让智能体学会自主管理记忆，动态调整增删改查操作。
AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation
- 将记忆操作拆解为创建、读取、更新、删除四类原子动作
- 在3个长序列任务上，80亿参数模型表现优于传统静态记忆方法
- 适合需要长期规划与复杂记忆的AI代理系统研发
赋予智能体记忆能力是解决现实世界长时序问题的关键。然而，现有大多数记忆机制依赖静态且人工设计的流程，限制了性能与泛化能力，亟需更灵活、可学习的记忆框架。本文提出AtomMem，将记忆管理重构为动态决策问题。通过将高层记忆过程分解为基本的原子CRUD（创建、读取、更新、删除）操作，将记忆工作流转化为可学习的决策过程。结合监督微调与强化学习，AtomMem训练出一个自主、任务对齐的策略，以适应特定任务需求。在3个长上下文基准上的实验表明，训练后的AtomMem-8B持续优于以往静态流程的记忆方法。对训练动态的分析显示，基于学习的设定使智能体能够发现结构化、任务对齐的记忆管理策略，相较预设流程具有显著优势。
原文摘要 · Abstract (English)
Equipping agents with memory is essential for solving real-world long-horizon problems. However, most existing agent memory mechanisms rely on static and hand-crafted workflows. This limits the performance and generalization ability of these memory designs, which highlights the need for a more flexible, learning-based memory framework. In this paper, we propose AtomMem, which reframes memory management as a dynamic decision-making problem. We deconstruct high-level memory processes into fundamental atomic CRUD (Create, Read, Update, Delete) operations, transforming the memory workflow into a learnable decision process. By combining supervised fine-tuning with reinforcement learning, AtomMem learns an autonomous, task-aligned policy to orchestrate memory behaviors tailored to specific task demands. Experimental results across 3 long-context benchmarks demonstrate that the trained AtomMem-8B consistently outperforms prior static-workflow memory methods. Further analysis of training dynamics shows that our learning-based formulation enables the agent to discover structured, task-aligned memory management strategies, highlighting a key advantage over predefined routines.
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