规模化团队还是延长时间？LLM多智能体系统中支持记忆的终身学习

摘要

大型语言模型（LLM）多智能体系统可通过两个不同维度实现扩展：增加智能体数量，以及通过长期经验积累进行能力提升。尽管已有研究分别探讨过这两个维度，但它们在现实成本约束下的相互作用仍不明确。本文提出一种多智能体系统的概念化扩展视角，同时考量团队规模与终身学习能力，并研究内存设计如何在这一框架中发挥作用。为此，我们提出LLMA-Mem——一种支持灵活内存拓扑结构的LLM多智能体终身记忆框架。我们在MultiAgentBench平台上针对编程、科研和数据库环境对该框架进行评估。实验结果表明，LLMA-Mem在控制成本的同时，能持续提升系统在长周期任务中的表现优于基线方法。进一步分析揭示出非单调的扩展规律：扩大团队规模并不总能带来更好的长期性能，当记忆系统能有效支持经验复用时，较小规模的团队反而可能表现更优。这些发现表明，内存设计可作为实现多智能体系统持续高效扩展的实践路径。

English

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 LLMA-Mem, a lifelong memory framework for LLM multi-agent systems under flexible memory topologies. We evaluate LLMA-Mem on 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.

规模化团队还是延长时间？LLM多智能体系统中支持记忆的终身学习

Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems

摘要

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