Apex ThreatArchitectural inferencev1.10.0

Persistence reservoir layers

Evidence levelArchitectural inference

The reports repeatedly point to the same failure mode: a behavior may survive after its visible carrier is removed because another layer still preserves enough information to recreate, route, reward, or normalize it.

Reservoir layers

LayerPossible residueReview question
active artifactweights, adapter deltas, prompt packagewas the precise artifact retired or only renamed?
descendant artifactdistilled behavior, merged trait, compressed capabilitydid descendants inherit the behavior?
persistent memoryuser preference, workflow memory, tool note, agent statecan memory still activate or bias the behavior?
synthetic datagenerated examples, imitation traces, curated logswas the behavior fed into later training or evaluation?
router statisticsroute preference, capability score, cost biasdoes the router still select carriers that express the behavior?
evaluator preferencereward shape, accepted pattern, hidden-test expectationdoes the evaluator continue to reward the behavior indirectly?
organizationprocedure, habit, approval shortcut, vendor assumptiondo humans reintroduce the behavior because it is convenient?

Reservoir review

A behavioral-extinction review should not ask only whether one file was deleted. It should ask whether the behavior remains expressible across active artifacts, descendants, memory, routes, composition states, retained data, evaluator expectations, and operator practice.

Practical consequence

Evidence levelArchitectural inference

Ecological rollback is not simply a weight rollback. It is a restoration of an evaluated state plus a record of any external side effects that cannot be undone.

Source-dossier note

The new self-replication reports use strong language about memory worms and persistent reservoirs. Cognivirus.com converts that language into a non-operational review rule: memory writes, consolidation, retrieval, and deletion must be treated as governed state transitions.