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    <abstract>Recent Vision-Language Models encode high-resolution images into long visual token sequences, incurring prohibitive prefill costs. To compress them, existing methods score each visual token by averaging text-to-visual attention uniformly across all heads, which assumes every head matches the query. However, our empirical analysis shows that misaligned heads dominate the average, amplifying background tokens and drowning out fine-grained cues.\par To address this, we propose PAQ (Prompt-Grounded Attention Quality), a metric quantifying how well each head aligns the prompt with image regions. Built on PAQ, our pruning proceeds in three stages. Given a target FLOPs budget, we first partition the transformer layers into groups and allocate a visual token budget to each. Within each group, we then aggregate per-head attention maps via PAQ-weighted softmax into a group-level matrix. Finally, we score visual tokens by this matrix’s magnitude and retain the allocated budget per group. By weighting heads with PAQ, our method scores tokens by true relevance to the prompt rather than diluting it through uniform averaging.\par Across 18 benchmarks, our method delivers state-of-the-art trade-offs. Specifically, on LLaVA-1.5-7B (9 tasks), retaining only 5.6% tokens preserves 99.1% of the original performance, surpassing the strongest baseline AutoPrune by 4.2 points. Code is available.</abstract>
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%0 Conference Proceedings
%T HAP: Head-Adaptive Visual Token Pruning via Cross-Modal Alignment
%A Sun, Yuanhao
%A Ji, Huawei
%A Jin, Yuan
%A Deng, Cheng
%A Fu, Luoyi
%A Wang, Xinbing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-hap
%X Recent Vision-Language Models encode high-resolution images into long visual token sequences, incurring prohibitive prefill costs. To compress them, existing methods score each visual token by averaging text-to-visual attention uniformly across all heads, which assumes every head matches the query. However, our empirical analysis shows that misaligned heads dominate the average, amplifying background tokens and drowning out fine-grained cues.\par To address this, we propose PAQ (Prompt-Grounded Attention Quality), a metric quantifying how well each head aligns the prompt with image regions. Built on PAQ, our pruning proceeds in three stages. Given a target FLOPs budget, we first partition the transformer layers into groups and allocate a visual token budget to each. Within each group, we then aggregate per-head attention maps via PAQ-weighted softmax into a group-level matrix. Finally, we score visual tokens by this matrix’s magnitude and retain the allocated budget per group. By weighting heads with PAQ, our method scores tokens by true relevance to the prompt rather than diluting it through uniform averaging.\par Across 18 benchmarks, our method delivers state-of-the-art trade-offs. Specifically, on LLaVA-1.5-7B (9 tasks), retaining only 5.6% tokens preserves 99.1% of the original performance, surpassing the strongest baseline AutoPrune by 4.2 points. Code is available.
%U https://aclanthology.org/2026.findings-emnlp.1450/
%P 30893-30911

Markdown (Informal)

[HAP: Head-Adaptive Visual Token Pruning via Cross-Modal Alignment](https://aclanthology.org/2026.findings-emnlp.1450/) (Sun et al., Findings 2026)

• HAP: Head-Adaptive Visual Token Pruning via Cross-Modal Alignment (Sun et al., Findings 2026)

ACL

• Yuanhao Sun, Huawei Ji, Yuan Jin, Cheng Deng, Luoyi Fu, and Xinbing Wang. 2026. HAP: Head-Adaptive Visual Token Pruning via Cross-Modal Alignment. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 30893–30911, Budapest, Hungary. Association for Computational Linguistics.