Persistent Memory Through Triple-Loop Consolidation in a Non-Gradient Dissipative Cognitive Architecture

TL;DR

This paper introduces Deep Memory, a novel non-gradient persistent memory mechanism for dissipative cognitive architectures, utilizing a triple-loop consolidation cycle to maintain context-specific memory despite continuous energy-driven state resets.

Contribution

The paper presents Deep Memory, a new non-gradient memory method that enables persistent, context-specific memory in dissipative systems through a triple-loop consolidation process and expert routing.

Findings

Deep Memory achieves high recall accuracy (R=0.984) in simulations.

Discrete expert routing is necessary for specialization and effective memory.

Deep Memory outperforms non-gradient baselines like Hopfield and ESN.

Abstract

Dissipative cognitive architectures maintain computation through continuous energy expenditure, where units that exhaust their energy are stochastically replaced with fresh random state. This creates a fundamental challenge: how can persistent, context-specific memory survive when all learnable state is periodically destroyed? Existing memory mechanisms -- including elastic weight consolidation, synaptic intelligence, and surprise-driven gating -- rely on gradient computation and are inapplicable to non-gradient dissipative systems. We introduce Deep Memory (DM), a non-gradient persistent memory mechanism operating through a triple-loop consolidation cycle: (1) recording of expert-specific content centroids, (2) seeding of replaced units with stored representations, and (3) stabilization through continuous re-entry. We demonstrate that discrete expert routing via Mixture-of-Experts (MoE) gating is a causal prerequisite for DM, preventing centroid convergence that would render stored memories identical. Across ${\sim}970$ simulation runs spanning thirteen experimental blocks: (i) discrete routing is causally necessary for specialization ($\text{MI}=1.10$ vs. $0.001$; $n=91$); (ii) DM achieves $R=0.984$ vs. $0.385$ without memory ($n=16$); (iii) continuous seeding reconstructs representations after interference ($R_\mathrm{recon}=0.978$; one-shot fails; $n=30$); (iv) the mechanism operates within a characterized $(K,p)$ envelope ($n=350$); (v) recording $\times$ seeding is the minimal critical dyad ($n=40$); (vi) DM outperforms non-gradient baselines (Hopfield, ESN) under matched turnover ($n=370$). These results establish DM as a falsifiable mechanism for persistent memory in non-gradient cognitive systems, with functional parallels to hippocampal consolidation.