How can LLMs decentralize multi-agent coordination?
AGENTNET: DECENTRALIZED EVOLUTIONARY COORDINATION FOR LLM-BASED MULTI-AGENT SYSTEMS
This paper introduces AgentNet, a decentralized framework for LLM-based multi-agent systems. Unlike centralized approaches, AgentNet lets agents specialize dynamically and collaborate via a self-organizing network, routing tasks without predefined workflows. Key features include decentralized coordination, dynamic graph topology, adaptive learning for expertise refinement (using a RAG memory system), and enhanced privacy. This results in improved efficiency, adaptability, and scalability in dynamic environments compared to traditional centralized multi-agent systems. The paper emphasizes autonomous agent evolution, optimized task coordination, and preservation of privacy as key improvements for LLM-based multi-agent systems.