@inproceedings{fang-etal-2025-synworld,
    title = "{S}yn{W}orld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement",
    author = "Fang, Runnan  and
      Wang, Xiaobin  and
      Liang, Yuan  and
      Qiao, Shuofei  and
      Wu, Jialong  and
      Xi, Zekun  and
      Zhang, Ningyu  and
      Jiang, Yong  and
      Xie, Pengjun  and
      Huang, Fei  and
      Chen, Huajun",
    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 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-short.33/",
    pages = "437--448",
    ISBN = "979-8-89176-252-7",
    abstract = "In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments."
}