2025/04/04 by Runnan Fang, Xiaobin Wang, Fang, Runnan +19 · 3 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multi-Agent Systems and Negotiation #Multiagent Systems (cs.MA) #Robotics and Automated Systems #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2504.03561
openalex publication_date 2025/04/04 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28
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. Code is available at https://github.com/zjunlp/SynWorld.