Memory-Centric Continual Learning for AI Agents

The paper introduces Memory-Centric Continual Learning (MCCL), arguing that agent memory should be judged by whether it improves future actions rather than by retrieval accuracy alone. It proposes a three-axis taxonomy (substrates, assets, policies), a two-loop lifecycle where memory state is updated in an inner loop and management policies are refined in an outer loop, an evaluation protocol measuring retention under interference, adaptation, and task transfer, and a memory scaling framework relating performance to experience, storage, and compute across parametric and non-parametric memory.

Why it matters

It reframes external memory for LLM agents from passive retrieval toward continual, behavior-improving learning, offering a structured vocabulary and evaluation approach for self-improving agents.

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Source claims not independently checked; the supplied text is an abstract and provides no empirical results to support the proposed framework.