[简介] 大模型的迅猛发展正在深刻变革网络运营管理方式，推动自智网络从“外挂式智能”迈向“内生式智能”。聚焦大模型驱动的网络智能运营管理，在分析网络运营管理智能化的发展需求基础上，总结了网络运营管理大模型标准化进展。之后，在提出大模型驱动的网络智能运营管理架构基础上，阐述了大模型在网络自配置、自优化、自治愈等过程的关键技术和挑战。针对大模型在网络运营管理智能化中的应用和实例进行了验证，展望面向未来“标准引领、价值落地、能力演进”愿景的大模型运维体系，为实现真正智能自治的网络管理范式转型提供参考。

[简介]总结了光纤通信技术从基础突破到融合创新的发展历程，分析了光纤通信从光层基础到多域组网的关键技术进展，并展望了其未来发展趋势，同时指出中国面临的发展机遇和挑战。针对人工智能、6G等未来发展新需求，建议业界继续协同聚力推进光纤通信关键技术基础突破和融合创新，支撑信息基础设施高质量发展。

[Introduction] Millimeter-wave (mmWave) technology has been extensively studied for indoor short-range communications. In such fixed network applications, the emerging FTTR architecture allows mmWave technology to be well cascaded with in-room optical network terminals, supporting high-speed communication at rates over tens of Gb/s. In this FTTR-mmWave system, the severe signal attenuation over distance and high penetration loss through room walls are no longer bottlenecks for practical mmWave deployment. Instead, these properties create high spatial isolation, which prevents mutual interference between data streams and ensures information security. This paper surveys the promising integration of Fiber-to-the-Room (FTTR) and millimeter-wave (mmWave) access for next-generation indoor high-speed communications, with a particular focus on the Ultra-Converged Access Network (U-CAN) architecture. It is structured in two main parts: it first traces this new FTTR-mmWave architecture from the perspective of Wi-Fi and mmWave communication evolution, and then focuses specifically on the development of key mmWave chipsets for FTTR-mmW Wi-Fi applications. This work aims to provide a comprehensive reference for researchers working toward immersive, untethered indoor wireless experiences for users.

[Introduction]Open-set object detectors, as exemplified by Grounding DINO, have attracted significant attention due to their remarkable performance on in-domain datasets like Common Objects in Context (COCO) after only few-shot fine-tuning. However, their generalization capabilities in cross-domain scenarios remain substantially inferior to their in-domain few-shot performance. Prior work on fine-tuning Grounding DINO for cross-domain few-shot object detection has primarily focused on data augmentation, leaving broader systemic optimizations unexplored. To bridge this gap, we propose a comprehensive end-to-end fine-tuning framework specifically designed to optimize Grounding DINO for cross-domain few-shot scenarios. In addition, we propose Mixture-of-Experts (MoE)-Grounding DINO, a novel architecture that integrates the MoE architecture to enhance adaptability in cross-domain settings. Our approach demonstrates a significant 15.4 Mean Average Precision (mAP) improvement over the Grounding DINO baseline on the Roboflow20-VL benchmark, establishing a new state of the art for cross-domain few-shot object detection (CD-FSOD). The source code and models will be made available upon publication.