Author

Chengzhu Bao

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Preprint Oct 2026

ProQuant: Preserving Expert Quality over Quantity in Ultra-Low-Bit Quantization of Mixture-of-Experts Models

Mixture-of-Experts (MoE) large language models reduce computation through sparse activation but still incur substantial storage costs. Mixed-precision post-training quantization (PTQ) reduces these costs by quantizing different model components at different bit widths, but existing methods often struggle to preserve ge...

Jia-Qi Chen, Xiang-Long Yan, Cheng-Zhu Bao et al. · 0 citations

Preprint Aug 2026

FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling

FOCUS is proposed, a post-training quantization framework with end-to-end scale learning for FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling, which relaxes the tight coupling between quantization and dequantization scales with a learnable full-precision coefficient, enabling more effective optimiza...

Xiang-Long Yan, Hong Liu, Cheng-Zhu Bao et al. · 1 citation