突破性研究：首次实现无限深和宽Transformer的可训练性，彻底解决深层网络训练瓶颈
arXiv:2605.17660v1 Announce Type: cross Abstract: Transformers have become the dominant architecture in modern machine learning, yet the theoretical u…
探讨组合稀疏性作为神经网络架构设计的归纳偏置，从理论上破解高维学习中的维度灾难。
arXiv:2605.14764v1 Announce Type: cross Abstract: Identifying the structural priors that enable Deep Neural Networks (DNNs) to overcome the curse of d…
6G发展面临结构性转折，论文主张运营商应夺回网络控制权，扭转供应商主导的旧模式。
arXiv:2605.15553v1 Announce Type: cross Abstract: Sixth-generation mobile networks (6G) are approaching a structural inflection point. Five generation…