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Memory-Centric Continual Learning (MCCL): Memory-Driven Continual Learning for AI Agents

The paper proposes Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves an agent's subsequent actions rather than by fact retrieval. It includes a three-axis taxonomy (organizing memory by storage location, form, and management approach), a two-loop lifecycle, an evaluation protocol, and a memory scaling framework.

Part of the event: Proposes MCCL Memory-Centered Continual Learning Framework

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