Author

Wen-Ya Wang

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#artificial intelligence Preprint Sep 2026

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models

These findings show that effective pruning should preserve broad visual coverage before fusion and progressively refine the retained tokens as cross-modal evidence evolves during fusion, and propose STAR-Pro (STage-Wise Adaptive Token Reduction with Progressive Refinement), a training-free two-stage framework.

Yi-Chen Guo, Ting-Hao Wang, Qi-Zhe Zhang et al. · 1 citation

Jun 2026

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

This work proposes FADE (FFN Attenuation for DEcoding), a training-free method that attenuates FFN outputs to reduce language-prior dominance and shows that FADE effectively mitigates hallucinations while preserving inference efficiency.

Yichen Guo, Kai Tang, Fenglai Lin et al. · arXiv.org · 2 citations