Year: 2025
Type: article
Abstract: Supersession Note — May 2026 This December 2025 paper is a developmental predecessor to the current identity-persistence stack. It remains useful as an early architecture-level statement of coherence-bound memory, PAS_h-governed retrieval, drift-bounded updating, deterministic forgetting, and identity-level memory stability. It is not the canonical statement of the formal program. The current canonical framework is narrower and more precise: identity persistence under transformation requires an explicitly declared identity-bearing unit, admissible transformations, admissible redescription, identity-relevant quotient, continuation structure, invariant basis, governance order, and drift bounds. Memory systems are now treated as downstream implementations or applications of that identity-persistence regime. Claims in this record concerning uniqueness, biological generality, neuroscience, entropy, or completion of prior memory theory should be read as developmental framing unless re-derived under the current forcing, calculus, mathematical theory, and coding-theorem stack. 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.
Author: Bostick, Devin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108751998
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