arXiv AI October 7, 2026 By Riccardo Barbano, Vincent Pauline, Runchang Li, George Webber, Alexander Denker, \v{Z}eljko Kereta, Stefan Bauer, Francisco Vargas, Esmeralda S. Whitammer

One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control

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The paper introduces Coordinated Multi-Agent Diffusion Steering (CMDS), a framework that reuses independently trained diffusion models as generative primitives and learns a control policy to coordinate their reverse processes. By framing coordination as a stochastic optimal control problem, CMDS balances an assembly-level reward with adherence to pretrained dynamics, enabling the learned control to generalise across new tasks. Experiments demonstrate CMDS’s ability to recover target distributions, satisfy spatial constraints, and separate sources from degraded mixtures in applications such as maze navigation, robot planning, and text-conditioned human motion.

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