标题： G-内存：多智能体系统的分层内存追踪 显示英文标题

标题： G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems

摘要： 大型语言模型（LLM）驱动的多智能体系统（MAS）已经展示了远超单个LLM代理的认知和执行能力，但它们的自我进化能力仍受到记忆架构发展不足的限制。 经过仔细检查，我们惊讶地发现当前的MAS记忆机制（1）过于简单化，完全忽略了复杂的跨代理协作轨迹，并且（2）缺乏跨试验和特定代理的定制化，这与为单个代理开发的表达性记忆形成了鲜明对比。 为弥合这一差距，我们引入了G-Memory，这是一种受组织记忆理论启发的分层代理记忆系统，用于MAS，通过三层图层次结构管理长时间的MAS交互：洞见图、查询图和交互图。 当接收到新的用户查询时，G-Memory执行双向记忆遍历以检索既能使系统利用跨试验知识的$\textit{high-level, generalizable insights}$和能紧凑编码先前协作经验的$\textit{fine-grained, condensed interaction trajectories}$。 在任务执行过程中，整个层次结构通过同化新的协作轨迹而演变，促进代理团队的逐步进化。 在五个基准数据集、三种LLM主干网络和三种流行的MAS框架上的广泛实验表明，G-Memory分别将具身行动的成功率和知识问答的准确性提高了$20.89\%$和$10.12\%$，而无需对原始框架进行任何修改。 我们的代码可在https://github.com/bingreeky/GMemory获取。 显示英文摘要

摘要： Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customization, in stark contrast to the expressive memory developed for single agents. To bridge this gap, we introduce G-Memory, a hierarchical, agentic memory system for MAS inspired by organizational memory theory, which manages the lengthy MAS interaction via a three-tier graph hierarchy: insight, query, and interaction graphs. Upon receiving a new user query, G-Memory performs bi-directional memory traversal to retrieve both $\textit{high-level, generalizable insights}$ that enable the system to leverage cross-trial knowledge, and $\textit{fine-grained, condensed interaction trajectories}$ that compactly encode prior collaboration experiences. Upon task execution, the entire hierarchy evolves by assimilating new collaborative trajectories, nurturing the progressive evolution of agent teams. Extensive experiments across five benchmarks, three LLM backbones, and three popular MAS frameworks demonstrate that G-Memory improves success rates in embodied action and accuracy in knowledge QA by up to $20.89\%$ and $10.12\%$, respectively, without any modifications to the original frameworks. Our codes are available at https://github.com/bingreeky/GMemory.