Deterministic Memory Architecture_ A Coherence-Bound Model for Cross-Context Reasoning

Abstract Memory in computational and biological systems is often treated as passive storage rather than a coherence-regulated dynamic process. Existing approaches—including raw buffers, vector similarity retrieval, statistical models, and classical psychological frameworks—lack a scalar invariant for determining when a memory trace remains valid, when it should be updated, and when it must be forgotten. This paper introduces a deterministic memory architecture grounded in two invariants: PAS_h, a harmonic coherence metric, and ΔPAS_zeta, a drift measure governing temporal divergence. Memory is formulated as a three-layer system—episodic, semantic, and identity—each regulated by explicit coherence thresholds and drift bounds. Retrieval becomes a legality operation rather than similarity search, updating follows echo-based stabilization, and forgetting occurs deterministically once drift exceeds a defined corridor. This framework unifies classical models (episodic/semantic distinctions, working memory), neural network theories (attractors, complementary learning systems), reconsolidation dynamics, and entropy-minimization accounts under a single invariant-based law. The result is a general, implementable model of memory as coherence preserved through time, suitable for deterministic inference substrates and long-horizon reasoning systems.