GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems

2026-02-17

AI Summary

This work addresses the challenge of cooperative decision-making in partially observable multi-agent systems, where agents’ local observations often hinder accurate inference of the global state. To overcome this limitation, we propose GlobeDiff, the first approach to leverage diffusion models for global state reconstruction in such settings. GlobeDiff formulates global state estimation as a multimodal diffusion process that generates high-fidelity approximations of the global state using only local observations, bypassing the constraints of conventional belief propagation and explicit communication protocols. The method is accompanied by theoretical guarantees on estimation error bounds. Empirical evaluations across diverse environments demonstrate that GlobeDiff significantly outperforms existing approaches, achieving both high accuracy and robustness.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Diffusion Models for Vision
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 achieves superior performance and is capable of accurately inferring the global state.

Problem

• partial observability
• multi-agent systems
• global state inference
• state estimation
• coordination

Innovation

• partial observability
• multi-agent systems
• state diffusion
• global state inference
• multi-modal diffusion