CoBel-World: Harnessing LLM Reasoning to Build a Collaborative Belief World for Optimizing Embodied Multi-Agent Collaboration

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 open-world task knowledge into structured beliefs via a symbolic belief language, 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 22-60% 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.

Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction

Accurate prediction of molecular properties is critical in the field of drug discovery. However, existing methods do not fully consider the fact that molecules in the real world usually possess multiple property labels, and complex high-order relationships may exist among these labels. Therefore, molecular representation learning models should generate differential molecular representations that consider multi-granularity correlation information among tasks. To this end, our research introduces a Hierarchical Prompted Molecular Representation Learning Framework (HiPM), which enhances the differential expression of tasks in molecular representations through task-aware prompts, and utilizes shared information among labels to mitigate negative transfer between different tasks. HiPM primarily consists of two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). The MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atomic and motif levels, while the TAP uses agglomerative hierarchical clustering to build a prompt tree that reflects the affinity and distinctiveness of tasks, enabling the model to effectively handle the complexity of multi-label property predictions. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a new perspective on multi-label molecular representation learning.