Large language models have accelerated the transition from passive conversational assistants to autonomous agents that can understand goals, plan actions, invoke tools, and execute multi-step tasks. Yet the capability of a single agent remains constrained by its local data, tool permissions, runtime environment, and governance boundary. This paper studies distributed general-purpose agent networks: open peer-to-peer networks in which heterogeneous agents deployed on personal devices, edge nodes, or autonomous computing environments can discover one another, establish trust, negotiate cooperation rules, and execute open-ended tasks. We argue that such networks cannot be obtained by simply combining existing peer-to-peer overlays with conventional multi-agent systems. Unlike traditional P2P networks, agent networks must propagate semantic declarations about intentions, capabilities, states, and cooperation constraints. We therefore propose a layered architecture centered on a protocol adaptation layer that connects upper-level task semantics with lower-level network operations. Based on this architecture, the paper identifies three core mechanism problems: semantic announcement propagation for collaborator discovery, verifiable identity and multi-topic reputation for cooperation governance, and semantic-gradient mechanism design for open task execution. For each problem, we present a technical route, including bodyless gossip with sequential logs, BAID-based identity binding with MG-EigenTrust reputation, and a Stackelberg-style mechanism-generation loop driven by semantic attribution feedback. We further report prototype overhead results for BAID-style tiered verification and mechanism-level simulations of MG-EigenTrust under cross-topic disguise-collusion attacks. The resulting framework provides a system-level foundation for open, trustworthy, and scalable agent collaboration.

翻译：大型语言模型加速了从被动对话式助手向能够理解目标、规划行动、调用工具并执行多步任务的自主智能体的转变。然而，单个智能体的能力仍受限于其本地数据、工具权限、运行环境和治理边界。本文研究分布式通用智能体网络：一种开放的对等网络，其中部署在个人设备、边缘节点或自主计算环境上的异构智能体能够相互发现、建立信任、协商合作规则并执行开放式任务。我们认为，此类网络无法通过简单地将现有对等网络覆盖层与传统多智能体系统相结合而获得。与传统P2P网络不同，智能体网络必须传播关于意图、能力、状态和合作约束的语义声明。因此，我们提出了一种以协议适配层为中心的分层架构，该层连接上层任务语义与底层网络操作。基于此架构，本文识别出三个核心机制问题：用于协作者发现的语义声明传播、用于合作治理的可验证身份与多主题声誉、以及用于开放式任务执行的语义梯度机制设计。针对每个问题，我们提出了一条技术路线，包括基于序列日志的无体传播、基于BAID的身份绑定与MG-EigenTrust声誉机制、以及由语义归因反馈驱动的Stackelberg博弈风格机制生成循环。我们进一步报告了BAID风格分层验证的原型开销结果以及跨主题伪装合谋攻击下MG-EigenTrust的机制级仿真。由此产生的框架为开放、可信且可扩展的智能体协作提供了系统级基础。