Horses for Courses: Role-Configuration-Driven Long-Horizon Stable Control for Multi-Agent Systems

Long-horizon stable control in multi-agent systems requires agents to maintain task performance while reacting to capability changes, disturbance propagation, and evolving system stability. Agents' functional contributions may change over time, requiring the controller to adapt its control emphasis according to the current role configuration of the team. We propose RoleFit, a role-configuration-driven method for long-horizon stable multi-agent control. RoleFit first constructs structured role evidence from local observations, executed actions, and coupled system conditions, and infers each agent's functional role as a Primary Contributor, Compensatory Contributor, or Limited Contributor. It then builds team-level role-topology evidence to reason about the current control mode: Balanced Control, Redistribution Control, or Stabilization Control. The selected mode is executed by a corresponding homogeneous role-conditioned policy, so that language-model reasoning is used for structured role and mode decisions, while low-level actions remain generated by trained control policies. Experiments on power-grid voltage control, traffic signal control, and collaborative robotic transport show that RoleFit improves long-horizon stability, reduces unsafe failures, and achieves more robust performance under changing disturbances.