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[2602.15776] GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems
Summary
The paper presents GlobeDiff, a novel algorithm addressing partial observability in multi-agent systems by utilizing a state diffusion process to enhance global state inference.
Why It Matters
In multi-agent systems, effective coordination is hindered by partial observability. GlobeDiff offers a solution that improves decision-making by accurately inferring global states, which is crucial for applications in robotics, AI agents, and collaborative systems.
Key Takeaways
- GlobeDiff addresses the limitations of existing belief-based and communication methods in multi-agent systems.
- The algorithm formulates state inference as a multi-modal diffusion process, enhancing accuracy.
- Experimental results indicate that GlobeDiff significantly outperforms traditional methods in state estimation.
Computer Science > Artificial Intelligence arXiv:2602.15776 (cs) [Submitted on 17 Feb 2026] Title:GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems Authors:Yiqin Yang, Xu Yang, Yuhua Jiang, Ni Mu, Hao Hu, Runpeng Xie, Ziyou Zhang, Siyuan Li, Yuan-Hua Ni, Qianchuan Zhao, Bo Xu View a PDF of the paper titled GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems, by Yiqin Yang and 10 other authors View PDF HTML (experimental) Abstract:In the realm of multi-agent systems, the challenge of \emph{partial observability} is a critical barrier to effective coordination and decision-making. Existing approaches, such as belief state estimation and inter-agent communication, often fall short. Belief-based methods are limited by their focus on past experiences without fully leveraging global information, while communication methods often lack a robust model to effectively utilize the auxiliary information they provide. To solve this issue, we propose Global State Diffusion Algorithm~(GlobeDiff) to infer the global state based on the local observations. By formulating the state inference process as a multi-modal diffusion process, GlobeDiff overcomes ambiguities in state estimation while simultaneously inferring the global state with high fidelity. We prove that the estimation error of GlobeDiff under both unimodal and multi-modal distributions can be bounded. Extensive experimental results demonstrate that GlobeDiff achiev...
