Audit pipeline PR #9: determinism + fact extraction + verifier gate + KB stats #9
@ -112,9 +112,19 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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{ role: "system", content: systemMsg },
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{ role: "system", content: systemMsg },
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{ role: "user", content: userMsg },
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{ role: "user", content: userMsg },
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],
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],
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max_tokens: 3000,
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// Deterministic classification mode — temp=0 is greedy-sample,
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temperature: 0.2,
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// so identical input → identical output on the same model
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think: true, // T3 overseer should reason — JSON shape is still required
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// version. think=false disables the reasoning trace that was
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// letting variable prose leak into the classification output
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// and inflate the audit_lessons signature set (observed as
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// sig_count creep across the 9-run empirical test).
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//
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// max_tokens tightened to 1500 — the structured JSON response
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// fits comfortably in 1500 tokens for typical PRs (~7 claims);
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// the old 3000 just gave the model room to wander.
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max_tokens: 1500,
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temperature: 0,
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think: false,
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}),
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}),
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signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
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signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
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});
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});
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@ -28,7 +28,7 @@ const AUDIT_LESSONS = `${REPO}/data/_kb/audit_lessons.jsonl`;
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const VERDICTS_DIR = `${REPO}/data/_auditor/verdicts`;
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const VERDICTS_DIR = `${REPO}/data/_auditor/verdicts`;
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const POLL_INTERVAL_MS = 5_000;
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const POLL_INTERVAL_MS = 5_000;
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const AUDIT_TIMEOUT_MS = 180_000;
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const AUDIT_TIMEOUT_MS = 180_000;
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const RUNS = 9;
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const RUNS = Number(process.env.LH_AUDIT_RUNS ?? 9);
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const TARGET_PR = Number(process.env.LH_AUDIT_PR ?? 8);
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const TARGET_PR = Number(process.env.LH_AUDIT_PR ?? 8);
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async function sh(cmd: string): Promise<{ stdout: string; stderr: string; code: number }> {
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async function sh(cmd: string): Promise<{ stdout: string; stderr: string; code: number }> {
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