.
+  appendAuditLessons(verdict).catch(e =>
+    console.error(`[audit] audit_lessons append failed: ${(e as Error).message}`));
+
   if (!opts.dry_run) {
     await postToGitea(verdict);
   }
@@ -87,6 +102,42 @@ export async function auditPr(pr: PrSnapshot, opts: AuditOptions = {}): Promise<
   return verdict;
 }
 
+// Normalizes a finding summary for dedup: strips path-specific tails
+// ("in path/to/file.ts" → "in <file>"), line numbers, and long
+// commit-hash snippets. The goal is: the SAME class of finding on
+// DIFFERENT files should share a signature, so we can measure
+// "this pattern keeps showing up."
+function normalizedSignature(f: Finding): string {
+  const summary = String(f.summary)
+    .replace(/\bin\s+\S+\.(ts|rs|js|py|md)\b/gi, "in <file>")
+    .replace(/:\+?\d+\b/g, ":<line>")
+    .replace(/[0-9a-f]{8,}/gi, "<hash>")
+    .replace(/\s+/g, " ")
+    .trim()
+    .slice(0, 240);
+  const src = `${f.check}::${f.severity}::${summary}`;
+  return createHash("sha256").update(src).digest("hex").slice(0, 16);
+}
+
+async function appendAuditLessons(v: Verdict): Promise<void> {
+  const actionable = v.findings.filter(f => f.severity === "block" || f.severity === "warn");
+  if (actionable.length === 0) return;
+  await mkdir(join(AUDIT_LESSONS_JSONL, ".."), { recursive: true });
+  const rows: string[] = [];
+  for (const f of actionable) {
+    rows.push(JSON.stringify({
+      signature: normalizedSignature(f),
+      check: f.check,
+      severity: f.severity,
+      summary: f.summary,
+      pr_number: v.pr_number,
+      head_sha: v.head_sha,
+      audited_at: v.audited_at,
+    }));
+  }
+  await appendFile(AUDIT_LESSONS_JSONL, rows.join("\n") + "\n");
+}
+
 async function persistVerdict(v: Verdict): Promise<void> {
   await mkdir(VERDICTS_DIR, { recursive: true });
   const filename = `${v.pr_number}-${v.head_sha.slice(0, 12)}.json`;

auditor/checks/inference.ts

@@ -13,17 +13,55 @@
 // with a 15KB diff + claim list).
 
 import type { Claim, Finding } from "../types.ts";
+import { Glob } from "bun";
+import { readFile, mkdir, appendFile } from "node:fs/promises";
+import { extractFacts } from "../fact_extractor.ts";
 
 const GATEWAY = process.env.LH_GATEWAY_URL ?? "http://localhost:3100";
 const MODEL = process.env.LH_AUDITOR_REVIEW_MODEL ?? "gpt-oss:120b";
+// Tie-breaker for claims where the N=3 consensus produces a 1-1-1
+// split (genuinely borderline). Different architecture from the
+// primary reviewer (gpt-oss) so the tie-break isn't correlated with
+// the original disagreement. qwen3-coder:480b is a newer coding
+// specialist at 480B params, well-suited to PR-diff claim verification
+// and distinct in training lineage from gpt-oss.
+const TIEBREAKER_MODEL = process.env.LH_AUDITOR_TIEBREAKER_MODEL ?? "qwen3-coder:480b";
+const N_CONSENSUS = Number(process.env.LH_AUDITOR_CONSENSUS_N ?? 3);
+const AUDIT_DISCREPANCIES_JSONL = "/home/profit/lakehouse/data/_kb/audit_discrepancies.jsonl";
 // 40KB comfortably fits gpt-oss:120b's context. PR #1 (~39KB) was
 // previously truncated at 15KB causing the reviewer to miss later
 // files (gitea.ts, policy.ts) and flag "no Gitea client present" as a
 // block finding when the file was simply outside the truncation window.
+//
+// Above this threshold we curate via tree-split rather than truncate,
+// following the scrum_master pattern: shard the diff, summarize each
+// shard against the claim-verification task, merge into a compact
+// scratchpad, then ask the cloud to verify claims against the
+// scratchpad. This gives the cloud full-PR fidelity without bursting
+// its context window (observed failure mode: empty response or
+// unparseable output when prompt exceeds model's comfortable range).
 const MAX_DIFF_CHARS = 40000;
+// Tree-split kicks in above this. 30KB is below MAX_DIFF_CHARS so we
+// curate BEFORE truncation would happen — never lose signal to a hard
+// cut. Shard size is chosen so ~10 shards cover PR #8-size diffs in a
+// reasonable round-trip budget.
+const CURATION_THRESHOLD = 30000;
+const DIFF_SHARD_SIZE = 4500;
 const CALL_TIMEOUT_MS = 120_000;
+const REPO_ROOT = "/home/profit/lakehouse";
 
-export async function runInferenceCheck(claims: Claim[], diff: string): Promise<Finding[]> {
+export interface InferenceContext {
+  pr_number: number;
+  head_sha: string;
+}
+
+const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
+
+export async function runInferenceCheck(
+  claims: Claim[],
+  diff: string,
+  ctx?: InferenceContext,
+): Promise<Finding[]> {
   if (claims.length === 0) {
     return [{
       check: "inference",
@@ -33,9 +71,41 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
     }];
   }
 
-  const truncated = diff.length > MAX_DIFF_CHARS
-    ? diff.slice(0, MAX_DIFF_CHARS) + `\n...[${diff.length - MAX_DIFF_CHARS} more chars truncated]`
-    : diff;
+  // Empirical claims (runtime metrics / observed outcomes) can't be
+  // verified from the diff. Drop them from the cloud prompt so the
+  // reviewer doesn't chase ghosts. A future `runtime_evidence` check
+  // can validate these against data/_kb/*/summary.json outputs.
+  const verifiable = claims.filter(c => c.strength !== "empirical");
+  const empiricalCount = claims.length - verifiable.length;
+  if (verifiable.length === 0) {
+    return [{
+      check: "inference",
+      severity: "info",
+      summary: `all ${claims.length} claims are empirical (runtime metrics) — skipping cloud inference`,
+      evidence: [`empirical claims can't be verified from a static diff; needs runtime-evidence check`],
+    }];
+  }
+
+  // Diff source for the cloud prompt — either the raw diff (small
+  // enough to fit), or a tree-split scratchpad (curation layer). We
+  // prefer curation to truncation: truncation silently drops files
+  // past the window; curation summarizes them so the cloud still sees
+  // what changed, just densified.
+  let diffForPrompt: string;
+  let curationNote = "";
+  if (diff.length > CURATION_THRESHOLD) {
+    const ts = await treeSplitDiff(diff, verifiable);
+    diffForPrompt = ts.scratchpad;
+    curationNote = ` (curated: ${diff.length} chars → ${ts.shards} shards → scratchpad ${ts.scratchpad.length} chars)`;
+  } else {
+    diffForPrompt = diff;
+  }
+  // Belt-and-suspenders truncation — even a tree-split scratchpad
+  // shouldn't exceed MAX_DIFF_CHARS in practice, but guard anyway so
+  // pathological inputs can't burst the prompt.
+  const truncated = diffForPrompt.length > MAX_DIFF_CHARS
+    ? diffForPrompt.slice(0, MAX_DIFF_CHARS) + `\n...[${diffForPrompt.length - MAX_DIFF_CHARS} more chars truncated]`
+    : diffForPrompt;
 
   // Build the reviewer prompt in the same shape as run_codereview's
   // review stage (llm_team_ui.py:10950), adapted for claim verification:
@@ -43,6 +113,30 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
   //   "Code: ..."
   //   "Review: bugs/security/perf/style/edge. Provide corrected code."
   // We add: claim list upfront + ask for structured JSON verdict.
+  //
+  // When the diff was curated (tree-split scratchpad), we add an
+  // explicit anti-false-positive instruction: the scratchpad is a
+  // distillation, not the full source, so absence-from-scratchpad is
+  // NOT evidence of absence-from-diff. Mirrors the fix we made in
+  // scrum_master's review prompt for the same class of error.
+  const isCurated = curationNote.length > 0;
+  const curationGuard = isCurated
+    ? [
+        "",
+        "CRITICAL: the 'Diff' below is a curated multi-shard scratchpad,",
+        "NOT the full raw diff. The scratchpad distills each shard down",
+        "to facts useful for claim verification and drops the rest.",
+        "DO NOT flag a function/field/feature as 'missing' or 'not",
+        "implemented' based solely on its absence from the scratchpad —",
+        "absence in a distillation is NOT evidence of absence in the",
+        "actual diff. Only judge a claim NOT BACKED when the scratchpad",
+        "DIRECTLY contradicts it (e.g. scratchpad shows the function was",
+        "added empty, or shows the claimed code path is a stub).",
+        "Skip the unflagged_gaps section entirely when operating on a",
+        "curated scratchpad — you can't reliably detect gaps from a",
+        "distillation, and false positives there are worse than misses.",
+      ].join("\n")
+    : "";
   const systemMsg = [
     "You review pull-request diffs against the author's own ship-claims.",
     "For each claim, decide: is it backed by actual code in the diff, or is",
@@ -56,6 +150,7 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
     "  - the claim claims integration but the integration point is a stub",
     "  - the diff contains unimplemented!() / todo!() / TODO comments",
     "  - the claim says 'works end-to-end' but the diff has no end-to-end test",
+    curationGuard,
     "",
     "Respond with strict JSON only. No prose before or after. Shape:",
     "{",
@@ -70,7 +165,7 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
 
   const userMsg = [
     `Ship-claims the author made (numbered 0..N-1):`,
-    claims.map((c, i) => `  ${i}. [${c.strength}] "${c.text}" at ${c.location}`).join("\n"),
+    verifiable.map((c, i) => `  ${i}. [${c.strength}] "${c.text}" at ${c.location}`).join("\n"),
     "",
     `Diff:`,
     "```",
@@ -82,79 +177,131 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
     `Strict JSON only, matching the shape described. No prose outside JSON.`,
   ].join("\n");
 
-  let resp: Response;
-  try {
-    resp = await fetch(`${GATEWAY}/v1/chat`, {
-      method: "POST",
-      headers: { "content-type": "application/json" },
-      body: JSON.stringify({
-        provider: "ollama_cloud",
-        model: MODEL,
-        messages: [
-          { role: "system", content: systemMsg },
-          { role: "user", content: userMsg },
-        ],
-        max_tokens: 3000,
-        temperature: 0.2,
-        think: true, // T3 overseer should reason — JSON shape is still required
-      }),
-      signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
-    });
-  } catch (e) {
-    // Cloud unreachable → soft-fail. Don't block a PR because the
-    // reviewer model is down. Static + dynamic + kb still run.
+  // N=3 consensus — run the primary reviewer in parallel, collect
+  // all three parsed responses, majority-vote per claim. Parallel
+  // (Promise.all) because each call is ~20-30s and they're independent;
+  // wall-clock stays ~same as single call, cost 3x tokens. Empirical
+  // justification: in 3-run determinism tests, 7/8 findings were
+  // stable but 1 flipped across runs — majority vote stabilizes the
+  // flipping class without losing the stable signal.
+  const primaryRuns = await Promise.all(
+    Array.from({ length: N_CONSENSUS }, () =>
+      runCloudInference(systemMsg, userMsg, MODEL)),
+  );
+
+  const parsedRuns = primaryRuns.filter(r => r.parsed !== null);
+  if (parsedRuns.length === 0) {
+    // All N calls failed. Surface the first-run diagnostic so the
+    // operator sees *why* (unreachable / non-200 / unparseable).
+    const first = primaryRuns[0];
     return [{
       check: "inference",
       severity: "info",
-      summary: "cloud inference unreachable — skipped",
-      evidence: [`fetch failed: ${(e as Error).message.slice(0, 180)}`],
-    }];
-  }
-
-  if (!resp.ok) {
-    return [{
-      check: "inference",
-      severity: "info",
-      summary: `cloud inference returned ${resp.status} — skipped`,
-      evidence: [`body: ${(await resp.text()).slice(0, 200)}`],
-    }];
-  }
-
-  const body: any = await resp.json();
-  const content: string = body?.choices?.[0]?.message?.content ?? "";
-  const usage = body?.usage ?? {};
-
-  const parsed = extractJson(content);
-  if (!parsed) {
-    return [{
-      check: "inference",
-      severity: "info",
-      summary: "cloud returned unparseable output — skipped",
+      summary: `cloud inference all ${N_CONSENSUS} consensus runs failed — ${first.error ?? "unknown"}`,
       evidence: [
-        `head: ${content.slice(0, 200)}`,
-        `tokens: ${usage.total_tokens ?? "?"}`,
+        `first-run diagnostic: ${first.diagnostic ?? "(none)"}`,
+        `successful runs: 0 / ${N_CONSENSUS}`,
       ],
     }];
   }
 
+  // Aggregate votes per claim_idx.
+  interface Votes { trues: number; falses: number; evidences: string[] }
+  const votesByClaim = new Map<number, Votes>();
+  const unflaggedByRun: any[][] = [];
+  let totalTokens = 0;
+  for (const run of parsedRuns) {
+    totalTokens += run.tokens;
+    unflaggedByRun.push(Array.isArray(run.parsed?.unflagged_gaps) ? run.parsed.unflagged_gaps : []);
+    for (const v of run.parsed?.claim_verdicts ?? []) {
+      const idx = Number(v?.claim_idx);
+      if (!Number.isFinite(idx)) continue;
+      const rec = votesByClaim.get(idx) ?? { trues: 0, falses: 0, evidences: [] };
+      if (v.backed === false) {
+        rec.falses++;
+        rec.evidences.push(String(v.evidence ?? ""));
+      } else if (v.backed === true) {
+        rec.trues++;
+      }
+      votesByClaim.set(idx, rec);
+    }
+  }
+
   const findings: Finding[] = [];
 
-  // One summary info finding so the verdict layer knows the check ran.
+  // Summary finding so the verdict layer knows the check ran.
   findings.push({
     check: "inference",
     severity: "info",
-    summary: `cloud review completed (model=${MODEL}, tokens=${usage.total_tokens ?? "?"})`,
+    summary: `cloud review completed (model=${MODEL}, consensus=${parsedRuns.length}/${N_CONSENSUS}, tokens=${totalTokens})${curationNote}`,
     evidence: [
-      `claim_verdicts: ${parsed.claim_verdicts?.length ?? 0}, unflagged_gaps: ${parsed.unflagged_gaps?.length ?? 0}`,
+      `claims voted: ${votesByClaim.size}`,
+      `parsed runs: ${parsedRuns.length} / ${N_CONSENSUS}`,
     ],
   });
 
-  for (const v of parsed.claim_verdicts ?? []) {
-    if (v?.backed === false) {
-      const idx = typeof v.claim_idx === "number" ? v.claim_idx : -1;
-      const claim = claims[idx];
-      if (!claim) continue;
-      // Strong+unbacked = BLOCK. That's the whole point of the auditor.
+  // Per-claim majority vote; tie-break if no majority.
+  const discrepancies: Array<{
+    claim_idx: number;
+    claim_text: string;
+    votes: { trues: number; falses: number };
+    resolution: "majority_backed" | "majority_not_backed" | "tiebreaker_backed" | "tiebreaker_not_backed" | "unresolved";
+    tiebreaker_model?: string;
+  }> = [];
+
+  for (const [idx, votes] of votesByClaim) {
+    const claim = verifiable[idx];
+    if (!claim) continue;
+    const totalVotes = votes.trues + votes.falses;
+    let notBacked: boolean | null = null;
+    let resolution: typeof discrepancies[number]["resolution"] = "majority_backed";
+    let evidenceText = "";
+    let tbModel: string | undefined;
+
+    if (votes.falses > votes.trues) {
+      notBacked = true;
+      resolution = "majority_not_backed";
+      evidenceText = votes.evidences[0] ?? "(no reason given)";
+    } else if (votes.trues > votes.falses) {
+      notBacked = false;
+      resolution = "majority_backed";
+    } else {
+      // Tie. Run tie-breaker with a different-architecture model.
+      const tb = await runCloudInference(systemMsg, userMsg, TIEBREAKER_MODEL);
+      if (tb.parsed) {
+        const tv = (tb.parsed.claim_verdicts ?? []).find((v: any) => Number(v?.claim_idx) === idx);
+        if (tv?.backed === false) {
+          notBacked = true;
+          resolution = "tiebreaker_not_backed";
+          evidenceText = `(tie-breaker ${TIEBREAKER_MODEL}) ${String(tv.evidence ?? "")}`;
+          tbModel = TIEBREAKER_MODEL;
+        } else if (tv?.backed === true) {
+          notBacked = false;
+          resolution = "tiebreaker_backed";
+          tbModel = TIEBREAKER_MODEL;
+        } else {
+          resolution = "unresolved";
+        }
+      } else {
+        resolution = "unresolved";
+      }
+    }
+
+    // Log every case where the N runs disagreed — discrepancies are
+    // signal, not noise. Separate from audit_lessons.jsonl because
+    // they're about the *auditor's* quality, not the PR's quality.
+    const disagreed = totalVotes >= 2 && votes.trues > 0 && votes.falses > 0;
+    if (disagreed || resolution.startsWith("tiebreaker") || resolution === "unresolved") {
+      discrepancies.push({
+        claim_idx: idx,
+        claim_text: claim.text,
+        votes: { trues: votes.trues, falses: votes.falses },
+        resolution,
+        tiebreaker_model: tbModel,
+      });
+    }
+
+    if (notBacked === true) {
       const sev: Finding["severity"] = claim.strength === "strong" ? "block"
         : claim.strength === "moderate" ? "warn"
         : "info";
@@ -165,24 +312,337 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
         summary: `cloud: claim not backed — "${claim.text.slice(0, 100)}"`,
         evidence: [
           `at ${claim.location}`,
-          `cloud reason: ${String(v.evidence ?? "no reason given").slice(0, 200)}`,
+          `consensus: ${votes.falses}/${totalVotes} not-backed (resolution: ${resolution})`,
+          `cloud reason: ${evidenceText.slice(0, 200)}`,
         ],
       });
     }
   }
 
-  for (const g of parsed.unflagged_gaps ?? []) {
+  // Persist discrepancies so we can measure consensus drift over time.
+  if (discrepancies.length > 0 && ctx) {
+    persistDiscrepancies(ctx, discrepancies).catch(e =>
+      console.error(`[inference] discrepancy log failed: ${(e as Error).message}`));
+  }
+
+  // Use first run's parsed for downstream unflagged_gaps processing.
+  const parsed = parsedRuns[0].parsed;
+
+  // Route the curated scratchpad through llm_team's extract-facts
+  // pipeline when we have (a) a curated scratchpad (best signal about
+  // what the PR actually changed) and (b) PR context to scope facts.
+  // AWAITED (not fire-and-forget) so CLI callers like audit_one.ts
+  // don't exit before extraction lands; the systemd poller has plenty
+  // of headroom (90s cycle vs ~15s extraction). A failure inside
+  // extractAndPersistFacts is caught + logged but never throws.
+  if (isCurated && ctx && process.env.LH_AUDITOR_SKIP_EXTRACT !== "1") {
+    try {
+      await extractAndPersistFacts(diffForPrompt, ctx);
+    } catch (e) {
+      console.error(`[inference] fact extraction failed: ${(e as Error).message}`);
+    }
+  }
+
+  // Belt-and-suspenders: when operating on a curated scratchpad, drop
+  // the unflagged_gaps section entirely. The distillation can't
+  // reliably ground gap-detection, and false positives are worse than
+  // misses for this signal class. The systemMsg already asks the
+  // cloud to skip this section when curated — but the model may still
+  // emit it, so we filter here too.
+  const gapsToEmit = isCurated ? [] : (parsed.unflagged_gaps ?? []);
+  for (const g of gapsToEmit) {
+    const summary = String(g?.summary ?? "?");
+    const location = String(g?.location ?? "?");
+    // False-positive guard — when the cloud says "X not defined in this
+    // diff" or "missing implementation of X", the cloud may just mean
+    // "X is not in the added lines," not "X doesn't exist in the repo."
+    // Extract candidate symbol names and grep the repo. If any symbol
+    // is defined elsewhere, drop the finding — it's a known-symbol
+    // reference, not a placeholder.
+    if (/not\s+defined|missing\s+implementation|never\s+referenced\s+or\s+integrated/i.test(summary)) {
+      const symbols = extractSymbols(summary);
+      if (symbols.length > 0) {
+        const resolved = await symbolsExistInRepo(symbols);
+        if (resolved.length === symbols.length) {
+          // Every named symbol exists somewhere in the repo — silent drop.
+          continue;
+        }
+        if (resolved.length > 0) {
+          // Partially resolved — demote to info with a note.
+          findings.push({
+            check: "inference",
+            severity: "info",
+            summary: `cloud gap partially resolved by repo grep: ${summary.slice(0, 120)}`,
+            evidence: [
+              `location: ${location.slice(0, 140)}`,
+              `resolved via grep: ${resolved.join(",")}`,
+              `unresolved: ${symbols.filter(s => !resolved.includes(s)).join(",")}`,
+            ],
+          });
+          continue;
+        }
+      }
+    }
     findings.push({
       check: "inference",
       severity: "warn",
-      summary: `cloud-flagged gap not in any claim: ${String(g?.summary ?? "?").slice(0, 120)}`,
-      evidence: [`location: ${String(g?.location ?? "?").slice(0, 140)}`],
+      summary: `cloud-flagged gap not in any claim: ${summary.slice(0, 120)}`,
+      evidence: [`location: ${location.slice(0, 140)}`],
     });
   }
 
   return findings;
 }
 
+// Single cloud call — the consensus loop calls this N times in
+// parallel. Returns the parsed JSON shape + token usage + any error
+// diagnostic. NEVER throws; the consensus aggregator handles partial
+// failures by dropping non-parsed runs from the vote.
+interface CloudRunResult {
+  parsed: any | null;
+  tokens: number;
+  error?: string;       // "unreachable" | "non_200" | "unparseable"
+  diagnostic?: string;  // first 200 chars for debugging
+  model: string;
+}
+
+async function runCloudInference(systemMsg: string, userMsg: string, model: string): Promise<CloudRunResult> {
+  let resp: Response;
+  try {
+    resp = await fetch(`${GATEWAY}/v1/chat`, {
+      method: "POST",
+      headers: { "content-type": "application/json" },
+      body: JSON.stringify({
+        provider: "ollama_cloud",
+        model,
+        messages: [
+          { role: "system", content: systemMsg },
+          { role: "user", content: userMsg },
+        ],
+        // temp=0 (greedy) + think=true. think=true is required for
+        // gpt-oss:120b — without it the model returns empty content
+        // on large prompts. Variance from the think trace is observed
+        // in practice, which is why we use N=3 consensus, not single-
+        // call determinism.
+        max_tokens: 3000,
+        temperature: 0,
+        think: true,
+      }),
+      signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
+    });
+  } catch (e) {
+    return { parsed: null, tokens: 0, error: "unreachable", diagnostic: (e as Error).message.slice(0, 200), model };
+  }
+  if (!resp.ok) {
+    return { parsed: null, tokens: 0, error: "non_200", diagnostic: `${resp.status}: ${(await resp.text()).slice(0, 160)}`, model };
+  }
+  let body: any;
+  try { body = await resp.json(); }
+  catch (e) { return { parsed: null, tokens: 0, error: "unparseable", diagnostic: (e as Error).message, model }; }
+  const content: string = body?.choices?.[0]?.message?.content ?? "";
+  const tokens: number = body?.usage?.total_tokens ?? 0;
+  const parsed = extractJson(content);
+  if (!parsed) {
+    return { parsed: null, tokens, error: "unparseable", diagnostic: content.slice(0, 200), model };
+  }
+  return { parsed, tokens, model };
+}
+
+async function persistDiscrepancies(ctx: InferenceContext, discrepancies: any[]): Promise<void> {
+  await mkdir("/home/profit/lakehouse/data/_kb", { recursive: true });
+  const rows = discrepancies.map(d => JSON.stringify({
+    pr_number: ctx.pr_number,
+    head_sha: ctx.head_sha,
+    logged_at: new Date().toISOString(),
+    ...d,
+  }));
+  await appendFile(AUDIT_DISCREPANCIES_JSONL, rows.join("\n") + "\n");
+}
+
+// Extract structured knowledge from the curated scratchpad and append
+// to data/_kb/audit_facts.jsonl — one row per extract run, keyed by
+// PR number + head SHA for scope tracking. kb_query tails this next
+// audit to surface recurring entities/relationships across PRs.
+async function extractAndPersistFacts(scratchpad: string, ctx: InferenceContext): Promise<void> {
+  const ex = await extractFacts(scratchpad);
+  if (ex.error && ex.entities.length === 0 && ex.facts.length === 0) {
+    // Full failure — log but don't write an empty row.
+    console.error(`[inference] extractFacts skipped row: ${ex.error}`);
+    return;
+  }
+  const row = {
+    pr_number: ctx.pr_number,
+    head_sha: ctx.head_sha,
+    extracted_at: ex.extracted_at,
+    extractor: ex.extractor_model,
+    verifier: ex.verifier_model,
+    llm_team_run_id: ex.llm_team_run_id ?? null,
+    facts: ex.facts,
+    entities: ex.entities,
+    relationships: ex.relationships,
+    verification_preview: ex.verification.slice(0, 400),
+    verifier_verdicts: ex.verifier_verdicts,
+    facts_dropped_by_verifier: ex.facts_dropped_by_verifier ?? 0,
+    schema_version: 2,
+    source: "audit_inference",
+  };
+  await mkdir("/home/profit/lakehouse/data/_kb", { recursive: true });
+  await appendFile(AUDIT_FACTS_JSONL, JSON.stringify(row) + "\n");
+}
+
+// Curation via tree-split — ports the scrum_master pattern into the
+// inference check. Shards the raw diff into DIFF_SHARD_SIZE chunks,
+// summarizes each shard *against the claim-verification task* so the
+// summary preserves exactly what the cloud needs to judge claims
+// (function signatures, struct fields, deletions, new files), drops
+// everything else. Merges into a compact scratchpad.
+//
+// Cost: N cloud calls for the shard summaries + 1 cloud call for the
+// final verification = N+1 calls instead of 1. Mitigation: shards run
+// serially (not parallel) to keep gateway load bounded; summary calls
+// use max_tokens=400 so they're fast (~2s each on gpt-oss:120b).
+//
+// Determinism: each shard summary call uses temp=0 + think=true (same
+// as the top-level inference call), so identical input yields
+// identical scratchpad. The final verification call then sees a
+// stable scratchpad, giving stable verdicts.
+async function treeSplitDiff(
+  fullDiff: string,
+  claims: Claim[],
+): Promise<{ scratchpad: string; shards: number }> {
+  const shards: Array<{ from: number; to: number; text: string }> = [];
+  for (let i = 0; i < fullDiff.length; i += DIFF_SHARD_SIZE) {
+    const end = Math.min(i + DIFF_SHARD_SIZE, fullDiff.length);
+    shards.push({ from: i, to: end, text: fullDiff.slice(i, end) });
+  }
+  // Curate the claim list into a short form the summary prompt can
+  // use to bias extraction toward relevant facts.
+  const claimDigest = claims.map((c, i) =>
+    `${i}. [${c.strength}] "${c.text.slice(0, 100)}"`
+  ).join("\n");
+
+  let scratchpad = "";
+  for (const [si, shard] of shards.entries()) {
+    const prompt = [
+      `You are summarizing shard ${si + 1}/${shards.length} (chars ${shard.from}..${shard.to}) of a PR diff.`,
+      `The downstream task will verify these ship-claims against the full-PR summary. Extract ONLY facts that could confirm or refute these claims:`,
+      "",
+      claimDigest,
+      "",
+      "Extract: new function/method signatures, struct fields, deletions, new files, wiring (function X calls Y), absence-of-implementation markers, TODO comments on added lines.",
+      "Skip: comment-only edits, whitespace, import reordering, unrelated cosmetic changes.",
+      "",
+      "─────── shard diff ───────",
+      shard.text,
+      "─────── end shard ───────",
+      "",
+      "Output: up to 180 words of facts in bullet form. No prose preamble, no claim verdicts (that's for the downstream step).",
+    ].join("\n");
+
+    const r = await callCloud(prompt, 400);
+    if (r.content) {
+      scratchpad += `\n--- shard ${si + 1} (chars ${shard.from}..${shard.to}) ---\n${r.content.trim()}\n`;
+    }
+  }
+  return { scratchpad: scratchpad.trim(), shards: shards.length };
+}
+
+// Minimal cloud caller used only by treeSplitDiff — same gateway +
+// model as the top-level call, but think=false. Shards are small
+// (≤DIFF_SHARD_SIZE ~4500 chars) and the task is pure fact
+// extraction, not reasoning. think=true on the shards introduced
+// variance in reasoning traces that compounded across 23 calls into
+// a non-deterministic scratchpad (observed during curation
+// validation: same-SHA runs produced 5/7/8 final findings).
+// think=false on small prompts is stable — only breaks at the main
+// call's 10K+ prompt size, which keeps think=true.
+async function callCloud(prompt: string, maxTokens: number): Promise<{ content: string }> {
+  try {
+    const r = await fetch(`${GATEWAY}/v1/chat`, {
+      method: "POST",
+      headers: { "content-type": "application/json" },
+      body: JSON.stringify({
+        provider: "ollama_cloud",
+        model: MODEL,
+        messages: [{ role: "user", content: prompt }],
+        max_tokens: maxTokens,
+        temperature: 0,
+        think: false,
+      }),
+      signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
+    });
+    if (!r.ok) return { content: "" };
+    const j: any = await r.json();
+    return { content: j?.choices?.[0]?.message?.content ?? "" };
+  } catch {
+    return { content: "" };
+  }
+}
+
+// Pull out plausible code-symbol names from a summary string.
+// Matches:
+//   - identifier with backticks: `foo_bar`
+//   - identifier followed by parens: foo_bar()
+//   - CamelCase types
+//   - snake_case_functions
+// Filters out common English words that could be matched accidentally.
+const STOPWORDS = new Set([
+  "not","the","and","for","this","that","with","but","are","was","has",
+  "have","been","any","missing","implementation","diff","defined","never",
+  "referenced","integrated","flow","code","file","some","only","when",
+]);
+function extractSymbols(text: string): string[] {
+  const out = new Set<string>();
+  // `backticked` symbols
+  for (const m of text.matchAll(/`([A-Za-z_][A-Za-z0-9_]{2,})`/g)) out.add(m[1]);
+  // foo() or foo_bar() calls
+  for (const m of text.matchAll(/\b([A-Za-z_][A-Za-z0-9_]{2,})\s*\(/g)) out.add(m[1]);
+  // CamelCase types (3+ chars, must start with uppercase)
+  for (const m of text.matchAll(/\b([A-Z][A-Za-z0-9]{2,})\b/g)) out.add(m[1]);
+  return Array.from(out).filter(s => !STOPWORDS.has(s.toLowerCase()));
+}
+
+// Scan the repo for at least one definition of each symbol. Uses Bun's
+// Glob to walk TS/Rust/Python/JS sources; ignores node_modules, data/,
+// and target/. Skips files > 500KB — those are fixtures/snapshots that
+// won't contain a definition line and slurping them slows the audit.
+async function symbolsExistInRepo(symbols: string[]): Promise<string[]> {
+  const patterns = ["**/*.ts", "**/*.tsx", "**/*.rs", "**/*.py", "**/*.js"];
+  const skip = (p: string) => p.includes("/node_modules/") || p.startsWith("data/") || p.includes("/target/") || p.startsWith("dist/");
+  const MAX_FILE_BYTES = 500_000;
+  const { stat } = await import("node:fs/promises");
+  const resolved = new Set<string>();
+  const toFind = new Set(symbols);
+  for (const pat of patterns) {
+    if (toFind.size === 0) break;
+    const glob = new Glob(pat);
+    for await (const f of glob.scan({ cwd: REPO_ROOT, onlyFiles: true })) {
+      if (skip(f)) continue;
+      try { const s = await stat(`${REPO_ROOT}/${f}`); if (s.size > MAX_FILE_BYTES) continue; } catch { continue; }
+      let content: string;
+      try { content = await readFile(`${REPO_ROOT}/${f}`, "utf8"); } catch { continue; }
+      for (const sym of Array.from(toFind)) {
+        // Definition heuristics: `function sym`, `fn sym`, `const sym`,
+        // `let sym`, `def sym`, `class sym`, `struct sym`, `enum sym`,
+        // `trait sym`, `async function sym`, `pub (async )?fn sym`.
+        const re = new RegExp(
+          `\\b(function|async\\s+function|const|let|var|def|class|struct|enum|trait|impl|type|interface|fn|pub\\s+(async\\s+)?fn)\\s+${escapeRe(sym)}\\b`
+        );
+        if (re.test(content)) {
+          resolved.add(sym);
+          toFind.delete(sym);
+          if (toFind.size === 0) break;
+        }
+      }
+    }
+  }
+  return Array.from(resolved);
+}
+
+function escapeRe(s: string): string {
+  return s.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
+}
+
 // Lift the first balanced JSON object out of the response. Tolerates
 // leading prose, code fences, and model reasoning preamble when the
 // cloud model ignored "strict JSON only."

auditor/checks/kb_query.ts

@@ -18,11 +18,14 @@
 import { readFile, readdir, stat } from "node:fs/promises";
 import { join } from "node:path";
 import type { Claim, Finding } from "../types.ts";
+import { aggregate, ratingSeverity, formatAgg } from "../kb_index.ts";
 
 const KB_DIR = "/home/profit/lakehouse/data/_kb";
 const OBSERVER_OPS = "/home/profit/lakehouse/data/_observer/ops.jsonl";
 const BOT_CYCLES_DIR = "/home/profit/lakehouse/data/_bot/cycles";
 const SCRUM_REVIEWS_JSONL = "/home/profit/lakehouse/data/_kb/scrum_reviews.jsonl";
+const AUDIT_LESSONS_JSONL = "/home/profit/lakehouse/data/_kb/audit_lessons.jsonl";
+const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
 const TAIL_LINES = 500;
 const MAX_BOT_CYCLE_FILES = 30;
 
@@ -59,6 +62,26 @@ export async function runKbCheck(claims: Claim[], prFiles: string[] = []): Promi
     findings.push(...scrumFindings);
   }
 
+  // 6b. Audit-facts (llm_team extract pipeline output) — surface
+  //     entities that recur across multiple PRs. These are the
+  //     "core system entities" accumulating in the knowledge base;
+  //     showing them as info on future audits gives reviewers
+  //     architectural context the raw diff doesn't convey.
+  const factFindings = await checkAuditFacts();
+  findings.push(...factFindings);
+
+  // 6. Audit-lessons feedback loop — summarize the top recurring
+  //    patterns from prior audits' block/warn findings. If the same
+  //    pattern signature has fired 3+ times across prior audits,
+  //    emit it as a block-severity finding so reviewers know this
+  //    is a known-recurring class, not a one-off. Does NOT couple
+  //    to the current audit's static/inference findings (those run
+  //    in parallel and we can't see them here) — the amplification
+  //    is emergent: if the current audit's finding-summary matches
+  //    a top recurrence, the reviewer sees both.
+  const auditLessonFindings = await checkAuditLessons();
+  findings.push(...auditLessonFindings);
+
   return findings;
 }
 
@@ -193,6 +216,135 @@ function observerBySource(ops: any[]): string {
   return Object.entries(c).sort((a, b) => b[1] - a[1]).map(([k, v]) => `${k}=${v}`).join(", ") || "empty";
 }
 
+// Audit-facts — reads data/_kb/audit_facts.jsonl (populated by every
+// curated inference run via llm_team's extract pipeline). Each row
+// has arrays: facts, entities, relationships. We explode entities and
+// aggregate them across PRs using kb_index. An entity seen in 3+ PRs
+// is a "core system entity" — we surface the top N as info context.
+//
+// Filters out short names (<3 chars, likely qwen2.5 truncation
+// artifacts) and generic types ("string", "number") that would
+// otherwise dominate the ranking.
+const ENTITY_NAME_MIN_LEN = 3;
+const GENERIC_ENTITY_NAMES = new Set([
+  "string", "number", "boolean", "any", "void", "unknown", "never",
+  "object", "array", "function", "const", "let", "var", "true", "false",
+  "null", "undefined", "promise", "map", "set", "record",
+]);
+
+async function checkAuditFacts(): Promise<Finding[]> {
+  // Read raw rows — each row has multiple entities, so we can't just
+  // use aggregate() directly (it's one-signature-per-row). Explode
+  // entities into (row, entity) pairs, then aggregate by entity name.
+  let raw: string;
+  try { raw = await (await import("node:fs/promises")).readFile(AUDIT_FACTS_JSONL, "utf8"); }
+  catch { return []; }
+  const lines = raw.split("\n").filter(l => l.length > 0);
+  if (lines.length === 0) return [];
+
+  interface EntityRow { entity_key: string; pr_number: number; type: string; name: string; description: string }
+  const entityRows: EntityRow[] = [];
+  for (const line of lines.slice(-TAIL_LINES * 2)) {
+    let row: any;
+    try { row = JSON.parse(line); } catch { continue; }
+    const prNum = Number(row?.pr_number);
+    if (!Number.isFinite(prNum)) continue;
+    for (const e of Array.isArray(row?.entities) ? row.entities : []) {
+      const name = String(e?.name ?? "").trim();
+      if (name.length < ENTITY_NAME_MIN_LEN) continue;
+      if (GENERIC_ENTITY_NAMES.has(name.toLowerCase())) continue;
+      entityRows.push({
+        entity_key: name.toLowerCase(),
+        pr_number: prNum,
+        type: String(e?.type ?? "?"),
+        name,
+        description: String(e?.description ?? "").slice(0, 160),
+      });
+    }
+  }
+  if (entityRows.length === 0) return [];
+
+  // Aggregate manually — one key per entity name, distinct_scopes by PR.
+  type Agg = { count: number; scopes: Set<number>; types: Set<string>; last_name: string; last_desc: string };
+  const byEntity = new Map<string, Agg>();
+  for (const r of entityRows) {
+    const a = byEntity.get(r.entity_key) ?? {
+      count: 0, scopes: new Set<number>(), types: new Set<string>(), last_name: "", last_desc: "",
+    };
+    a.count += 1;
+    a.scopes.add(r.pr_number);
+    a.types.add(r.type);
+    a.last_name = r.name;
+    a.last_desc = r.description;
+    byEntity.set(r.entity_key, a);
+  }
+
+  // Rank: require 2+ distinct PRs (same-PR entity-repeats don't count
+  // as "cross-cutting"). Take the top 5 to avoid flooding the verdict.
+  const ranked = Array.from(byEntity.entries())
+    .filter(([_, a]) => a.scopes.size >= 2)
+    .sort((a, b) => b[1].scopes.size - a[1].scopes.size || b[1].count - a[1].count)
+    .slice(0, 5);
+
+  if (ranked.length === 0) {
+    // Useful to know the KB is being populated — emit a single
+    // summary so operators see fact extraction is alive.
+    return [{
+      check: "kb_query",
+      severity: "info",
+      summary: `audit_facts KB has ${entityRows.length} entity-observations across ${new Set(entityRows.map(r => r.pr_number)).size} PRs (no cross-PR recurrences yet)`,
+      evidence: [`source: ${AUDIT_FACTS_JSONL}`],
+    }];
+  }
+
+  return ranked.map(([_, a]) => ({
+    check: "kb_query" as const,
+    severity: "info" as const,
+    summary: `core entity \`${a.last_name}\` recurs in ${a.scopes.size} PRs (types: ${Array.from(a.types).join(",")})`,
+    evidence: [
+      `count=${a.count} distinct_PRs=${a.scopes.size}`,
+      `description: ${a.last_desc.slice(0, 200)}`,
+      `PRs: ${Array.from(a.scopes).sort((x, y) => x - y).join(",")}`,
+    ],
+  }));
+}
+
+// Audit-lessons — reads data/_kb/audit_lessons.jsonl (populated by
+// every audit's appendAuditLessons). Uses the shared kb_index
+// aggregator: groups by `signature`, distinct-scopes keyed by PR
+// number, severity from ratingSeverity(agg) which applies the
+// confidence × count rating (see kb_index.ts). This is the same
+// aggregation any other KB reader uses — shared discipline, not
+// per-check custom logic.
+async function checkAuditLessons(): Promise<Finding[]> {
+  const bySig = await aggregate<any>(AUDIT_LESSONS_JSONL, {
+    keyFn: (r) => r?.signature,
+    scopeFn: (r) => (r?.pr_number !== undefined ? `pr-${r.pr_number}` : undefined),
+    checkFn: (r) => r?.check,
+    tailLimit: TAIL_LINES * 4,
+  });
+  if (bySig.size === 0) return [];
+
+  const findings: Finding[] = [];
+  for (const [sig, agg] of bySig) {
+    // Silent on first-ever occurrence — not yet signal.
+    if (agg.count < 2) continue;
+    const sev = ratingSeverity(agg);
+    findings.push({
+      check: "kb_query",
+      severity: sev,
+      summary: `recurring audit pattern (${agg.distinct_scopes} distinct PRs, ${agg.count} flaggings, conf=${agg.confidence.toFixed(2)}): ${agg.representative_summary.slice(0, 160)}`,
+      evidence: [
+        `signature=${sig}`,
+        `checks: ${agg.checks.join(",")}`,
+        `scopes: ${agg.scopes.slice(-6).join(",")}`,
+        formatAgg(agg),
+      ],
+    });
+  }
+  return findings;
+}
+
 // Scrum-master reviews — the scrum pipeline writes one row per
 // accepted per-file review. We match reviews whose `file` matches
 // any path in the PR's diff, then surface the *preview* + which

auditor/checks/static.ts

@@ -61,7 +61,13 @@ export function runStaticCheck(diff: string): Finding[] {
 
       if (!isAuditorCheckerFile) {
         for (const { re, why } of BLOCK_PATTERNS) {
-          if (re.test(added)) {
+          const m = added.match(re);
+          if (m && typeof m.index === "number") {
+            // Skip if the match sits inside a quoted string literal —
+            // this is how rubric files (tests/real-world/*, prompt
+            // templates) legitimately reference the patterns they
+            // guard against, without actually executing them.
+            if (isInsideQuotedString(added, m.index)) continue;
             findings.push({
               check: "static",
               severity: "block",
@@ -154,6 +160,25 @@ function extractNewFields(addedLines: string[]): string[] {
   return Array.from(fields);
 }
 
+// True if `pos` falls inside a double- or single-quoted string on this
+// line (backtick template literals too). Walks left→right toggling the
+// "in quote" state on each unescaped quote. Good enough for single-
+// line matches; multi-line strings aren't parsed (they're extremely
+// rare in the patterns we're blocking on, and would require a proper
+// tokenizer to handle correctly).
+function isInsideQuotedString(line: string, pos: number): boolean {
+  let inDouble = false, inSingle = false, inBacktick = false;
+  for (let i = 0; i < pos; i++) {
+    const c = line[i];
+    const esc = i > 0 && line[i - 1] === "\\";
+    if (esc) continue;
+    if (c === '"' && !inSingle && !inBacktick) inDouble = !inDouble;
+    else if (c === "'" && !inDouble && !inBacktick) inSingle = !inSingle;
+    else if (c === "`" && !inDouble && !inSingle) inBacktick = !inBacktick;
+  }
+  return inDouble || inSingle || inBacktick;
+}
+
 function escape(s: string): string {
   return s.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
 }

auditor/claim_parser.ts

@@ -49,6 +49,58 @@ const WEAK_PATTERNS: RegExp[] = [
   /\bprobably\b/i,
 ];
 
+// Empirical claims: runtime measurements / observed outcomes that can't
+// be verified from a diff (only from the actual run that produced
+// them). Classifying as empirical lets the inference check skip
+// diff-verification and saves the ladder for falsifiable claims.
+//
+// Two classes share this bucket because they share the skip discipline:
+//
+//   1. Runtime metrics    — "58 cloud calls", "306s end-to-end"
+//   2. History/proof refs — "verified on PR #8", "was flipping across runs"
+//
+// Both are assertions about state outside the current diff. The cloud
+// would flag them as "not backed" — but that's a false positive: the
+// proof lives in the referenced run, prior commit, or test output, not
+// in the added lines the cloud is reading.
+const EMPIRICAL_PATTERNS: RegExp[] = [
+  // ─── Runtime metrics ───
+  // Iteration / attempt counts: "6/6 iterations", "attempt 5", "accepted on attempt 3"
+  /\b\d+\s*\/\s*\d+\s+(iterations?|attempts?|cycles?|runs?|shards?)\b/i,
+  /\b(accepted|resolved|converged)\s+on\s+attempt\s+\d+\b/i,
+  // Runtime metrics: "58 cloud calls", "306s end-to-end", "245s total", "5931 chars"
+  /\b\d+\s+(cloud\s+)?calls?\b/i,
+  /\b\d+\s*(ms|s|seconds?|minutes?|m)\s+(end[- ]to[- ]end|total|elapsed|duration)\b/i,
+  /\b\d+\s+chars?\b.*\b(accepted|generated|produced)\b/i,
+  // "escalated through N tiers", "N distinct models"
+  /\bescalated\s+through\s+\d+\b/i,
+  /\b\d+\s+distinct\s+(model|tier)s?\b/i,
+
+  // ─── History / proof references ───
+  // "verified on PR #8", "verified end-to-end on PR 8", "tested against PR #4"
+  // Require PR#N / commit-hash / "prior <word>" to avoid matching
+  // "verified ... in production" (PR without \b-ish anchor previously
+  // consumed "pr" of "production").
+  /\bverified\s+(?:end[- ]to[- ]end\s+)?(?:on|against|in)\s+(?:PR\s*#?\d+|commit\s+[0-9a-f]{6,}|prior\s+\w+|the\s+\w+\s+audit)\b/i,
+  /\btested\s+(?:against|in|on)\s+(?:PR\s*#?\d+|commit\s+[0-9a-f]{6,}|prior\s+\w+)\b/i,
+  // Direct PR/commit references: "PR #8", "on PR 9", "from commit abc123"
+  /\b(?:on|from|in|via|per)\s+PR\s*#?\d+\b/i,
+  /\b(?:from|in|per|against)\s+commit\s+[0-9a-f]{6,}/i,
+  // Observational descriptions of prior behavior: "was flipping", "was X before", "previously observed"
+  /\b(?:was|were)\s+(?:flipping|drifting|inconsistent|non[- ]deterministic|creeping)\b/i,
+  /\bpreviously\s+(?:observed|flagged|reported|seen|landed)\b/i,
+  /\bused\s+to\s+(?:flip|fail|flag|reject|block)\b/i,
+  /\bobserved\s+(?:in|during|on|across)\s+(?:PR|prior|\d+\s+(?:runs?|audits?))/i,
+  // "flipping/drifting across N runs" — historical variance description
+  /\b(?:flipping|drifting|varying|oscillating)\s+across\s+(?:\d+\s+)?(?:runs?|audits?|iterations?)\b/i,
+  // "the proven X" referring to prior work (proven is a STRONG pattern
+  // but in context "the proven FOO" is usually a historical reference,
+  // not a fresh claim). We catch it here so the empirical skip wins.
+  /\bthe\s+proven\s+(?:escalation\s+ladder|pipeline|flow|loop|tier|path)/i,
+  // "from the 9-run test", "across the 5-run validation"
+  /\b(?:from|across|in|during)\s+the\s+\d+[- ]run\s+(?:test|validation|probe|experiment)/i,
+];
+
 export interface ParsedClaims {
   claims: Claim[];
   commits_scanned: number;
@@ -77,9 +129,22 @@ function scanText(text: string, location_prefix: string, commit_sha: string, out
     const line = lines[i];
     if (line.length < 3) continue;
 
-    // Strong patterns first — if a line matches strong, it's strong,
-    // don't double-count as moderate.
-    const strong = firstMatch(line, STRONG_PATTERNS);
+    // Empirical match wins over everything else — if a line ALSO
+    // contains a moderate word like "complete", we still want to
+    // classify it as empirical so the inference check doesn't ask
+    // the cloud to prove "58 cloud calls" from the diff. Order:
+    // empirical → strong → moderate → weak.
+    const empirical = firstUnquotedMatch(line, EMPIRICAL_PATTERNS);
+    if (empirical) {
+      out.push({
+        text: line.trim().slice(0, 200),
+        commit_sha,
+        location: `${location_prefix}:${i + 1}`,
+        strength: "empirical",
+      });
+      continue;
+    }
+    const strong = firstUnquotedMatch(line, STRONG_PATTERNS);
     if (strong) {
       out.push({
         text: line.trim().slice(0, 200),
@@ -89,7 +154,7 @@ function scanText(text: string, location_prefix: string, commit_sha: string, out
       });
       continue;
     }
-    const moderate = firstMatch(line, MODERATE_PATTERNS);
+    const moderate = firstUnquotedMatch(line, MODERATE_PATTERNS);
     if (moderate) {
       out.push({
         text: line.trim().slice(0, 200),
@@ -99,7 +164,7 @@ function scanText(text: string, location_prefix: string, commit_sha: string, out
       });
       continue;
     }
-    const weak = firstMatch(line, WEAK_PATTERNS);
+    const weak = firstUnquotedMatch(line, WEAK_PATTERNS);
     if (weak) {
       out.push({
         text: line.trim().slice(0, 200),
@@ -111,9 +176,35 @@ function scanText(text: string, location_prefix: string, commit_sha: string, out
   }
 }
 
-function firstMatch(text: string, patterns: RegExp[]): RegExp | null {
+// Match a pattern only when its match position is NOT inside a quoted
+// string on the line. Mirrors the same guard in auditor/checks/static.ts
+// — the two files have the same false-positive class: PR authors
+// quote pattern examples in commit message bodies (e.g. `"Phase 45
+// shipped"` as a test example) and without this guard those quoted
+// references get flagged as fresh ship-claims. Only skips when the
+// match itself falls inside quotes; real (unquoted) uses of the same
+// vocabulary still classify correctly.
+function firstUnquotedMatch(text: string, patterns: RegExp[]): RegExp | null {
   for (const p of patterns) {
-    if (p.test(text)) return p;
+    const m = text.match(p);
+    if (!m || typeof m.index !== "number") continue;
+    if (isInsideQuotedString(text, m.index)) continue;
+    return p;
   }
   return null;
 }
+
+// Walks left→right toggling in-quote state on each unescaped quote.
+// Good enough for single-line claims; multi-line strings aren't parsed.
+function isInsideQuotedString(line: string, pos: number): boolean {
+  let inDouble = false, inSingle = false, inBacktick = false;
+  for (let i = 0; i < pos; i++) {
+    const c = line[i];
+    const esc = i > 0 && line[i - 1] === "\\";
+    if (esc) continue;
+    if (c === '"' && !inSingle && !inBacktick) inDouble = !inDouble;
+    else if (c === "'" && !inDouble && !inBacktick) inSingle = !inSingle;
+    else if (c === "`" && !inDouble && !inSingle) inBacktick = !inBacktick;
+  }
+  return inDouble || inSingle || inBacktick;
+}

auditor/fact_extractor.ts

@@ -0,0 +1,271 @@
+// fact_extractor — routes curated TEXT through llm_team_ui's
+// "knowledge extract facts" mode (mode=extract at /api/run).
+//
+// What it gives us: structured {facts, entities, relationships} from
+// whatever curated blob we send. Auditor sends the tree-split
+// inference scratchpad (the best distillation of what a PR changed).
+// Scrum_master will later send its accepted review bodies.
+//
+// Why route through llm_team and not just extract directly from our
+// own checks: llm_team's extract uses a local EXTRACTOR model
+// (qwen2.5) + a separate VERIFIER (gemma2). This cross-check is the
+// discipline J wants for knowledge going into the playbook — facts
+// go in only after a second model has rated them CORRECT /
+// UNVERIFIABLE. Fast (local models, ~10-20s), free, and matches the
+// codereview pattern J already trusts.
+//
+// SSE parsing: llm_team streams SSE events. We're only interested in
+// the final "response" event with role="final" + the extraction
+// response (role="extraction N"). Parse the JSON from the extractor's
+// response text.
+
+const LLM_TEAM = process.env.LH_LLM_TEAM_URL ?? "http://localhost:5000";
+const EXTRACTOR = process.env.LH_FACT_EXTRACTOR ?? "qwen2.5:latest";
+const VERIFIER = process.env.LH_FACT_VERIFIER ?? "gemma2:latest";
+const EXTRACT_TIMEOUT_MS = 120_000;
+const PROJECT_CONTEXT_FILE = process.env.LH_AUDITOR_CONTEXT_FILE
+  ?? "/home/profit/lakehouse/docs/AUDITOR_CONTEXT.md";
+
+let cachedContext: string | null = null;
+async function loadProjectContext(): Promise<string> {
+  if (cachedContext !== null) return cachedContext;
+  try {
+    const { readFile } = await import("node:fs/promises");
+    const raw = await readFile(PROJECT_CONTEXT_FILE, "utf8");
+    // Cap at 4KB — anything past that is more noise than signal for
+    // the extractor/verifier's attention budget.
+    cachedContext = raw.slice(0, 4000);
+  } catch {
+    cachedContext = "";  // context file missing → extractor runs without preamble
+  }
+  return cachedContext;
+}
+
+export interface Entity {
+  name: string;
+  type: string;
+  description?: string;
+}
+
+export interface Relationship {
+  from: string;
+  to: string;
+  type: string;
+}
+
+export interface ExtractedFacts {
+  facts: string[];
+  entities: Entity[];
+  relationships: Relationship[];
+  verification: string;
+  extractor_model: string;
+  verifier_model: string;
+  source_preview: string;
+  // Populated when the extract run completed server-side (llm_team
+  // persists to its own team_runs; this is for our own cross-ref).
+  llm_team_run_id?: number;
+  extracted_at: string;
+  // Per-fact verdicts from the verifier pass (CORRECT/INCORRECT/
+  // UNVERIFIABLE/UNCHECKED). Aligned 1:1 with the *raw* fact list
+  // pre-drop so operators can see which verdicts mapped to dropped
+  // facts if needed.
+  verifier_verdicts?: Array<"CORRECT" | "INCORRECT" | "UNVERIFIABLE" | "UNCHECKED">;
+  facts_dropped_by_verifier?: number;
+  error?: string;
+}
+
+/**
+ * Run the llm_team extract pipeline on `source` text. Returns
+ * structured {facts, entities, relationships}.
+ *
+ * Returns an object with `error` set if the pipeline failed — never
+ * throws, because fact extraction is best-effort enrichment (the
+ * primary audit must not break if llm_team is down).
+ */
+export async function extractFacts(source: string): Promise<ExtractedFacts> {
+  const base: ExtractedFacts = {
+    facts: [],
+    entities: [],
+    relationships: [],
+    verification: "",
+    extractor_model: EXTRACTOR,
+    verifier_model: VERIFIER,
+    source_preview: source.slice(0, 240),
+    extracted_at: new Date().toISOString(),
+  };
+
+  // Prepend project context to the source so the extractor + verifier
+  // know what codebase/framework these facts belong to. Without this,
+  // the verifier marks most domain-specific facts as UNVERIFIABLE ("I
+  // don't know what Lakehouse is"). With it, the verifier can CORRECT-
+  // stamp facts that align with the stated architecture.
+  const context = await loadProjectContext();
+  const prompt = context.length > 0
+    ? `=== PROJECT CONTEXT (for grounding facts; do NOT extract facts from this section) ===\n${context}\n\n=== CONTENT TO EXTRACT FACTS FROM ===\n${source}`
+    : source;
+
+  let resp: Response;
+  try {
+    resp = await fetch(`${LLM_TEAM}/api/run`, {
+      method: "POST",
+      headers: { "content-type": "application/json" },
+      body: JSON.stringify({
+        mode: "extract",
+        prompt,
+        extractor: EXTRACTOR,
+        verifier: VERIFIER,
+        source: "prompt",
+        skip_cache: true,  // cache by prompt would dedup identical
+                           // scratchpads, but we want fresh extraction
+                           // for per-audit facts; cheap since local.
+      }),
+      signal: AbortSignal.timeout(EXTRACT_TIMEOUT_MS),
+    });
+  } catch (e) {
+    return { ...base, error: `fetch failed: ${(e as Error).message}` };
+  }
+
+  if (!resp.ok) {
+    const body = await resp.text().catch(() => "");
+    return { ...base, error: `llm_team /api/run ${resp.status}: ${body.slice(0, 200)}` };
+  }
+
+  // Stream SSE lines; collect the one extraction response + the run_saved event
+  // so we can capture the team-runs ID for cross-ref.
+  const decoder = new TextDecoder();
+  const reader = resp.body?.getReader();
+  if (!reader) return { ...base, error: "no response body" };
+
+  let buffer = "";
+  let extractionText = "";
+  let verifierText = "";
+  let runId: number | undefined = undefined;
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+      buffer += decoder.decode(value, { stream: true });
+      let nl: number;
+      while ((nl = buffer.indexOf("\n\n")) >= 0) {
+        const chunk = buffer.slice(0, nl);
+        buffer = buffer.slice(nl + 2);
+        const dataLine = chunk.split("\n").find(l => l.startsWith("data: "));
+        if (!dataLine) continue;
+        try {
+          const ev = JSON.parse(dataLine.slice(6));
+          if (ev.type === "response") {
+            const role = String(ev.role ?? "");
+            if (role.startsWith("extraction")) extractionText = String(ev.text ?? "");
+            else if (role === "verifier") verifierText = String(ev.text ?? "");
+          } else if (ev.type === "run_saved") {
+            const id = Number(ev.run_id);
+            if (Number.isFinite(id)) runId = id;
+          }
+        } catch { /* skip malformed SSE */ }
+      }
+    }
+  } catch (e) {
+    return { ...base, error: `SSE read failed: ${(e as Error).message}` };
+  }
+
+  // Pull the JSON object out of extractionText (may be wrapped in ```json fences).
+  const parsed = extractFirstJsonObject(extractionText);
+  if (!parsed) {
+    return { ...base, error: "extractor returned no parseable JSON", verification: verifierText };
+  }
+
+  const rawFacts: string[] = Array.isArray(parsed.facts)
+    ? parsed.facts.slice(0, 50).map(String)
+    : [];
+
+  // Parse the verifier's free-form prose into per-fact verdicts, then
+  // drop any fact the verifier explicitly marked INCORRECT. Leave
+  // UNVERIFIABLE in place: many of our extractions are domain-specific
+  // (Lakehouse internals) and the verifier has no prior-knowledge
+  // anchor, so UNVERIFIABLE is the expected verdict for new signal,
+  // not a quality fail. This is verifier-gated persistence: drop only
+  // what's affirmatively wrong, not what's novel.
+  const verdicts = parseVerifierVerdicts(verifierText, rawFacts.length);
+  const incorrectIdx = new Set<number>();
+  verdicts.forEach((v, i) => { if (v === "INCORRECT") incorrectIdx.add(i); });
+  const kept = rawFacts.filter((_, i) => !incorrectIdx.has(i));
+
+  return {
+    ...base,
+    facts: kept,
+    entities: Array.isArray(parsed.entities)
+      ? parsed.entities.slice(0, 30).map((e: any) => ({
+          name: String(e?.name ?? ""),
+          type: String(e?.type ?? ""),
+          description: typeof e?.description === "string" ? e.description.slice(0, 240) : undefined,
+        })).filter(e => e.name.length > 0)
+      : [],
+    relationships: Array.isArray(parsed.relationships)
+      ? parsed.relationships.slice(0, 30).map((r: any) => ({
+          from: String(r?.from ?? ""),
+          to: String(r?.to ?? ""),
+          type: String(r?.type ?? ""),
+        })).filter(r => r.from.length > 0 && r.to.length > 0)
+      : [],
+    verification: verifierText.slice(0, 1500),
+    facts_dropped_by_verifier: incorrectIdx.size,
+    verifier_verdicts: verdicts,
+    llm_team_run_id: runId,
+  };
+}
+
+// Parse verifier's free-form output into a per-fact verdict array.
+// Gemma2 uses several formats depending on prompt mood:
+//   Format A:  **1.** claim... * **Verdict:** CORRECT
+//   Format B:  **1.** claim... * **CORRECT**  (no "Verdict:" label)
+//   Format C:  1. claim... CORRECT
+// Strategy: split on fact numbers, then find the first
+// CORRECT|INCORRECT|UNVERIFIABLE token in each section. Handles all
+// three formats without regex gymnastics.
+function parseVerifierVerdicts(
+  verifierText: string,
+  numFacts: number,
+): Array<"CORRECT" | "INCORRECT" | "UNVERIFIABLE" | "UNCHECKED"> {
+  const out: Array<"CORRECT" | "INCORRECT" | "UNVERIFIABLE" | "UNCHECKED"> =
+    Array(numFacts).fill("UNCHECKED");
+  if (!verifierText) return out;
+
+  // Find each fact section start — "**N.**" or "N." at line start —
+  // and slice out the content up to the NEXT fact number. Each section
+  // gets scanned for the first CORRECT/INCORRECT/UNVERIFIABLE token.
+  const starts: Array<{ idx: number; pos: number }> = [];
+  const header = /(?:^|\n)\s*(?:\*\*)?(\d+)[.)]/g;
+  for (const m of verifierText.matchAll(header)) {
+    const factNum = Number(m[1]);
+    if (!Number.isFinite(factNum)) continue;
+    starts.push({ idx: factNum - 1, pos: m.index! });
+  }
+  for (let i = 0; i < starts.length; i++) {
+    const s = starts[i];
+    const end = i + 1 < starts.length ? starts[i + 1].pos : verifierText.length;
+    if (s.idx < 0 || s.idx >= numFacts) continue;
+    const section = verifierText.slice(s.pos, end);
+    const v = section.match(/\b(CORRECT|INCORRECT|UNVERIFIABLE)\b/i);
+    if (v) out[s.idx] = v[1].toUpperCase() as "CORRECT" | "INCORRECT" | "UNVERIFIABLE";
+  }
+  return out;
+}
+
+// Lift the first balanced JSON object out of (possibly fenced) text.
+// Same discipline as inference.ts::extractJson.
+function extractFirstJsonObject(text: string): any | null {
+  const cleaned = text.replace(/^```(?:json)?\s*/im, "").replace(/```\s*$/im, "");
+  let depth = 0, start = -1;
+  for (let i = 0; i < cleaned.length; i++) {
+    const c = cleaned[i];
+    if (c === "{") { if (depth === 0) start = i; depth++; }
+    else if (c === "}") {
+      depth--;
+      if (depth === 0 && start >= 0) {
+        try { return JSON.parse(cleaned.slice(start, i + 1)); } catch { start = -1; }
+      }
+    }
+  }
+  return null;
+}

auditor/kb_index.ts

@@ -0,0 +1,161 @@
+// kb_index — generic on-the-fly aggregation over append-only JSONL
+// scratchpads (audit_lessons, scrum_reviews, outcomes, observer ops).
+//
+// The mem0 insight: raw rows are CHEAP and tell the full story, but
+// downstream prompts need a DEFINITION, not a log. A definition is
+// the aggregate: "this signature has fired N times across M distinct
+// scopes, first_seen=X, last_seen=Y, confidence=M/N."
+//
+// This library is the single shared aggregator. Every KB writer keeps
+// appending raw rows; every KB reader uses aggregate() instead of
+// tailing the raw stream. No second file to sync, no ADD/UPDATE/NOOP
+// routing — the stats roll up from the raw rows every time.
+//
+// Why this works past hundreds of runs:
+//   - aggregate() is bounded by distinct_signatures, not total_rows.
+//   - confidence = distinct_scopes / count — low for same-scope noise,
+//     high for cross-scope patterns. Downstream severity ramps on
+//     confidence × count, not raw count, so one unfixed PR can't
+//     inflate its own recurrence score (the classic mem0 failure).
+//   - rotation (later) moves old raw to archive files; aggregate()
+//     can still read both to compute lifetime counts when needed.
+
+import { readFile } from "node:fs/promises";
+
+export interface AggregateRow {
+  signature: string;
+  count: number;
+  distinct_scopes: number;
+  first_seen: string;
+  last_seen: string;
+  confidence: number;        // distinct_scopes / count — capped at 1.0
+  representative_summary: string;  // most-recent summary for this signature
+  scopes: string[];          // up to 20 most-recent scopes for debugging
+  checks: string[];          // distinct `check` values (audit_lessons-specific)
+}
+
+export interface AggregateOptions<T> {
+  /** How to extract the dedup key from a row. */
+  keyFn: (row: T) => string | undefined;
+  /** How to extract the "scope" — distinct scopes count gives confidence. */
+  scopeFn: (row: T) => string | undefined;
+  /** How to extract the timestamp (defaults to row.audited_at / row.reviewed_at / row.timestamp). */
+  timeFn?: (row: T) => string | undefined;
+  /** How to extract a representative summary (defaults to row.summary). */
+  summaryFn?: (row: T) => string | undefined;
+  /** Max rows to read from the JSONL tail; 0 = read all. */
+  tailLimit?: number;
+  /** Include per-row check field (for multi-check aggregates). */
+  checkFn?: (row: T) => string | undefined;
+}
+
+/**
+ * Read a JSONL file and produce the aggregate map keyed by signature.
+ * Safe on missing or malformed files — returns empty map.
+ */
+export async function aggregate<T = any>(
+  jsonlPath: string,
+  opts: AggregateOptions<T>,
+): Promise<Map<string, AggregateRow>> {
+  const out = new Map<string, AggregateRow>();
+  let raw: string;
+  try { raw = await readFile(jsonlPath, "utf8"); } catch { return out; }
+  const lines = raw.split("\n").filter(l => l.length > 0);
+  const sliceFrom = opts.tailLimit && opts.tailLimit > 0 ? Math.max(0, lines.length - opts.tailLimit) : 0;
+
+  const timeFn = opts.timeFn ?? ((r: any) => r?.audited_at ?? r?.reviewed_at ?? r?.timestamp ?? r?.ran_at);
+  const summaryFn = opts.summaryFn ?? ((r: any) => r?.summary ?? r?.representative_summary);
+
+  // Per-signature scope tracking — need counts by scope to compute
+  // distinct_scopes without double-counting a scope that appears 50
+  // times. Using a Set<scope> per signature.
+  const scopeSets = new Map<string, Set<string>>();
+  const checkSets = new Map<string, Set<string>>();
+
+  for (let i = sliceFrom; i < lines.length; i++) {
+    let row: T;
+    try { row = JSON.parse(lines[i]) as T; } catch { continue; }
+    const sig = opts.keyFn(row);
+    if (!sig) continue;
+
+    let agg = out.get(sig);
+    if (!agg) {
+      agg = {
+        signature: sig,
+        count: 0,
+        distinct_scopes: 0,
+        first_seen: "",
+        last_seen: "",
+        confidence: 0,
+        representative_summary: "",
+        scopes: [],
+        checks: [],
+      };
+      out.set(sig, agg);
+      scopeSets.set(sig, new Set<string>());
+      checkSets.set(sig, new Set<string>());
+    }
+
+    agg.count += 1;
+
+    const scope = opts.scopeFn(row);
+    if (scope !== undefined && scope !== null && scope !== "") {
+      scopeSets.get(sig)!.add(String(scope));
+      // Keep scopes array ordered by recency (newest wins — shift
+      // oldest when at cap).
+      const arr = agg.scopes;
+      const s = String(scope);
+      const existing = arr.indexOf(s);
+      if (existing >= 0) arr.splice(existing, 1);
+      arr.push(s);
+      if (arr.length > 20) arr.shift();
+    }
+
+    if (opts.checkFn) {
+      const c = opts.checkFn(row);
+      if (c) checkSets.get(sig)!.add(String(c));
+    }
+
+    const t = timeFn(row);
+    if (t) {
+      if (!agg.first_seen || t < agg.first_seen) agg.first_seen = t;
+      if (!agg.last_seen  || t > agg.last_seen)  agg.last_seen = t;
+    }
+
+    const s = summaryFn(row);
+    if (s) agg.representative_summary = String(s);
+  }
+
+  // Finalize derived fields.
+  for (const [sig, agg] of out) {
+    const scopes = scopeSets.get(sig) ?? new Set<string>();
+    agg.distinct_scopes = scopes.size;
+    agg.confidence = agg.count > 0 ? Math.min(1, agg.distinct_scopes / agg.count) : 0;
+    const checks = checkSets.get(sig);
+    if (checks) agg.checks = Array.from(checks).sort();
+  }
+  return out;
+}
+
+/**
+ * Severity policy derived from aggregate stats. The rating lives here
+ * (not in each check) so all KB readers ramp severity consistently.
+ *
+ *   - confidence × count product is the real signal.
+ *   - Low confidence (< 0.3) = same-scope noise → info regardless of count.
+ *   - Mid confidence (0.3-0.6) = mixed signal → warn at count ≥ 3.
+ *   - High confidence (> 0.6) with count ≥ 5 = block-worthy cross-cutting pattern.
+ *
+ * Callers can override by reading agg directly; this is the default
+ * policy that matches the "don't escalate one unfixed PR" discipline.
+ */
+export function ratingSeverity(agg: AggregateRow): "info" | "warn" | "block" {
+  if (agg.confidence >= 0.6 && agg.count >= 5) return "block";
+  if (agg.confidence >= 0.3 && agg.count >= 3) return "warn";
+  return "info";
+}
+
+/** Human-friendly one-line summary of an aggregate row for finding evidence. */
+export function formatAgg(agg: AggregateRow): string {
+  return `count=${agg.count} distinct_scopes=${agg.distinct_scopes} confidence=${agg.confidence.toFixed(2)} seen=[${agg.first_seen.slice(0, 10)}..${agg.last_seen.slice(0, 10)}]`;
+}

auditor/kb_stats.ts

@@ -0,0 +1,269 @@
+// kb_stats — on-demand dashboard numbers from the KB scratchpad
+// files. Reads data/_auditor/verdicts/*, data/_kb/audit_lessons.jsonl,
+// data/_kb/audit_facts.jsonl, data/_kb/audit_discrepancies.jsonl,
+// data/_kb/scrum_reviews.jsonl and prints:
+//
+//   - verdict flip-flop rate (same SHA re-audited, verdict changed?)
+//   - consensus discrepancy rate (N runs disagreed on a claim)
+//   - confidence distribution from kb_index aggregator
+//   - top N recurring entities from audit_facts
+//   - fact growth over time
+//   - scrum vs inference KB split
+//
+// Run:  bun run auditor/kb_stats.ts
+//       bun run auditor/kb_stats.ts --top 15     # show top 15 entities
+//       bun run auditor/kb_stats.ts --json       # machine-readable
+//
+// This is the "dashboard" without running Grafana. If someone really
+// wants a dashboard, wire this output into a static HTML page + cron.
+
+import { readFile, readdir } from "node:fs/promises";
+import { join } from "node:path";
+import { aggregate } from "./kb_index.ts";
+
+const REPO = "/home/profit/lakehouse";
+const VERDICTS_DIR = `${REPO}/data/_auditor/verdicts`;
+const AUDIT_LESSONS = `${REPO}/data/_kb/audit_lessons.jsonl`;
+const AUDIT_FACTS = `${REPO}/data/_kb/audit_facts.jsonl`;
+const AUDIT_DISCREPANCIES = `${REPO}/data/_kb/audit_discrepancies.jsonl`;
+const SCRUM_REVIEWS = `${REPO}/data/_kb/scrum_reviews.jsonl`;
+
+interface Args {
+  top: number;
+  json: boolean;
+}
+
+function parseArgs(argv: string[]): Args {
+  const a: Args = { top: 10, json: false };
+  for (let i = 2; i < argv.length; i++) {
+    if (argv[i] === "--top") a.top = Number(argv[++i] ?? 10);
+    else if (argv[i] === "--json") a.json = true;
+  }
+  return a;
+}
+
+async function readJsonl<T = any>(path: string): Promise<T[]> {
+  try {
+    const raw = await readFile(path, "utf8");
+    return raw.split("\n").filter(l => l.length > 0).map(l => {
+      try { return JSON.parse(l) as T; } catch { return null as any; }
+    }).filter(r => r !== null);
+  } catch { return []; }
+}
+
+async function loadVerdicts(): Promise<Array<{ pr: number; sha: string; overall: string; findings_total: number; findings_block: number; findings_warn: number }>> {
+  let files: string[] = [];
+  try { files = await readdir(VERDICTS_DIR); } catch { return []; }
+  const out = [];
+  for (const f of files) {
+    if (!f.endsWith(".json")) continue;
+    const m = f.match(/^(\d+)-([0-9a-f]+)\.json$/);
+    if (!m) continue;
+    try {
+      const v = JSON.parse(await readFile(join(VERDICTS_DIR, f), "utf8"));
+      out.push({
+        pr: Number(m[1]),
+        sha: m[2],
+        overall: String(v.overall),
+        findings_total: Number(v.metrics?.findings_total ?? 0),
+        findings_block: Number(v.metrics?.findings_block ?? 0),
+        findings_warn: Number(v.metrics?.findings_warn ?? 0),
+      });
+    } catch { /* skip corrupt */ }
+  }
+  return out;
+}
+
+interface Stats {
+  audit_count: number;
+  verdict_distribution: Record<string, number>;
+  // Same PR with multiple SHAs — if verdicts differ, that's drift across
+  // the PR's commit history. Not a flip-flop in the classical sense,
+  // but worth surfacing (e.g. "PR #8 was block block req req block").
+  per_pr_verdict_sequences: Record<number, string[]>;
+  // For each PR with ≥ 2 audits, how many distinct verdicts did it
+  // produce? 1 = stable; 2+ = some flipping.
+  verdict_instability: { pr_count: number; pr_with_multiple_verdicts: number; pr_with_3plus: number };
+  consensus: { discrepancy_count: number; tiebreaker_used: number; unresolved: number };
+  kb: {
+    audit_lessons_rows: number;
+    audit_facts_rows: number;
+    scrum_reviews_rows: number;
+    distinct_finding_signatures: number;
+    distinct_entities_across_prs: number;
+    entities_in_2plus_prs: number;
+    entities_in_5plus_prs: number;
+  };
+  fact_quality: {
+    verifier_verdict_distribution: Record<string, number>;
+    facts_dropped_by_verifier_total: number;
+    extraction_success_rate: number;
+  };
+  top_entities: Array<{ name: string; distinct_prs: number; count: number; types: string[] }>;
+  kb_by_source: Record<string, number>;
+}
+
+async function collect(args: Args): Promise<Stats> {
+  const verdicts = await loadVerdicts();
+  const lessons = await readJsonl<any>(AUDIT_LESSONS);
+  const facts = await readJsonl<any>(AUDIT_FACTS);
+  const disc = await readJsonl<any>(AUDIT_DISCREPANCIES);
+  const reviews = await readJsonl<any>(SCRUM_REVIEWS);
+
+  // Verdict stability
+  const byPr: Record<number, string[]> = {};
+  const verdictDist: Record<string, number> = {};
+  for (const v of verdicts) {
+    (byPr[v.pr] ??= []).push(v.overall);
+    verdictDist[v.overall] = (verdictDist[v.overall] ?? 0) + 1;
+  }
+  let multi = 0, tri = 0;
+  for (const [_, seq] of Object.entries(byPr)) {
+    const distinct = new Set(seq);
+    if (distinct.size >= 2) multi++;
+    if (distinct.size >= 3) tri++;
+  }
+
+  // Consensus drift
+  const consensus = {
+    discrepancy_count: disc.length,
+    tiebreaker_used: disc.filter(d => String(d.resolution).startsWith("tiebreaker")).length,
+    unresolved: disc.filter(d => d.resolution === "unresolved").length,
+  };
+
+  // Lesson signatures
+  const lessonAgg = await aggregate<any>(AUDIT_LESSONS, {
+    keyFn: r => r?.signature,
+    scopeFn: r => (r?.pr_number !== undefined ? `pr-${r.pr_number}` : undefined),
+  });
+
+  // Entity aggregation across audit_facts rows
+  interface EntAgg { distinct_prs: Set<number>; count: number; types: Set<string>; name: string; sources: Set<string> }
+  const entAgg = new Map<string, EntAgg>();
+  const sourceCount: Record<string, number> = {};
+  let totalVerdictDist: Record<string, number> = { CORRECT: 0, INCORRECT: 0, UNVERIFIABLE: 0, UNCHECKED: 0 };
+  let factsDroppedTotal = 0;
+  let extractionsWithFacts = 0;
+
+  for (const row of facts) {
+    const src = String(row.source ?? "unknown");
+    sourceCount[src] = (sourceCount[src] ?? 0) + 1;
+    const pr = Number(row.pr_number);
+    if (Array.isArray(row.verifier_verdicts)) {
+      for (const v of row.verifier_verdicts) {
+        totalVerdictDist[v] = (totalVerdictDist[v] ?? 0) + 1;
+      }
+    }
+    factsDroppedTotal += Number(row.facts_dropped_by_verifier ?? 0);
+    if ((Array.isArray(row.facts) && row.facts.length > 0) || (Array.isArray(row.entities) && row.entities.length > 0)) {
+      extractionsWithFacts++;
+    }
+    for (const e of Array.isArray(row.entities) ? row.entities : []) {
+      const name = String(e?.name ?? "").trim();
+      if (name.length < 3) continue;
+      const key = name.toLowerCase();
+      const agg = entAgg.get(key) ?? { distinct_prs: new Set(), count: 0, types: new Set(), name, sources: new Set() };
+      agg.count++;
+      if (Number.isFinite(pr) && pr > 0) agg.distinct_prs.add(pr);
+      if (e?.type) agg.types.add(String(e.type));
+      agg.sources.add(src);
+      entAgg.set(key, agg);
+    }
+  }
+
+  const entitiesIn2Plus = Array.from(entAgg.values()).filter(a => a.distinct_prs.size >= 2).length;
+  const entitiesIn5Plus = Array.from(entAgg.values()).filter(a => a.distinct_prs.size >= 5).length;
+  const topEntities = Array.from(entAgg.values())
+    .sort((a, b) => b.distinct_prs.size - a.distinct_prs.size || b.count - a.count)
+    .slice(0, args.top)
+    .map(a => ({
+      name: a.name,
+      distinct_prs: a.distinct_prs.size,
+      count: a.count,
+      types: Array.from(a.types),
+    }));
+
+  const stats: Stats = {
+    audit_count: verdicts.length,
+    verdict_distribution: verdictDist,
+    per_pr_verdict_sequences: byPr,
+    verdict_instability: {
+      pr_count: Object.keys(byPr).length,
+      pr_with_multiple_verdicts: multi,
+      pr_with_3plus: tri,
+    },
+    consensus,
+    kb: {
+      audit_lessons_rows: lessons.length,
+      audit_facts_rows: facts.length,
+      scrum_reviews_rows: reviews.length,
+      distinct_finding_signatures: lessonAgg.size,
+      distinct_entities_across_prs: entAgg.size,
+      entities_in_2plus_prs: entitiesIn2Plus,
+      entities_in_5plus_prs: entitiesIn5Plus,
+    },
+    fact_quality: {
+      verifier_verdict_distribution: totalVerdictDist,
+      facts_dropped_by_verifier_total: factsDroppedTotal,
+      extraction_success_rate: facts.length > 0 ? extractionsWithFacts / facts.length : 0,
+    },
+    top_entities: topEntities,
+    kb_by_source: sourceCount,
+  };
+  return stats;
+}
+
+function renderHuman(s: Stats): string {
+  const lines: string[] = [];
+  lines.push("═══ KB STATS ═══");
+  lines.push("");
+  lines.push(`Audits: ${s.audit_count} total across ${s.verdict_instability.pr_count} distinct PRs`);
+  lines.push(`Verdicts: ${Object.entries(s.verdict_distribution).map(([k, v]) => `${k}=${v}`).join("  ")}`);
+  const multiplePct = s.verdict_instability.pr_count > 0
+    ? Math.round(100 * s.verdict_instability.pr_with_multiple_verdicts / s.verdict_instability.pr_count)
+    : 0;
+  lines.push(`Verdict instability: ${s.verdict_instability.pr_with_multiple_verdicts}/${s.verdict_instability.pr_count} PRs had 2+ distinct verdicts (${multiplePct}%) — 3+ distinct: ${s.verdict_instability.pr_with_3plus}`);
+  lines.push("");
+  lines.push("─── Consensus ───");
+  lines.push(`  discrepancies logged: ${s.consensus.discrepancy_count}`);
+  lines.push(`  tiebreaker used: ${s.consensus.tiebreaker_used}`);
+  lines.push(`  unresolved: ${s.consensus.unresolved}`);
+  const dRate = s.audit_count > 0 ? (100 * s.consensus.discrepancy_count / s.audit_count).toFixed(1) : "0";
+  lines.push(`  discrepancy rate: ${dRate}% of audits`);
+  lines.push("");
+  lines.push("─── KB size ───");
+  lines.push(`  audit_lessons.jsonl:     ${s.kb.audit_lessons_rows} rows, ${s.kb.distinct_finding_signatures} distinct signatures`);
+  lines.push(`  audit_facts.jsonl:       ${s.kb.audit_facts_rows} rows, ${s.kb.distinct_entities_across_prs} distinct entities`);
+  lines.push(`  scrum_reviews.jsonl:     ${s.kb.scrum_reviews_rows} rows`);
+  lines.push(`  entities in 2+ PRs:      ${s.kb.entities_in_2plus_prs}`);
+  lines.push(`  entities in 5+ PRs:      ${s.kb.entities_in_5plus_prs} ← strong cross-cutting signal`);
+  lines.push("");
+  lines.push("─── Fact quality ───");
+  const v = s.fact_quality.verifier_verdict_distribution;
+  lines.push(`  verifier verdicts:  CORRECT=${v.CORRECT ?? 0}  UNVERIFIABLE=${v.UNVERIFIABLE ?? 0}  UNCHECKED=${v.UNCHECKED ?? 0}  INCORRECT=${v.INCORRECT ?? 0}`);
+  lines.push(`  facts dropped by verifier: ${s.fact_quality.facts_dropped_by_verifier_total}`);
+  lines.push(`  extraction success rate: ${(s.fact_quality.extraction_success_rate * 100).toFixed(1)}%`);
+  lines.push("");
+  lines.push("─── KB sources ───");
+  for (const [src, n] of Object.entries(s.kb_by_source)) {
+    lines.push(`  ${src}: ${n}`);
+  }
+  lines.push("");
+  lines.push(`─── Top ${s.top_entities.length} recurring entities ───`);
+  for (const e of s.top_entities) {
+    lines.push(`  [${e.distinct_prs} PRs × ${e.count} obs]  ${e.name}  (${e.types.join(",")})`);
+  }
+  return lines.join("\n");
+}
+
+async function main() {
+  const args = parseArgs(process.argv);
+  const stats = await collect(args);
+  if (args.json) {
+    console.log(JSON.stringify(stats, (_, v) => v instanceof Set ? Array.from(v) : v, 2));
+  } else {
+    console.log(renderHuman(stats));
+  }
+}
+
+main().catch(e => { console.error("[kb_stats] fatal:", e); process.exit(1); });

auditor/types.ts

@@ -18,7 +18,14 @@ export interface Claim {
   // Heuristic rating of how strong the claim is. "green+tested"
   // is strong; "should work" is weak. Drives sensitivity — stronger
   // claims get harder-blocked on weak evidence.
-  strength: "weak" | "moderate" | "strong";
+  //
+  // "empirical" is a separate class: runtime measurements like
+  // "N cloud calls" / "306s end-to-end" / "accepted on attempt N".
+  // These cannot be verified from a static diff — only from the test
+  // output that produced them. Inference skips diff-verification for
+  // empirical claims; they become info-level context unless a future
+  // runtime_evidence check contradicts them.
+  strength: "weak" | "moderate" | "strong" | "empirical";
 }
 
 export interface Finding {

docs/AUDITOR_CONTEXT.md

@@ -0,0 +1,69 @@
+# Auditor Context — project preamble for fact extraction
+
+This file is read by `auditor/fact_extractor.ts` and prepended to the
+extract-facts prompt sent to llm_team. The goal: give the extractor +
+verifier enough grounding to ground domain-specific facts instead of
+marking them UNVERIFIABLE by default.
+
+Keep this short (< 400 words). Verifier only reads the first ~4KB of
+the prompt alongside the facts. Longer = noise, not signal.
+
+Update when: a new Phase lands, a crate is added/removed, the project's
+primary domain shifts (e.g. staffing → DevOps).
+
+---
+
+## What Lakehouse is
+
+Lakehouse is a Rust-first data platform over S3-compatible object
+storage. Primary use: a staffing company ingesting legacy CRM data for
+AI-powered worker matching, contract fulfillment, and playbook-driven
+coordination.
+
+Architecture: 13 Rust crates + a Python sidecar (Ollama) + TypeScript
+sub-agents (auditor, scrum_master, bot). Runs on a single server
+(Nvidia A4000, 128GB RAM). All services on localhost: gateway :3100,
+sidecar :3200, UI :3300, MCP :3700, observer :3800, MinIO :9000.
+
+## Key crates (each maps to a responsibility)
+
+- **shared** — types, Arrow helpers, PII utilities, SecretsProvider
+- **proto** — gRPC definitions
+- **storaged** — S3/MinIO I/O, AppendLog, ErrorJournal
+- **catalogd** — metadata authority (manifests, views, tombstones)
+- **queryd** — DataFusion SQL, MemTable cache, compaction
+- **ingestd** — CSV/JSON/PDF/Postgres/MySQL ingest
+- **vectord** — embeddings, HNSW index, **playbook_memory meta-index** (Phase 19+)
+- **vectord-lance** — Lance 4.0 firewall crate (separate Arrow version)
+- **journald** — append-only mutation event log
+- **aibridge** — Rust↔Python sidecar bridge, context budget + continuation
+- **gateway** — Axum HTTP :3100 + gRPC :3101 (Phase 38+ adds /v1/chat)
+- **ui** — Dioxus WASM (stale, pre-Phase-9)
+- **lance-bench** — standalone benchmark
+
+## Current architectural direction (Phase 38-44)
+
+Universal AI Control Plane: a `/v1/chat` OpenAI-compatible API that
+routes all LLM traffic through one layer for token accounting + provider
+fallback. Truth Layer + Validation Pipeline enforce staffing-domain
+invariants (worker eligibility, PII, contract rules). The Auditor
+(Phase A of cohesion plan) hard-blocks PR merges on placeholder code.
+
+## Auditor sub-agent role
+
+`auditor/` (TypeScript, Bun runtime) polls Gitea every 90s for open PRs.
+For each fresh head SHA it runs 4 checks in parallel: static (grep-style
+placeholder detection), dynamic (runs the hybrid fixture), inference
+(gpt-oss:120b cloud review with N=3 consensus + qwen3-coder:480b
+tie-breaker), and kb_query (reads `data/_kb/*.jsonl` for prior evidence).
+Verdicts post to Gitea as commit status + review comment. Findings
+append to `data/_kb/audit_lessons.jsonl` (path-agnostic signatures for
+dedup). Curated scratchpads from tree-split get routed through this
+extract-facts pipeline to populate `audit_facts.jsonl` — which is what
+you (the extractor) are currently producing.
+
+## Things that are NOT the auditor
+
+- The LLM Team UI at `/root/llm_team_ui.py` (devop.live:5000) — a separate product for human-facing multi-model experimentation
+- The scrum_master pipeline at `tests/real-world/scrum_master_pipeline.ts` — reviews files, not claims
+- The bot at `bot/` — will apply fixes, doesn't audit

tests/real-world/nine_consecutive_audits.ts

@@ -0,0 +1,181 @@
+// Nine-consecutive audit runner — empirical test of the predictive-
+// compounding property. Pushes 9 empty commits to the current branch,
+// waits for each audit to complete on the new SHA, captures the
+// verdict + audit_lessons state after each run, and reports whether
+// the KB stabilizes or drifts.
+//
+// What we expect (favorable compounding):
+//   - signature_count grows sublinearly (same patterns recur, so
+//     distinct-signature count stabilizes fast)
+//   - verdict settles on a stable value after run 2-3 (first audit
+//     establishes baseline, rest repeat)
+//   - confidence stays LOW for all signatures (same PR repeatedly)
+//   - NO new recurring findings fire because confidence < 0.3 on
+//     same-PR noise (kb_index rating policy)
+//
+// What would indicate drift (the thing we want to prove DOESN'T happen):
+//   - signature_count grows linearly — each run produces new signatures
+//   - verdict oscillates (block → approve → block ...)
+//   - confidence inflates — kb_index rating escalates on repeated runs
+//
+// Run: bun run tests/real-world/nine_consecutive_audits.ts
+
+import { readFile } from "node:fs/promises";
+import { aggregate } from "../../auditor/kb_index.ts";
+
+const REPO = "/home/profit/lakehouse";
+const AUDIT_LESSONS = `${REPO}/data/_kb/audit_lessons.jsonl`;
+const VERDICTS_DIR = `${REPO}/data/_auditor/verdicts`;
+const POLL_INTERVAL_MS = 5_000;
+const AUDIT_TIMEOUT_MS = 180_000;
+const RUNS = Number(process.env.LH_AUDIT_RUNS ?? 9);
+const TARGET_PR = Number(process.env.LH_AUDIT_PR ?? 8);
+
+async function sh(cmd: string): Promise<{ stdout: string; stderr: string; code: number }> {
+  const p = Bun.spawn(["bash", "-lc", cmd], { cwd: REPO, stdout: "pipe", stderr: "pipe" });
+  const [stdout, stderr] = await Promise.all([new Response(p.stdout).text(), new Response(p.stderr).text()]);
+  const code = await p.exited;
+  return { stdout, stderr, code };
+}
+
+async function getHeadSha(): Promise<string> {
+  const r = await sh("git rev-parse HEAD");
+  return r.stdout.trim();
+}
+
+async function pushEmptyCommit(n: number): Promise<string> {
+  const msg = `test: nine-consecutive audit run ${n}/${RUNS} (compounding probe)`;
+  await sh(`GIT_AUTHOR_NAME=profit GIT_AUTHOR_EMAIL=profit@lakehouse GIT_COMMITTER_NAME=profit GIT_COMMITTER_EMAIL=profit@lakehouse git commit --allow-empty -m "${msg}"`);
+  const sha = await getHeadSha();
+  const pushCmd = `PAT="dead60d1160a02f81d241197d5d18f4608794fb2"; git -c credential.helper='!f() { echo "username=profit"; echo "password='$PAT'"; }; f' push origin HEAD 2>&1`;
+  const pr = await sh(pushCmd);
+  if (pr.code !== 0) throw new Error(`push failed: ${pr.stderr || pr.stdout}`);
+  return sha;
+}
+
+async function waitForVerdict(sha: string, deadlineMs: number): Promise<any> {
+  const short = sha.slice(0, 12);
+  const path = `${VERDICTS_DIR}/${TARGET_PR}-${short}.json`;
+  const start = Date.now();
+  while (Date.now() - start < deadlineMs) {
+    try {
+      const raw = await readFile(path, "utf8");
+      return JSON.parse(raw);
+    } catch { /* not yet */ }
+    await new Promise(r => setTimeout(r, POLL_INTERVAL_MS));
+  }
+  throw new Error(`no verdict file after ${deadlineMs}ms: ${path}`);
+}
+
+async function captureAggState(): Promise<{ sig_count: number; max_count: number; max_confidence: number; top3: Array<{ sig: string; count: number; conf: number; summary: string }> }> {
+  const agg = await aggregate<any>(AUDIT_LESSONS, {
+    keyFn: (r) => r?.signature,
+    scopeFn: (r) => (r?.pr_number !== undefined ? `pr-${r.pr_number}` : undefined),
+  });
+  const list = Array.from(agg.values()).sort((a, b) => b.count - a.count);
+  return {
+    sig_count: list.length,
+    max_count: list[0]?.count ?? 0,
+    max_confidence: list.reduce((m, a) => Math.max(m, a.confidence), 0),
+    top3: list.slice(0, 3).map(a => ({
+      sig: a.signature,
+      count: a.count,
+      conf: a.confidence,
+      summary: a.representative_summary.slice(0, 80),
+    })),
+  };
+}
+
+interface RunRecord {
+  run: number;
+  sha: string;
+  verdict_overall: string;
+  findings_total: number;
+  findings_block: number;
+  findings_warn: number;
+  findings_info: number;
+  audit_duration_ms: number;
+  claims_total: number;
+  claims_empirical: number;
+  kb_sig_count_after: number;
+  kb_max_count_after: number;
+  kb_max_confidence_after: number;
+}
+
+async function main() {
+  console.log(`[nine] target PR: #${TARGET_PR}`);
+  console.log(`[nine] runs:      ${RUNS}`);
+  console.log(`[nine] audit_lessons.jsonl: ${AUDIT_LESSONS}`);
+  console.log("");
+
+  const baseline = await captureAggState();
+  console.log(`[nine] baseline: sig_count=${baseline.sig_count} max_count=${baseline.max_count} max_conf=${baseline.max_confidence.toFixed(2)}`);
+  console.log("");
+
+  const records: RunRecord[] = [];
+  for (let n = 1; n <= RUNS; n++) {
+    const t0 = Date.now();
+    console.log(`─── run ${n}/${RUNS} ───`);
+    const sha = await pushEmptyCommit(n);
+    console.log(`  pushed ${sha.slice(0, 12)}`);
+    const verdict = await waitForVerdict(sha, AUDIT_TIMEOUT_MS);
+    const after = await captureAggState();
+    const rec: RunRecord = {
+      run: n,
+      sha: sha.slice(0, 12),
+      verdict_overall: String(verdict.overall),
+      findings_total: Number(verdict.metrics?.findings_total ?? 0),
+      findings_block: Number(verdict.metrics?.findings_block ?? 0),
+      findings_warn: Number(verdict.metrics?.findings_warn ?? 0),
+      findings_info: Number(verdict.metrics?.findings_info ?? 0),
+      audit_duration_ms: Number(verdict.metrics?.audit_duration_ms ?? 0),
+      claims_total: Number(verdict.metrics?.claims_total ?? 0),
+      claims_empirical: Number(verdict.metrics?.claims_empirical ?? 0),
+      kb_sig_count_after: after.sig_count,
+      kb_max_count_after: after.max_count,
+      kb_max_confidence_after: after.max_confidence,
+    };
+    records.push(rec);
+    console.log(`  verdict=${rec.verdict_overall} findings=${rec.findings_total} (b=${rec.findings_block} w=${rec.findings_warn})`);
+    console.log(`  kb after: sig=${rec.kb_sig_count_after} max_count=${rec.kb_max_count_after} max_conf=${rec.kb_max_confidence_after.toFixed(2)}`);
+    console.log(`  elapsed: ${((Date.now() - t0) / 1000).toFixed(1)}s`);
+    console.log("");
+  }
+
+  console.log("═══ FINAL ═══");
+  console.log("run | verdict          | find  | block  warn  info | dur_s | kb_sig  max_count  max_conf");
+  for (const r of records) {
+    console.log(
+      `  ${String(r.run).padStart(1)}  | ${r.verdict_overall.padEnd(16)} | ${String(r.findings_total).padStart(4)}  | ${String(r.findings_block).padStart(5)} ${String(r.findings_warn).padStart(5)} ${String(r.findings_info).padStart(5)}  | ${(r.audit_duration_ms / 1000).toFixed(1).padStart(5)} | ${String(r.kb_sig_count_after).padStart(6)}  ${String(r.kb_max_count_after).padStart(9)}  ${r.kb_max_confidence_after.toFixed(2)}`,
+    );
+  }
+
+  console.log("");
+  console.log("═══ COMPOUNDING PROPERTY ═══");
+  const sigDelta = records[records.length - 1].kb_sig_count_after - baseline.sig_count;
+  const maxCount = records[records.length - 1].kb_max_count_after;
+  const maxConf = records[records.length - 1].kb_max_confidence_after;
+  console.log(`  signatures added over ${RUNS} runs: ${sigDelta}`);
+  console.log(`  max count after run ${RUNS}:         ${maxCount} (same-PR recurrences per signature)`);
+  console.log(`  max confidence after run ${RUNS}:    ${maxConf.toFixed(2)} (expect LOW — same-PR should not inflate)`);
+
+  const verdictSet = new Set(records.map(r => r.verdict_overall));
+  if (verdictSet.size === 1) {
+    console.log(`  verdict stable: all ${RUNS} runs returned '${[...verdictSet][0]}' ✓`);
+  } else {
+    console.log(`  verdict oscillated across runs: ${[...verdictSet].join(" | ")} ✗`);
+  }
+
+  if (maxConf < 0.3) {
+    console.log(`  confidence policy holding: same-PR noise stays below escalation threshold ✓`);
+  } else {
+    console.log(`  ⚠ confidence escalated above 0.3 on same-PR noise — kb_index policy needs tightening`);
+  }
+
+  const jsonOut = `${REPO}/tests/real-world/runs/nine_consecutive_${Date.now().toString(36)}.json`;
+  await Bun.write(jsonOut, JSON.stringify({ target_pr: TARGET_PR, baseline, records }, null, 2));
+  console.log("");
+  console.log(`  report: ${jsonOut}`);
+}
+
+main().catch(e => { console.error("[nine] fatal:", e); process.exit(1); });

tests/real-world/scrum_master_pipeline.ts

@@ -343,12 +343,50 @@ Respond with markdown. Be specific, not generic. Cite file-region + PRD-chunk-of
       attempts_made: history.length,
       tree_split_fired: treeSplitFired,
       suggestions_preview: accepted.slice(0, 2000),
+      schema_version: 2,
+      scrum_master_reviewed: true,
     };
     try {
       await appendFile(SCRUM_REVIEWS_JSONL, JSON.stringify(row) + "\n");
     } catch (e) {
       console.error(`[scrum] failed to append scrum_reviews.jsonl: ${(e as Error).message}`);
     }
+
+    // Route the accepted review through llm_team's fact extractor so
+    // its entities + relationships land in audit_facts.jsonl alongside
+    // inference-side extractions. Same index, two sources. Tagged
+    // source:"scrum_review" + scrum_master_reviewed:true so downstream
+    // queries can filter by provenance. Reviews shorter than 120
+    // chars are skipped — they're usually one-liners ("LGTM") with
+    // no extractable knowledge.
+    if (accepted.length >= 120 && process.env.LH_SCRUM_SKIP_EXTRACT !== "1") {
+      try {
+        const { extractFacts } = await import("../../auditor/fact_extractor.ts");
+        const ex = await extractFacts(accepted);
+        if (!ex.error || ex.entities.length + ex.facts.length > 0) {
+          const factRow = {
+            pr_number: 0,                   // scrum runs outside a PR scope
+            file: rel,
+            head_sha: "",                    // no SHA scope; scope is the file+timestamp
+            extracted_at: ex.extracted_at,
+            extractor: ex.extractor_model,
+            verifier: ex.verifier_model,
+            llm_team_run_id: ex.llm_team_run_id ?? null,
+            facts: ex.facts,
+            entities: ex.entities,
+            relationships: ex.relationships,
+            verification_preview: ex.verification.slice(0, 400),
+            schema_version: 2,
+            source: "scrum_review",
+            scrum_master_reviewed: true,
+          };
+          const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
+          await appendFile(AUDIT_FACTS_JSONL, JSON.stringify(factRow) + "\n");
+        }
+      } catch (e) {
+        console.error(`[scrum] fact extraction failed for ${rel}: ${(e as Error).message}`);
+      }
+    }
   }
 
   return review;