Multi-Agent Communication

Multi-agent communication examines how agents share information, form common beliefs, and coordinate actions when each may have only a partial view of a task. Approaches include learned signaling in decentralized reinforcement learning, graph-based routing and debate among language-model agents, and compact symbolic or latent messages designed to reduce bandwidth and latency. Researchers evaluate these methods in settings such as cooperative navigation, StarCraft, reasoning and coding tasks, and embodied collaboration with limited or unreliable communication. A central tension is that messages can reveal information teammates need, but added communication can also create overhead, spread errors, reduce diversity, or expose systems to attacks.