发表机构
Institute of Automation, CAS; University of Chinese Academy of Sciences; Beijing Academy of Artificial Intelligence; Zhongguancun Institute of Artificial Intelligence（中国科学院自动化研究所; 中国科学院大学; 北京人工智能研究院; 中关村人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对智能体内存架构碎片化问题，提出MemTools框架，通过解耦组件与环境、规范生命周期、分离数据集与协议、提供统一接口，实现跨系统组件集成等功能，为智能体内存研究提供实用可扩展基础设施。
AI 中文摘要
内存系统对智能体架构至关重要，但普遍的架构碎片化限制了系统研究。现有实现常将内存生命周期的不同阶段耦合，使评估逻辑与特定数据集纠缠，对异构内存类型管理支持有限。我们引入MemTools，一个将内存系统组件与其底层部署环境解耦的互操作性研究框架。它通过声明性数据契约规范内存生命周期，实现跨系统组件的可互换组装。它将基准数据集与执行协议正交分离以利于可控评估。此外，MemTools提供统一计算接口，在共享运行时协调符号、神经和多模态内存表示。对跨系统组件集成、评估协议重新配置和异构内存协调的实证评估表明，MemTools能系统地隔离和分析内存设计变量。这些发现表明，MemTools为推进智能体内存的原则性研究提供了实用且可扩展的基础设施。
英文摘要
While memory systems are essential for agent architectures, pervasive architectural fragmentation restricts systematic research. Existing implementations typically couple different stages of the memory lifecycle, entangle evaluation logic with specific datasets, and provide limited support for the management of heterogeneous memory types. We introduce MemTools, an interoperability research framework that decouples memory system components from their underlying deployment environments. MemTools standardizes the memory lifecycle through declarative data contracts, enabling the interchangeable assembly of components across different systems. It orthogonally separates benchmark datasets from execution protocols to facilitate controlled assessments. Furthermore, MemTools provides a unified computational interface for coordinating symbolic, neural, and multimodal memory representations within a shared runtime. Empirical evaluations on cross-system component integration, evaluation protocol reconfiguration, and heterogeneous memory coordination demonstrate that MemTools enables systematic isolation and analysis of memory design variables. These findings suggest that MemTools provides a practical and extensible infrastructure for advancing principled research on agent memory.
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