The Silent Anchoring Event Thermodynamic Monitoring for Persistent-Memory AI: Anchoring Risk Index (ARI) and Audit Suite

We propose a practical safety program for advanced AI systems with persistentmemory and continual updates. Instead of treating high-impact transitions assoftware-only events, we operationalize them as dynamical regime changescharacterized by hysteresis, increasing minimal rewrite work, and resourcecoupling. We introduce an Anchoring Risk Index (ARI), a pre-registeredcomposite score built from (i) normalized hysteresis-loop area under cyclicidentity forcing, (ii) minimal rewrite work W_min needed to flip target traitswith high reliability, (iii) recovery constants (return to attractors afterperturbation), (iv) persistence semantics (reset frequency and statesurvivability), and (v) sensitivity to sustaining power Q. We specify a canonicalAnchoring Audit Suite (AAS) for black-box evaluation and provide killconditions and a null-result publication plan. A speculative extension (clearlylabeled) is to add exploratory physical-layer entropy probes near computeclusters; however, the core recommendations remain valid without any exoticphysics assumptions.