@inproceedings{nakatsuji-etal-2025-act,
    title = "{ACT}: Knowledgeable Agents to Design and Perform Complex Tasks",
    author = "Nakatsuji, Makoto  and
      Tateishi, Shuhei  and
      Fujiwara, Yasuhiro  and
      Matsumoto, Ayaka  and
      Nomoto, Narichika  and
      Sato, Yoshihide",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.823/",
    pages = "16831--16861",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models enhance collaborative task execution in multi-agent systems. Current studies break complex task into manageable tasks, but agents lack understanding of the overall task and how others approach their tasks, hindering synergy and integration.We propose a method called knowledgeable \textbf{ \textit{A}}gents to design and perform \textbf{ \textit{C}}omplex \textbf{ \textit{T}}asks (ACT), where: (1) Agents independently manage their knowledge and tasks while collaboratively design the complex task into a more comprehensible form. In parallel, each agent also acquires knowledge of others, defined as a structured description of how other agents approach their tasks based on the agent{'}s own task resolution. (2) Each agent updates its knowledge and refines its task through interactions with others. By referencing structured knowledge, they effectively integrate their tasks to collaboratively solve the complex task.Three evaluations including creative writing and tool utilization, show that ACT accurately outperforms existing methods in solving complex tasks."
}