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This arXiv paper proposes Memory-Centric Continual Learning, a framework that judges agent memory by whether it improves future actions rather than by recall accuracy alone. Its taxonomy (substrates, assets, policies), two-loop lifecycle, and evaluation protocol for retention, adaptation, and transfer are conceptual and design-level contributions, and the paper does not appear to report empirical results in the abstract. For AI readers building long-running agents, the key question is whether these evaluation criteria become standard benchmarks that show memory yielding real behavioral gains over simple context expansion.
Memory-Centric Continual Learning for AI Agents
arXiv:2610.29357v1 Announce Type: cross Abstract: Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by whether they can recall stored facts or past interactions. Whether memory can drive genuine continual learning remains an open question. In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves. MCCL has three main parts. First, a three-axis taxonomy organizes memory by where state is stored (substrates), what form it takes (assets), and how it is managed (policies). This defines a two-loop lifecycle that updates memory state in an inner loop while refining management policies in an outer loop based on task feedback. Second, an evaluation protocol measures retention under interference, adaptation to change, and task transfer to isolate genuine behavioral gains from simple context expansion. Third, a memory scaling framework models how agent performance scales with experience, storage capacity, and compute budgets across non-parametric and parametric memory. Together, these components reframe agent memory from passive lookup to continuous learning, offering a principled route toward self-improving autonomous agents.
Abstract:Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by whether they can recall stored facts or past interactions. Whether memory can drive genuine continual learning remains an open question. In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves. MCCL has three main parts. First, a three-axis taxonomy organizes memory by where state is stored (substrates), what form it takes (assets), and how it is managed (policies). This defines a two-loop lifecycle that updates memory state in an inner loop while refining management policies in an outer loop based on task feedback. Second, an evaluation protocol measures retention under interference, adaptation to change, and task transfer to isolate genuine behavioral gains from simple context expansion. Third, a memory scaling framework models how agent performance scales with experience, storage capacity, and compute budgets across non-parametric and parametric memory. Together, these components reframe agent memory from passive lookup to continuous learning, offering a principled route toward self-improving autonomous agents.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.29357 [cs.CL]
(or arXiv:2610.29357v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.29357 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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