让多智能体系统记住过往任务，自动提升流程执行效率。
LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation
- 将任务轨迹拆成可复用的记忆模块，灵活分配给不同智能体。
- 在OfficeBench上，调度器记忆提升任务分解能力，细粒度记忆提高执行准确率。
- 小模型团队也能通过记忆弥补性能差距，适合自动化流程场景。
我们提出LEGOMem，一种用于多智能体大语言模型系统在工作流自动化中的模块化过程记忆框架。LEGOMem将过往任务轨迹分解为可复用的记忆单元，并灵活分配给编排器和任务智能体，以支持规划与执行。通过以LEGOMem为视角的系统性研究，我们探讨了多智能体系统中记忆的放置位置、检索方式及受益智能体。在OfficeBench基准上的实验表明，编排器记忆对有效任务分解与委派至关重要，而细粒度的智能体记忆则提升了执行准确性。我们发现，即使由较小的语言模型组成的团队，也能通过利用先前执行轨迹显著提升性能，缩小与更强智能体的差距。这些结果使LEGOMem既成为增强型智能体系统的实用框架，也成为研究多智能体工作流中记忆设计的工具。
原文摘要 · Abstract (English)
We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory units and flexibly allocates them across orchestrators and task agents to support planning and execution. To explore the design space of memory in multi-agent systems, we use LEGOMem as a lens and conduct a systematic study of procedural memory in multi-agent systems, examining where memory should be placed, how it should be retrieved, and which agents benefit most. Experiments on the OfficeBench benchmark show that orchestrator memory is critical for effective task decomposition and delegation, while fine-grained agent memory improves execution accuracy. We find that even teams composed of smaller language models can benefit substantially from procedural memory, narrowing the performance gap with stronger agents by leveraging prior execution traces for more accurate planning and tool use. These results position LEGOMem as both a practical framework for memory-augmented agent systems and a research tool for understanding memory design in multi-agent workflow automation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。