@inproceedings{song-etal-2026-visual,
title = "Visual Attention Faithfulness in Vision-Language Models is Heterogeneous",
author = "Song, Xurui and
Wang, Weishi and
Yue, Zhongqi and
Binici, Kuluhan and
Bai, Tao and
Shao, Hongxin and
Dahlmeier, Daniel and
Luo, Jun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1155/",
pages = "26472--26486",
ISBN = "979-8-89176-459-0",
abstract = "Whether attention weights faithfully reflect model reasoning has been actively debated in NLP, yet this question remains largely unexplored for the visual modality in Vision-Language Models (VLMs). We address this gap through causal perturbation analysis on current VLMs, evaluating both the comprehensiveness and sufficiency gap of attention-ranked visual tokens. Our analysis reveals that visual attention faithfulness is heterogeneous, manifesting in three distinct processing modes: *Faithful-Sufficient*, where top-$k$ attention tokens are both necessary and sufficient for prediction; *Faithful-Distributed*, where they are necessary but broader visual context remains required; and *Non-Focal*, where no localized attention region is individually necessary while visual information remains an essential trigger for prediction. Furthermore, human-annotated ground-truth regions satisfy comprehensiveness in only $\sim60${\%} of cases compared with model attention rankings, revealing systematic divergence between model visual reliance and human intuition. We demonstrate these patterns across both general VQA on VQAv2 and document tasks on VRDU and ChartQA, showing that visual attention faithfulness varies systematically with processing demands and model architectures rather than being uniformly faithful or unfaithful."
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<abstract>Whether attention weights faithfully reflect model reasoning has been actively debated in NLP, yet this question remains largely unexplored for the visual modality in Vision-Language Models (VLMs). We address this gap through causal perturbation analysis on current VLMs, evaluating both the comprehensiveness and sufficiency gap of attention-ranked visual tokens. Our analysis reveals that visual attention faithfulness is heterogeneous, manifesting in three distinct processing modes: *Faithful-Sufficient*, where top-k attention tokens are both necessary and sufficient for prediction; *Faithful-Distributed*, where they are necessary but broader visual context remains required; and *Non-Focal*, where no localized attention region is individually necessary while visual information remains an essential trigger for prediction. Furthermore, human-annotated ground-truth regions satisfy comprehensiveness in only \sim60% of cases compared with model attention rankings, revealing systematic divergence between model visual reliance and human intuition. We demonstrate these patterns across both general VQA on VQAv2 and document tasks on VRDU and ChartQA, showing that visual attention faithfulness varies systematically with processing demands and model architectures rather than being uniformly faithful or unfaithful.</abstract>
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%0 Conference Proceedings
%T Visual Attention Faithfulness in Vision-Language Models is Heterogeneous
%A Song, Xurui
%A Wang, Weishi
%A Yue, Zhongqi
%A Binici, Kuluhan
%A Bai, Tao
%A Shao, Hongxin
%A Dahlmeier, Daniel
%A Luo, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F song-etal-2026-visual
%X Whether attention weights faithfully reflect model reasoning has been actively debated in NLP, yet this question remains largely unexplored for the visual modality in Vision-Language Models (VLMs). We address this gap through causal perturbation analysis on current VLMs, evaluating both the comprehensiveness and sufficiency gap of attention-ranked visual tokens. Our analysis reveals that visual attention faithfulness is heterogeneous, manifesting in three distinct processing modes: *Faithful-Sufficient*, where top-k attention tokens are both necessary and sufficient for prediction; *Faithful-Distributed*, where they are necessary but broader visual context remains required; and *Non-Focal*, where no localized attention region is individually necessary while visual information remains an essential trigger for prediction. Furthermore, human-annotated ground-truth regions satisfy comprehensiveness in only \sim60% of cases compared with model attention rankings, revealing systematic divergence between model visual reliance and human intuition. We demonstrate these patterns across both general VQA on VQAv2 and document tasks on VRDU and ChartQA, showing that visual attention faithfulness varies systematically with processing demands and model architectures rather than being uniformly faithful or unfaithful.
%U https://aclanthology.org/2026.emnlp-main.1155/
%P 26472-26486
Markdown (Informal)
[Visual Attention Faithfulness in Vision-Language Models is Heterogeneous](https://aclanthology.org/2026.emnlp-main.1155/) (Song et al., EMNLP 2026)
ACL
- Xurui Song, Weishi Wang, Zhongqi Yue, Kuluhan Binici, Tao Bai, Hongxin Shao, Daniel Dahlmeier, and Jun Luo. 2026. Visual Attention Faithfulness in Vision-Language Models is Heterogeneous. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26472–26486, Budapest, Hungary. Association for Computational Linguistics.