Close the HarnessX self-improving loop without an agent in the loop. Every scan
now feeds its verifier outcomes back into the next scan's ranking:
- New `calibrate` stage (after rank, before findings) loads a persistent ledger
of scoped false-positive learnings and demotes every previously-rejected scope
(`calibrate_rankings`), writing `calibration/applied_calibrations.json`.
- New `record_calibration` stage (after verify) converts this run's non-accepted
verifier outcomes into scoped `FalsePositiveLearning` records
(`learnings_from_verifications`) and merges them into
`.openultrasast/calibration/false_positive_learnings.json`, de-duplicated by
finding ID so a repeat rejection does not compound without bound.
- Lower the calibration floor (MIN_CALIBRATED_PRIORITY = 0.1) so accumulated
rejections can demote a scope below the natural ranking minimum.
Demotion is scoped and reversible: a rejection in one path never touches an
accepted finding in another, and priority is demoted rather than the finding
deleted so the audit trail explains every downgrade. First run is a no-op
(empty ledger), so existing single-run behavior is unchanged.
Adds tests/test_pipeline_calibration.py (two-run demotion, order-independent),
documents the realized loop in the README, and checks off the
standard_security_harness milestone in tasks.md.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
