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 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 utili...
| Authors | Rajat Khanda, Mohammad Baqar, Sambuddha Chakrabarti et al. |
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| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 13 June 2026 |
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