Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments

2026-04-02

AI Summary

This work addresses the challenge of catastrophic forgetting and the difficulty in balancing old and new knowledge in autonomous AI agents during continual learning. The authors propose the Adaptive Memory Crystallization (AMC) architecture, which uniquely models memory stability as a continuous liquid–glass–crystal phase transition governed by an Itô stochastic differential equation, coupled with multi-objective utility signals to regulate experience transfer. Theoretical analysis yields a closed-form Beta stationary distribution, rigorously establishing convergence guarantees, error bounds, and memory capacity limits. Empirical evaluations demonstrate that AMC achieves 34–43% improvement in forward transfer, reduces forgetting by 67–80%, and decreases memory footprint by 62% across benchmarks including Meta-World MT50, a 20-task Atari sequence, and MuJoCo environments.

Abstract

Autonomous AI agents operating in dynamic environments face a persistent challenge: acquiring new capabilities without erasing prior knowledge. We present Adaptive Memory Crystallization (AMC), a memory architecture for progressive experience consolidation in continual reinforcement learning. AMC is conceptually inspired by the qualitative structure of synaptic tagging and capture (STC) theory, the idea that memories transition through discrete stability phases, but makes no claim to model the underlying molecular or synaptic mechanisms. AMC models memory as a continuous crystallization process in which experiences migrate from plastic to stable states according to a multi-objective utility signal. The framework introduces a three-phase memory hierarchy (Liquid--Glass--Crystal) governed by an It\^o stochastic differential equation (SDE) whose population-level behavior is captured by an explicit Fokker--Planck equation admitting a closed-form Beta stationary distribution. We provide proofs of: (i) well-posedness and global convergence of the crystallization SDE to a unique Beta stationary distribution; (ii) exponential convergence of individual crystallization states to their fixed points, with explicit rates and variance bounds; and (iii) end-to-end Q-learning error bounds and matching memory-capacity lower bounds that link SDE parameters directly to agent performance. Empirical evaluation on Meta-World MT50, Atari 20-game sequential learning, and MuJoCo continual locomotion consistently shows improvements in forward transfer (+34--43\% over the strongest baseline), reductions in catastrophic forgetting (67--80\%), and a 62\% decrease in memory footprint.

Problem

• catastrophic forgetting
• continual learning
• memory consolidation
• autonomous AI agents
• dynamic environments

Innovation

• Adaptive Memory Crystallization
• Continual Reinforcement Learning
• Stochastic Differential Equation
• Memory Consolidation
• Catastrophic Forgetting