Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents--a crucial capability for avoiding miscoordination and redundant communication under partial observable environments. Due to their strong planning and reasoning capabilities, large language models (LLMs) have emerged as promising autonomous agents for collaborative task solving. However, existing collaboration frameworks for LLMs overlook their reasoning potential for dynamic intent inference, and thus produce inconsistent plans and redundant communication, reducing collaboration efficiency. To bridge this gap, we propose CoBel-World, a novel framework that equips LLM agents with a Collaborative Belief World--an internal representation jointly modeling the physical environment and collaborators' mental states. CoBel-World enables agents to parse external open-world knowledge into structured beliefs via a symbolic belief representation module, and perform zero-shot Bayesian-style belief updates through LLM reasoning. This allows agents to proactively detect potential miscoordination (e.g., conflicting plans) and communicate adaptively. Evaluated on challenging embodied benchmarks (i.e., TDW-MAT and C-WAH), CoBel-World significantly reduces communication costs by 64-79% and improves task completion efficiency by 4-28% compared to the strongest baseline. Our results show that explicit, intent-aware belief modeling is essential for efficient and human-like collaboration in LLM-based multi-agent systems.

翻译：现实世界中有效的多智能体协作不仅需要精确的规划能力，更需要推理协作方意图的能力——这是在部分可观测环境下避免协调失误和冗余通信的关键能力。凭借其强大的规划与推理能力，大语言模型已成为解决协作任务的有前景的自主智能体。然而，现有的大语言模型协作框架忽视了其在动态意图推断方面的推理潜力，导致产生不一致的规划和冗余通信，从而降低了协作效率。为弥补这一差距，我们提出了CoBel-World框架，该框架通过为LLM智能体配备"协作信念世界"——一种联合建模物理环境与协作者心智状态的内部表征。CoBel-World使智能体能够通过符号化信念表征模块将外部开放世界知识解析为结构化信念，并借助LLM推理实现零样本贝叶斯式信念更新。这使得智能体能够主动检测潜在的协调失误（如冲突规划）并实现自适应通信。在具身智能基准测试（TDW-MAT与C-WAH）上的评估表明，相较于最强基线方法，CoBel-World显著降低了64-79%的通信成本，并将任务完成效率提升了4-28%。我们的研究结果表明，在基于大语言模型的多智能体系统中，显式的意图感知信念建模对于实现高效且类人的协作至关重要。