How to distribute differentiation without a leader?
Exact Leader Estimation: A New Approach for Distributed Differentiation
This paper introduces a novel algorithm for distributed differentiation in multi-agent systems. Agents estimate the state (position and higher-order derivatives) of a leader agent without needing to know which agent is the leader or the leader's input, relying only on communication with neighbors. The algorithm is robust to noisy and sampled measurements, providing accurate estimations even with imperfect data. This robustness is directly relevant to LLM-based multi-agent systems, where agent communication can be unreliable or delayed, and internal representations of world state are inherently noisy. The ability to estimate leader state in a distributed, robust manner is crucial for coordination in such systems.