Repeated State Detection and Escalation

To stop endless retry loops, repeated-state detection recognizes when an identical blocker signature appears across consecutive revisions and triggers an escalation to human review. This prevents the pipeline from continuously retrying the same blocking condition.

Exact-match fingerprinting is a technique that normalizes and records a unique signature of a blocker state. It detects repetition by comparing these fingerprints across consecutive revisions to see if the pipeline is stuck in an editor revision loop or holding-state workflow.

You escalate an editorial pipeline by using repeated-state detection to trigger escalation artifacts like lead-review.md when a blocker reoccurs after consecutive revisions. The article remains in a durable holding state to prevent regression into previous stages.

Yes, durable persistence of signature data and blocker metadata is required to track consecutive signatures across revisions. This holding-state flow ensures the workflow maintains history and prevents regression into previous stages during escalation.

The best way to verify exact-match behavior is by running the included tests that validate the detection helpers and signature normalization logic. These tests confirm that the fingerprinting mechanism accurately identifies repeated blocker states across revisions.