@inproceedings{zhang-etal-2026-adaptinfer,
title = "{A}dapt{I}nfer: Adaptive Token Pruning for Vision Language Model Inference via Dynamical Text Guidance",
author = "Zhang, Weichen and
Zhu, Zhui and
Li, Ningbo and
Tao, Shilong and
Zhu, Hongzi and
Xu, Jingao and
Liu, Kebin and
Liu, Yunhao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.945/",
doi = "10.18653/2026.findings-emnlp.945",
pages = "19920--19938",
ISBN = "979-8-89176-462-0",
abstract = "Vision{--}language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due to the large number of vision tokens processed during the prefill stage. Existing token pruning methods often rely on utilizing the static attention patterns directly, failing to exploit the dynamic internal signals within VLMs. To address the issue, we propose AdaptInfer, a novel plug-and-play framework for vision token pruning. First, we introduce a dynamic text-guided pruning mechanism that construct soft priors over text-token importance on each pruning layer, allowing more informed scoring of vision tokens at each stage. Second, we observe a highly consistent distribution of cross-modal attention shifts by architecture, which inspires us to introduce a efficient data-driven schedule which determines the pruning locations automatically. Experimental results have verified the effectiveness and generalization of the proposed method. Under the same token budget, AdaptInfer surpasses existing approaches in accuracy. For instance, AdaptInfer maintains averagely 99.4{\%} accuracy on Qwen2-VL while 70{\%} of the prefilling vision token overhead is reduced. The source code is available on: https://github.com/weiczh02/AdaptInfer-base."
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<abstract>Vision–language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due to the large number of vision tokens processed during the prefill stage. Existing token pruning methods often rely on utilizing the static attention patterns directly, failing to exploit the dynamic internal signals within VLMs. To address the issue, we propose AdaptInfer, a novel plug-and-play framework for vision token pruning. First, we introduce a dynamic text-guided pruning mechanism that construct soft priors over text-token importance on each pruning layer, allowing more informed scoring of vision tokens at each stage. Second, we observe a highly consistent distribution of cross-modal attention shifts by architecture, which inspires us to introduce a efficient data-driven schedule which determines the pruning locations automatically. Experimental results have verified the effectiveness and generalization of the proposed method. Under the same token budget, AdaptInfer surpasses existing approaches in accuracy. For instance, AdaptInfer maintains averagely 99.4% accuracy on Qwen2-VL while 70% of the prefilling vision token overhead is reduced. The source code is available on: https://github.com/weiczh02/AdaptInfer-base.</abstract>
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%0 Conference Proceedings
%T AdaptInfer: Adaptive Token Pruning for Vision Language Model Inference via Dynamical Text Guidance
%A Zhang, Weichen
%A Zhu, Zhui
%A Li, Ningbo
%A Tao, Shilong
%A Zhu, Hongzi
%A Xu, Jingao
%A Liu, Kebin
%A Liu, Yunhao
%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 zhang-etal-2026-adaptinfer
%X Vision–language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due to the large number of vision tokens processed during the prefill stage. Existing token pruning methods often rely on utilizing the static attention patterns directly, failing to exploit the dynamic internal signals within VLMs. To address the issue, we propose AdaptInfer, a novel plug-and-play framework for vision token pruning. First, we introduce a dynamic text-guided pruning mechanism that construct soft priors over text-token importance on each pruning layer, allowing more informed scoring of vision tokens at each stage. Second, we observe a highly consistent distribution of cross-modal attention shifts by architecture, which inspires us to introduce a efficient data-driven schedule which determines the pruning locations automatically. Experimental results have verified the effectiveness and generalization of the proposed method. Under the same token budget, AdaptInfer surpasses existing approaches in accuracy. For instance, AdaptInfer maintains averagely 99.4% accuracy on Qwen2-VL while 70% of the prefilling vision token overhead is reduced. The source code is available on: https://github.com/weiczh02/AdaptInfer-base.
%R 10.18653/2026.findings-emnlp.945
%U https://aclanthology.org/2026.findings-emnlp.945/
%U https://doi.org/10.18653/2026.findings-emnlp.945
%P 19920-19938
Markdown (Informal)
[AdaptInfer: Adaptive Token Pruning for Vision Language Model Inference via Dynamical Text Guidance](https://aclanthology.org/2026.findings-emnlp.945/) (Zhang et al., Findings 2026)
ACL
- Weichen Zhang, Zhui Zhu, Ningbo Li, Shilong Tao, Hongzi Zhu, Jingao Xu, Kebin Liu, and Yunhao Liu. 2026. AdaptInfer: Adaptive Token Pruning for Vision Language Model Inference via Dynamical Text Guidance. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19920–19938, Budapest, Hungary. Association for Computational Linguistics.