[简介] 针对AI大模型训练规模突破万卡、需跨园区长距组网的新需求，提出了一套端到端的联合优化解决方案：分级流控技术将任务完成时间（JCT）降低一个数量级；快速拥塞反馈机制将拥塞反馈延迟从毫秒压缩至数十微秒；逐流负载均衡协议消除端口负载不均问题；采用拓扑亲和的ZeRO 1策略替代ZeRO 3，大幅减少跨园区通信次数。实验网测试表明，通过整合上述关键技术，在千亿参数模型训练场景下可将跨园区训练的算力损失从10%~15%降低至5%以内，验证了长距网络支撑超大规模AI训练的可行性。

[简介]随着人工智能技术的快速发展，传统以比特传输为核心的通信范式正面临根本性变革。本文系统梳理了语义信息处理与通信从理论起源到技术演进的发展脉络，阐明其本质内涵与核心优势，分别从通信与智能两个维度，剖析其商用化面临的关键挑战并研判发展前景。语义通信是6G的重要候选技术，其商用化仍受制于理论体系不完善、全链路技术成熟度不足、标准化进程滞后三重瓶颈；而智能化的持续推进，特别是智能体技术的发展，将有力推动语义通信网的落地部署。前期研究表明，在工业物联网、应急通信、深海水声通信等具有明确语义任务和场景约束的垂直行业中，语义通信技术有望在3～5年内实现局部试商用；伴随6G技术和标准化的演进，语义编解码和语义认知网络架构等有望在5～10年纳入全球6G标准框架；而实现全网络语义内生智能化的大规模部署，则预计需要10～20年。本研究可为学术界与产业界开展语义通信研究及应用部署提供参考。

[Introduction] With the rapid advancement of generative artificial intelligence (AI) and large language model (LLM) technologies, AI agents are gradually becoming the core service units in networks, and their communication mode is evolving from local collaboration to wide-area interconnection. The construction of the Internet of Agents (IoA) faces multiple challenges, such as identity management, dynamic networking, and semantic routing, which urgently requires the design of a network protocol system that adapts to its new traffic characteristics and collaboration needs. Based on the application scenarios of agent communication, this paper systematically analyzes the management, control, and routing requirements that multi-agent collaboration imposes on IP networks, proposes a three-layer functional architecture for the IoA, and designs a protocol suite covering management, control, and routing around key issues such as agent registration and identification, service discovery, capability sensing, and cross-domain traffic assurance. By extending existing Internet protocols and introducing a semantically aware routing mechanism, this paper provides a scalable, efficient, and secure approach to implementing a protocol for end-to-end agent collaboration, thereby contributing to the construction of an open, large-scale agent collaboration ecosystem.

[Introduction] The convergence of artificial intelligence (AI) with the physical world is reshaping the future of intelligent systems through real-time perception, interaction, and control within physical environments. To support this new paradigm, 6G networks are envisioned as critical enablers, offering ultra-low latency, high reliability, and service-aware intelligence to facilitate seamless human-machine collaboration. This paper proposes a functional framework that integrates Agentic AI into the 6G architecture, introducing the concept of Agentic AI-Enabled 6G Network Services (AA6NS). In this framework, user intents are translated and processed across the application layer, core network (CN), and radio access network (RAN), where Agentic AI dynamically manages task-level quality of service/quality of experience (QoS/QoE), orchestrates multi-device service groups, and enables real-time network adaptation. The proposed architecture with the new 6G techniques establishes a foundation for future physical AI applications across domains such as autonomous mobility, smart manufacturing, and remote robotics.