ORCH: Organizational Principles Enable Collective Intelligence in Embodied AI
Embodied multi-agent systems often rely on rigid interaction topologies despite differing physical requirements. ORCH (Organizing Roles and Coordination Hierarchies) operationalizes organizational theory by integrating pooled interdependence for concurrent workloads with sequential interdependence for staged dependencies. Evaluated on 25 wildfire response missions with up to 50 heterogeneous agents across eight language models, the framework improved task completion by 63.97% and execution efficiency by 74.29% over existing multi-agent baselines, demonstrating that coordination structure outweighs pure model scale.
Why it's worth reading
Illustrates that task-aligned organizational structures govern collective multi-agent performance more effectively than simply scaling individual model sizes.