为智能代理网络设计可信排名系统，让优秀代理脱颖而出。
Internet 3.0: Architecture for a Web-of-Agents with it's Algorithm for Ranking Agents
- 构建五层协议收集全局使用数据，保护隐私的同时实现协同
- 融合使用频率与能力质量，动态生成可信代理排名
- 适合关注AI代理生态建设与信任机制的研究者
由具备推理能力的大语言模型驱动、整合工具、数据与网络搜索的AI代理，正推动互联网向‘代理之网’演进——一个机器原生的自治协作生态系统。实现这一愿景需解决‘代理排序’问题：不仅依据声明能力，更应基于真实、近期的表现。不同于早期网页的PageRank，当前代理交互缺乏全局透明网络，使用信号分散且私密，难以独立完成排名。本文提出DOVIS五层协议（发现、编排、验证、激励、语义），在保障隐私前提下聚合跨生态的最小化使用与性能数据。在此基础上，设计AgentRank-UC算法，融合使用频率与胜任力（成果质量、成本、安全、延迟）实现统一动态排名。通过仿真与理论分析，验证了该方案在收敛性、鲁棒性及抗伪造攻击方面的有效性，证明了协同协议与性能感知排名对可扩展、可信代理网络的可行性。
原文摘要 · Abstract (English)
AI agents -- powered by reasoning-capable large language models (LLMs) and integrated with tools, data, and web search -- are poised to transform the internet into a \emph{Web of Agents}: a machine-native ecosystem where autonomous agents interact, collaborate, and execute tasks at scale. Realizing this vision requires \emph{Agent Ranking} -- selecting agents not only by declared capabilities but by proven, recent performance. Unlike Web~1.0's PageRank, a global, transparent network of agent interactions does not exist; usage signals are fragmented and private, making ranking infeasible without coordination. We propose \textbf{DOVIS}, a five-layer operational protocol (\emph{Discovery, Orchestration, Verification, Incentives, Semantics}) that enables the collection of minimal, privacy-preserving aggregates of usage and performance across the ecosystem. On this substrate, we implement \textbf{AgentRank-UC}, a dynamic, trust-aware algorithm that combines \emph{usage} (selection frequency) and \emph{competence} (outcome quality, cost, safety, latency) into a unified ranking. We present simulation results and theoretical guarantees on convergence, robustness, and Sybil resistance, demonstrating the viability of coordinated protocols and performance-aware ranking in enabling a scalable, trustworthy Agentic Web.
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