Core Viewpoint - The GRA framework (Generator–Reviewer–Adjudicator) proposed by Shanghai AI Lab and Renmin University of China enables small models to collaboratively generate high-quality training data without the need for large-scale language model distillation [1][2][13]. Group 1: GRA Framework Overview - GRA operates on the principle of "multi-person collaboration" and "role division," simulating a peer review process to ensure data quality [7][12]. - The framework consists of three main roles: Generator, Reviewer, and Adjudicator, each contributing to the data generation and evaluation process [8][9][10]. Group 2: Experimental Results - GRA-generated data quality matches or exceeds that of single large language models across ten mainstream datasets, showing significant performance improvements [2][14]. - The GRA framework integrates five open-source small language models, demonstrating that collaboration among smaller models can yield competitive results against larger models [14][17]. Group 3: Performance Metrics - GRA-generated data improved training performance by an average of 6.18% on LLaMA-3.1 and 11.81% on Qwen-2.5 compared to original data [16]. - GRA's performance is only 0.59% lower than the Qwen-72B distilled version, while outperforming it by 8.83% when trained on Qwen-2.5 data [17]. Group 4: Advantages of GRA - GRA enhances data diversity and quality, filling gaps in the original seed data and providing a broader semantic coverage [18]. - The data quality is validated through a robust review process, with over 87.3% of samples receiving high consistency scores [19]. - GRA-generated data presents a higher task difficulty, increasing the effectiveness of training for small models [20].

Core Viewpoint - The article discusses the evolution and categorization of AI agent protocols, emphasizing the need for standardized communication to enhance collaboration and problem-solving capabilities among AI agents across various industries [1][9]. Summary by Sections AI Agent Protocols Overview - The report introduces a systematic two-dimensional classification framework for existing AI agent protocols, distinguishing between context-oriented protocols and inter-agent protocols, as well as general-purpose and domain-specific protocols [1]. Model Context Protocol (MCP) - MCP represents a centralized approach where a core "MCP travel client" agent coordinates all external services, leading to a star-shaped information flow. While it is simple and easy to control, it lacks flexibility and scalability, making it challenging to adapt to complex tasks [2][3]. Agent-to-Agent Protocol (A2A) - A2A promotes a distributed and collaborative model, allowing agents to communicate directly without a central coordinator. This flexibility supports dynamic responses to changing needs but may face challenges when crossing organizational boundaries [4][5]. Agent Network Protocol (ANP) - ANP standardizes cross-domain interactions, enabling agents from different organizations to collaborate effectively. It formalizes the request and response process, making it suitable for diverse and secure environments [6]. Agora Protocol - Agora focuses on translating user natural language requests into standardized protocols for execution by specialized agents. This three-stage process enhances adaptability and allows agents to concentrate on their core functions [7][8]. Future Trends in AI Agent Protocols - The development of AI agent protocols is expected to evolve towards more adaptive, privacy-focused, and modular systems. Short-term goals include establishing unified evaluation frameworks and enhancing privacy protection mechanisms [9][10]. - Mid-term trends may involve embedding protocol knowledge into large language models and developing layered protocol architectures to improve interoperability [11][12]. - Long-term aspirations include creating a collective intelligence infrastructure and specialized data networks to facilitate structured, intent-driven information exchange among agents [13][14][15]. Conclusion - The exploration of AI agent protocols indicates a clear trajectory towards a more intelligent, autonomous, and collaborative future, with significant implications for technology, society, and economic models [16][17].

论文作者包括来自上海交通大学的杨滢轩、柴化灿、宋源祎、齐思远、温睦宁、李宁、廖俊威、胡浩毅、林江浩、刘卫文、温颖、俞勇、张伟楠，以及 ANP 社区 发起人常高伟。 随着大语言模型 (LLM) 技术的迅猛发展，基于 LLM 的智能智能体在客户服务、内容创作、数据分析甚至医疗辅助等多个行业领域得到广泛应用。然而，不同智能 体系统间的碎片化通信标准已成为制约其进一步发展的瓶颈。上海交通大学团队与 ANP 社区合作推出了首个全面系统的 AI 智能体协议综述《A Survey of AI Agent Protocols》，为解决这一关键挑战提供了清晰的指导框架。 ArXiv 论文链接：https://arxiv.org/abs/2504.16736 Github 仓库地址：https://github.com/zoe-yyx/Awesome-AIAgent-Protocol 交互碎片化：阻碍智能智能体发展的关键瓶颈 正如早期互联网面临的通信标准分散问题，当前的智能智能体生态系统同样遭遇协议不统一的困境。研究团队指出，随着应用场景扩展和不同供应商、不同结构 的智能体涌现，智能体与实体之间的交互规则变得越来越复杂。这种协议 ...