发表机构
National Engineering Research Center of Software Engineering, Peking University; School of Computer Science, Peking University; Key Laboratory of High Confidence Software Technologies, Ministry of Education; Center on Frontiers of Computing Studies, Peking University（北京大学软件工程国家工程研究中心; 北京大学计算机学院; 教育部高可信软件技术重点实验室; 北京大学计算前沿研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GenMem提出生成式符号寻址的长期记忆管理方法，通过符号标识符分解稀疏记忆空间，在多智能体框架中实现自进化，在多个任务上优于基线。
AI 中文摘要
长期记忆通过跨任务保留经验和技能，并支持其在后续长时程决策中的检索、重用和修订，从而支撑LLM智能体的自进化。然而，现有的记忆管理方法仍局限于判别式检索，难以应对可复用经验稀疏、层次化和高度冗余的结构：只有一小部分、任务相关的轨迹和记忆值得保留、检索或修订。学习这些操作因稀疏、延迟和间接的任务级反馈而进一步复杂化，且在记忆生命周期中缺乏强监督。此外，持续的记忆进化引入了架构上的张力，即寻址不变性：存储的经验不断被修订，而学习到的检索策略所消费的寻址接口必须保持稳定。为解决这些问题，我们提出了GenMem，它将记忆管理重构为生成式符号寻址。其核心机制是符号标识符（SID），一个从笛卡尔积地址空间抽取的多级离散令牌元组，该空间使用不到一百个离散符号分解百万规模的稀疏记忆空间。记忆智能体学习生成SID，而非生成不断变化的原始内容，而记忆进化在固定地址重写负载，而不改变地址本身。在架构上，GenMem在多智能体框架内耦合了MemRetriever和MemEvolver，通过GRPO训练，并带有密集的过程和结果奖励以及双通道优化。在离线记忆进化下，涵盖ALFWorld、WebShop、多跳问答、医学推理和深度研究的实验，将GenMem与强记忆增强基线进行了比较...
英文摘要
Long-term memory supports the self-evolution of LLM agents by retaining experience and skills across tasks and enabling their retrieval, reuse, and revision in subsequent long-horizon decision-making. Yet existing memory management approaches remain limited to discriminative retrieval and to address the sparse, hierarchical, and highly redundant structure of reusable experience: only a small, task-dependent subset of trajectories and memories warrants retention, retrieval, or revision. Learning these operations is further complicated by sparse, delayed, and indirect task-level feedback, with weak supervision across the memory lifecycle. Moreover, continual memory evolution introduces an architectural tension as addressing invariance: stored experience is perpetually revised, yet the addressing interface consumed by learned retrieval policies must remain stable. To address, we present GenMem, which reformulates memory management as generative symbolic addressing. Its core mechanism is the Symbolic Identifier (SID), a multi-level discrete token tuple drawn from a Cartesian-product address space that factorizes a million-scale sparse memory space using fewer than one hundred discrete symbols. Instead of generating ever-changing raw content, the memory agent learns to generate SIDs, while memory evolution rewrites the payload at a fixed address without shifting the address itself. Architecturally, GenMem couples a MemRetriever and a MemEvolver within a multi-agent harness, trained via GRPO with dense process and outcome rewards with two-channels optimization. Under offline memory evolution, experiments spanning ALFWorld, WebShop, multi-hop QA, medical reasoning, and deep research evaluate GenMem against strong memory-augmented baselines...
