Forgetting as Coherence Maintenance Toward Regulatory Architecture for AI Memory Systems

Abstract The dominant engineering approach to AI memory treats forgetting as a failure mode to be eliminated by scaling context windows and retrieval accuracy. This paper proposes a reframe: forgetting is a regulatory function evolved to maintain coherence under metabolic, structural, informational, and boundary constraints. We reconstruct the Ebbinghaus forgetting curve and its companion phenomena—serial position effects, spacing effects, sleep-dependent consolidation—as solutions to the coherence-maintenance problem, not biological limitations. Mapping these phenomena onto current AI memory failures (Lost in the Middle, catastrophic forgetting, hallucination drift) reveals that disabling regulatory decay channels produces predictable decoherence pathologies. We introduce a four-channel diagnostic grammar (metabolic, structural, informational, boundary) that transforms vague symptoms into testable engineering targets. Design principles for principled forgetting are proposed, including a Conservation Principle that protects safety-critical traces from decay. The paper extends the ρ/C minimal mathematics of coherence (Smith 2026c) into the temporal domain: if ρ measures static coherence, the Ebbinghaus curve governs its dynamics under metabolic constraints. We position human gatekeepers as metabolic nodes requiring scheduled rest, extending prior work on epistemic hygiene (Smith 2026a) into human-AI interface design. Falsifiable predictions and experimental protocols are provided. Keywords: AI memory; forgetting as feature; regulatory homeostasis; Ebbinghaus curve; Lost in the Middle; catastrophic forgetting; coherence maintenance; ρ/C mathematics; epistemic hygiene; SHAI