arXiv:2607. 01674v1 Announce Type: new Abstract: In multi-source ECG deployment, models may need to incorporate new data sources when earlier raw ECGs cannot be retained or replayed.
By Yufan Lu, Xinhui Liu, Chenyang Xu, Yuxi Zhou, Hao Wang, Shenda Hong
arXiv:2608. 08853v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) routers commonly use the same scores both to select experts and to weight their already-computed outputs.
By Zongfei Li
arXiv:2608. 08627v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes over its full expert bank.
By Lei Xin, Bin Gu, Peize Li, Zitong Wang, Jianbo Zhao, Changjiang Jiang, Yanyue Xie, Chao Huang, Xuyang Zhao, Zunhai Su, Fanhu Zeng, Zhenglun Kong
arXiv:2606. 10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored.
By Jingyi Xie, Yijun Lin, Yinjiang Xiong, Zhikun Zhang, Sai Li
arXiv:2505. 17639v4 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization.
By Zehua Pei, Ying Zhang, Hui-Ling Zhen, Tao Yuan, Xianzhi Yu, Zhenhua Dong, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu
arXiv:2606. 07404v1 Announce Type: new Abstract: This paper reports on training a hundred-billion-parameter sparse mixture of experts on a single eight-GPU node, end to end.
By Rohan Shravan