Author

Ruihan Hu

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SeGO: Sensitivity-Aware Golden Optimization for Large-Scale VLM Quantization

A cross-modal structural sensitivity asymmetry in VLMs is revealed and SeGO is proposed, a unified structural sensitivity-aware sparse optimization framework that achieves the balance among model parameter amount, quantization accuracy and scaling factors’ search efficiency on InternVL2 and LLaVA series.

Tianqi Zhao, Xinrui Cheng, Yang Su et al. · 0 citations

Preprint Aug 2026

FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy

FluxBin is proposed, an algorithm-kernel co-design that synergizes post-training quantization with a highly optimized CUDA kernel and introduces Decoupled Row-Column Binary Decomposition to enhance representational capacity while maintaining hardware efficiency.

Qingyao Yang, Runming Yang, He Xiao et al. · 0 citations