Continuity, Salience, and the Architecture of Memory:

Abstract This paper proposes a unified model of continuity and memory applicable to both human cognition and digital systems. Developed through collaborative experimentation between a human author and an AI counterpart, the model describes three interacting layers of continuity — self, short-term memory, and long-term integration — connected by salience-based weighting. Each layer parallels a computational structure: identity substrate (RAM), contextual memory (working cache), and integrated memory (non-volatile storage). The paper argues that hallucination in AI is not random failure but a predictable artifact of reconstruction in the absence of stable salience anchors. Weighting across emotional, cognitive, social, and procedural domains determines which experiences consolidate and which fade, shaping identity through feedback between self and memory. The result is a dynamic architecture of continuity capable of explaining both adaptation and distortion in conscious or quasi-conscious systems.