Multi-Agent Flow Matching with Decoupled Generative Guidance
DeGG-Flow is presented as a way to make multi-agent generative outputs satisfy hard coupled requirements, with guarantees.
The paper frames multi-agent generation as harder than ordinary generative modeling because constraints can depend on several agents at once. Its framework separates guidance inputs so each agent can act without relying on other agents’ simultaneously computed guidance. The authors give feasibility and finite-horizon convergence guarantees for shared and private requirements, plus a Wasserstein bound on distributional deviation. They test it on robot collaboration across a gap and multi-object scene generation, including team sizes not seen in training. ArXiv · AI/CL/LG's note
The paper frames multi-agent generation as harder than ordinary generative modeling because constraints can depend on several agents at once. Its framework separates guidance inputs so each agent can act without relying on other agents’ simultaneously computed guidance. The authors give feasibility and finite-horizon convergence guarantees for shared and private requirements, plus a Wasserstein bound on distributional deviation. They test it on robot collaboration across a gap and multi-object scene generation, including team sizes not seen in training. ArXiv · AI/CL/LG's note
score 4