团队规模还是时间规模？基于内存的持续学习在大语言模型多智能体系统中的应用
Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
AI总结：
本文研究了大语言模型多智能体系统在团队规模和持续学习能力上的协同优化，提出LLMA-Mem框架，通过灵活内存拓扑提升长期性能并降低成本。
摘要：
大型语言模型（LLM）多智能体系统可以沿着两个不同的维度扩展：通过增加智能体数量或通过积累经验提升能力。尽管先前研究分别探讨了这两个维度，但在现实成本约束下它们的相互作用仍不明确。本文引入了一个多智能体系统的概念扩展视角，共同考虑团队规模和持续学习能力，并研究内存设计如何共享这一景观。为此，我们提出了LLMA-Mem，一种适用于灵活内存拓扑的LLM多智能体系统的持续记忆框架。我们在MultiAgentBench上评估了LLMA-Mem，覆盖编程、研究和数据库环境。实证结果显示，LLMA-Mem在减少成本的同时，始终优于基线方法，提升了长期表现。进一步分析揭示了非单调的扩展景观：较大的团队并不总是产生更好的长期表现，而当内存更好地支持经验重用时，较小的团队可以胜过较大的团队。这些发现将内存设计定位为一种更有效、更高效的多智能体系统扩展路径。
英文摘要：
Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time. Although prior work has studied these dimensions separately, their interaction under realistic cost constraints remains unclear. In this paper, we introduce a conceptual scaling view of multi-agent systems that jointly considers team size and lifelong learning ability, and we study how memory design shares this landscape. To this end, we propose \textbf{LLMA-Mem}, a lifelong memory framework for LLM multi-agent systems under flexible memory topologies. We evaluate LLMA-Mem on \textsc{MultiAgentBench} across coding, research, and database environments. Empirically, LLMA-Mem consistently improves long-horizon performance over baselines while reducing cost. Our analysis further reveals a non-monotonic scaling landscape: larger teams do not always produce better long-term performance, and smaller teams can outperform larger ones when memory better supports the reuse of experience. These findings position memory design as a practical path for scaling multi-agent systems more effectively and more efficiently over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
