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cs.AI2026
Fine-Grained Units, Flat Decisions: Hierarchical Micro-Expert Pruning for Mixture-of-Experts Models
Zukang Xu, Xinpei Gao, Xing Hu +2
Micro-expert pruning reduces the capacity of mixture-of-experts (MoE) models by removing coupled channels within routed experts. However, finer pruning granularity alone does not g…
cs.AI2026
ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs
Zukang Xu, Zhixiong Zhao, Xing Hu +5
Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs), yet fixed top-k routing activates the same number of expert slots for…