[2604.03295] Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems

Computer Science > Multiagent Systems arXiv:2604.03295 (cs) [Submitted on 27 Mar 2026] Title:Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems Authors:Shanglin Wu, Yuyang Luo, Yueqing Liang, Kaiwen Shi, Yanfang Ye, Ali Payani, Kai Shu View a PDF of the paper titled Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems, by Shanglin Wu and 6 other authors View PDF HTML (experimental) Abstract: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 m...