@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://aclanthology.org/2025.acl-short.33/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
%A Fang, Runnan
%A Wang, Xiaobin
%A Liang, Yuan
%A Qiao, Shuofei
%A Wu, Jialong
%A Xi, Zekun
%A Zhang, Ningyu
%A Jiang, Yong
%A Xie, Pengjun
%A Huang, Fei
%A Chen, Huajun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F fang-etal-2025-synworld
%X 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.
%R 10.18653/v1/2025.acl-short.33
%U https://aclanthology.org/2025.acl-short.33/
%U https://doi.org/10.18653/v1/2025.acl-short.33
%P 437-448
Markdown (Informal)
[SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement](https://aclanthology.org/2025.acl-short.33/) (Fang et al., ACL 2025)
ACL
- Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. 2025. SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 437–448, Vienna, Austria. Association for Computational Linguistics.