@inproceedings{wang-etal-2025-megaagent,
    title = "{M}ega{A}gent: A Large-Scale Autonomous {LLM}-based Multi-Agent System Without Predefined {SOP}s",
    author = "Wang, Qian  and
      Wang, Tianyu  and
      Tang, Zhenheng  and
      Li, Qinbin  and
      Chen, Nuo  and
      Liang, Jingsheng  and
      He, Bingsheng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.259/",
    pages = "4998--5036",
    ISBN = "979-8-89176-256-5",
    abstract = "LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose \textit{MegaAgent}, a large-scale autonomous LLM-based multi-agent system. \textit{MegaAgent} generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, \textit{MegaAgent} demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, \textit{MegaAgent} demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in MAS."
}