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cs.AI2026
Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation
Yaoxiang Wang, Kai Zhang, Ruobing Xie +4
Fine-grained mixture-of-experts (MoE) models commonly increase expert count by narrowing each expert, treating expert shape as a by-product of routing granularity. LatentMoE moves…
cs.CL2026
Hybrid Gated Attention
Zekun Zhou, Ruobing Xie, Lanrui Wang +1
Gated attention is an effective approach to mitigate attention sinks and enhance the representational capacity of attention. To further extend its effectiveness-efficiency Pareto f…
cs.CL2026
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Mao Zheng, Zheng Li, Tao Chen +10
Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of wh…