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Huimin Zeng

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#machine learning Preprint Oct 2026

MASKerade: Token-Routed Mask Experts for Dense-to-MoE Upcycling

Sparsely activated Mixture-of-Experts (MoE) models increase model capacity without a proportional increase in per-token computation. Dense-to-MoE upcycling reuses pretrained dense models to construct such systems, commonly by copying feed-forward networks (FFNs) into independently trained experts. We introduce MASKerad...

Ming-Yuan Zhang, Yue Bai, Zhong-Ruo Wang et al. · 0 citations
Conference Open access 2026

ACBQ: Adaptive Cross-Block Quantization of Large Language Models

ACBQ is presented, a simple yet effective framework that simultaneously addresses weight–activation joint quantization and extreme low-bit weight quantization and an adaptive cross-block quantization strategy that explicitly accounts for cross-layer dependencies by encouraging consistency across blocks.

Hailing Wang, Jianglin Lu, Yitian Zhang et al. · 0 citations

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