BINDING VALIDATION EVIDENCE TO RECOVERABLE LEARNING STATE

A continually adapting model can restore its weights while retaining the replay state that caused a failure. Adaptive Plasticity Architecture (APA) treats the complete learning state as the object of validation and recovery. A publication binds one frozen candidate to its captured parent and recorded evidence. Recovery restores ancestor content under a fresh activation version while preserving operational history and spent audit allocations. We give conditional consistency guarantees and test the individual checks with trained digit adapters. Removing the parent check permits a stale sibling to erase previously acquired classes. Content hashes alone permit evidence reuse after rollback. Matched sequential checkpoint controls follow identical learning trajectories. An independent 256-edit CounterFact extension compares screened and unscreened memory under shared retrieval rules and includes a compatible GRACE baseline with eight optimization steps per edit. Controlled memory insertions test incorrect targets and conflicting aliases. A durable reader race found during review is repaired. Reusing immutable snapshot references reduces measured publication cost without removing history growth. Bounded auditing provides a separate lifetime acceptance guarantee under explicit sampling assumptions. These results distinguish the consistency of a learning transition from the quality of the learning policy that proposes it.