Yunzhi Zhuge
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2026
ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs
This paper proposes ERA, a framework for efficient multimodal large language models using entropy-guided visual token pruning, rectified attention, and bias-aware token recycling.
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Papers
cs.CVEmpiricalRecentJun 30, 2026
ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs
Yuhao Wang, Mu Qiao, Haiwen Diao, Yunzhi Zhuge +4 more
This paper proposes ERA, a framework for efficient multimodal large language models using entropy-guided visual token pruning, rectified attention, and bias-aware token recycling.
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