AI 的记忆不该只是“记住”，而该让它“变强”
现在的大模型智能体，权重固定、上下文有限，靠外挂记忆库只能“回忆”，不能“成长”。这篇论文提出一个反直觉的转向：记忆的价值不在它存了什么，而在它能不能让智能体下次做得更好。作者给出了一套三维分类法(存在哪、长什么样、怎么管)，外加一个双循环机制：内循环更新记忆内容，外循环根据任务反馈调整记忆管理策略，让记忆从被动检索变成主动学习。他们还设计了评测协议，专门区分“真变强”和“只是上下文变长”，并建立了记忆规模与性能的缩放规律。这不是你明天能用的产品，但它给“AI 能不能自我进化”这个根本问题，画出了一条可检验的路线。
📄 原文摘要(英文)
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.