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Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More
Vision-Language Models (VLMs) have exhibited impressive performance across diverse visual scenarios. However, this success comes at the cost of explosive growth in visual tokens, which imposes substantial memory and computational overhead during inference, ultimately increasing latency. To improve VLM inference efficiency, a typical class of visual token pruning methods estimates token importance by aggregating attention scores across all heads in the pruning layer of the Large Language Model (LLM) backbone and prunes tokens based on aggregated scores. However, in this paper, we reveal a compe
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T03:27:14.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.