arXiv Machine Learning By Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Chongbo Sun, Xuefeng Tao, Wei Yang, Le Zhang, Wenwu Zhu, Peng Cui, Zhi Wang

Grounding Large Language Models as Generalizable Policies in Network Control

Read the original on arXiv Machine Learning →

arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 17

OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization

arXiv:2606. 18105v1 Announce Type: cross Abstract: Network planning optimization is a fundamental problem across diverse domains, including transportation systems, communication networks, and power grids.

By Longlong Zhu, Jiashuo Yu, Zedi Chen, Yuhan Wu, Zhifan Jiang, Yuchen Xian, Yimeng Liu, Jiajie Su, Shaopeng Zhou, Xingyuan Li, Hongyan Liu, Xuan Liu, Dong Zhang, Chunming Wu, Xiang Chen
arXiv AI
Jun 29

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference

arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.

By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu