Evaluate incremental dependency-aware recomputation at enterprise scale

Determine whether incremental, dependency-aware recomputation that updates only affected Posture Tree paths reduces latency and memory use at enterprise scale for the Trustworthy AI Posture (TAIP) Assurance Engine.

Background

The paper evaluates TAIP primarily through full policy-triggered recomputation over retained RepoAudit evidence. Although the assurance architecture gives a theoretical bound for incremental updates, the experiments do not measure an implementation of selective, dependency-aware recomputation. The unresolved issue is whether identifying shared evidence and policy dependencies and updating only affected Posture Tree paths would yield meaningful reductions in latency and memory consumption when assurance is maintained at enterprise scale.