Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments

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 u

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ethics.ai (2 April 2026), “Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments,” evidence record 6425, https://ethics.ai/record/6425 (originally published by arXiv).

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