Core Insights - The article discusses the urgent need for advanced network infrastructure to support large language model training and data center security in the context of rapid advancements in intelligent computing and future networks [2][3]. Group 1: Research Achievements - The research group led by Assistant Professor Liu Guyue from Peking University has made significant contributions, with five high-level papers accepted at ACM SIGCOMM 2025, making it the highest-publishing research group from a university this year [2][3]. - The acceptance rate for SIGCOMM 2025 was only 16.1%, with 461 submissions and only 74 accepted [2]. Group 2: Key Research Papers - **InfiniteHBD**: Proposes a transceiver-centered high-bandwidth domain architecture that overcomes scalability and fault tolerance issues in large model training, achieving a cost reduction to 31% of NVL-72 and nearly zero GPU waste [6][8]. - **DNSLogzip**: Introduces a novel approach for fast and high-ratio compression of DNS logs, reducing storage costs by approximately two-thirds, saving up to $163,000 per month per DNS service node [11][12]. - **BiAn**: A framework based on large language models for intelligent fault localization in production networks, reducing root cause identification time by 20.5% and improving accuracy by 9.2% [13][14]. - **MixNet**: A runtime reconfigurable optical-electrical network structure for distributed mixture-of-experts training, enhancing network cost efficiency by 1.2 to 2.3 times under various bandwidth conditions [15][18]. - **Mazu**: A high-speed encrypted traffic anomaly detection system implemented on programmable switches, successfully protecting over ten million servers and detecting malicious traffic with approximately 90% accuracy [19][22]. Group 3: Overall Impact - The five research outcomes collectively form a comprehensive technological loop across architecture, data, operations, and security, driving the efficient, reliable, and intelligent development of next-generation network systems [3].