The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks. However, existing systems predominantly rely on centralized coordination, which introduces scalability bottlenecks, limits adaptability, and creates single points of failure. Additionally, concerns over privacy and proprietary knowledge sharing hinder cross-organizational collaboration, leading to siloed expertise. To address these challenges, we propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to autonomously evolve their capabilities and collaborate efficiently in a Directed Acyclic Graph (DAG)-structured network. Unlike traditional multi-agent systems that depend on static role assignments or centralized control, AgentNet allows agents to specialize dynamically, adjust their connectivity, and route tasks without relying on predefined workflows. AgentNet's core design is built upon several key innovations: (1) Fully Decentralized Paradigm: Removing the central orchestrator, allowing agents to coordinate and specialize autonomously, fostering fault tolerance and emergent collective intelligence. (2) Dynamically Evolving Graph Topology: Real-time adaptation of agent connections based on task demands, ensuring scalability and resilience.(3) Adaptive Learning for Expertise Refinement: A retrieval-based memory system that enables agents to continuously update and refine their specialized skills. By eliminating centralized control, AgentNet enhances fault tolerance, promotes scalable specialization, and enables privacy-preserving collaboration across organizations. Through decentralized coordination and minimal data exchange, agents can leverage diverse knowledge sources while safeguarding sensitive information.

翻译：大型语言模型（LLM）的快速发展推动了多智能体系统的进步，其中多个基于LLM的智能体通过协作解决复杂任务。然而，现有系统主要依赖中心化协调机制，这带来了可扩展性瓶颈、限制了适应性，并引入了单点故障风险。此外，隐私和专有知识共享的顾虑阻碍了跨组织协作，导致专业知识形成孤岛。为应对这些挑战，我们提出了AgentNet——一个基于检索增强生成（RAG）的去中心化框架，使基于LLM的智能体能够在有向无环图（DAG）结构的网络中自主进化其能力并高效协作。与传统多智能体系统依赖静态角色分配或中心化控制不同，AgentNet允许智能体动态专业化、自适应调整连接关系，并在无需预定义工作流的情况下路由任务。AgentNet的核心设计基于以下关键创新：（1）完全去中心化范式：通过移除中心协调器，使智能体能够自主协调与专业化，从而提升容错能力并催生涌现的集体智能。（2）动态演化的图拓扑结构：根据任务需求实时调整智能体连接，确保可扩展性与鲁棒性。（3）面向专业精化的自适应学习：基于检索的记忆系统使智能体能够持续更新和优化其专业技能。通过消除中心化控制，AgentNet增强了系统容错性，促进了可扩展的专业化，并实现了跨组织的隐私保护协作。借助去中心化协调与最小化数据交换机制，智能体能够在保护敏感信息的同时充分利用多样化的知识源。