OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917376655622144 |
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| author | Chen, Xiwen Zhu, Wenhui Li, Gen Dong, Xuanzhao Xiong, Yujian Wang, Hao Qiu, Peijie Song, Qingquan Wang, Zhipeng Tang, Shao Wang, Yalin Razi, Abolfazl |
| author_facet | Chen, Xiwen Zhu, Wenhui Li, Gen Dong, Xuanzhao Xiong, Yujian Wang, Hao Qiu, Peijie Song, Qingquan Wang, Zhipeng Tang, Shao Wang, Yalin Razi, Abolfazl |
| contents | Multi-modal large language models (MLLMs) achieve strong visual-language reasoning but suffer from high inference cost due to redundant visual tokens. Recent work explores visual token pruning to accelerate inference, while existing pruning methods overlook the underlying distributional structure of visual representations. We propose OTPrune, a training-free framework that formulates pruning as distribution alignment via optimal transport (OT). By minimizing the 2-Wasserstein distance between the full and pruned token distributions, OTPrune preserves both local diversity and global representativeness while reducing inference cost. Moreover, we derive a tractable submodular objective that enables efficient optimization, and theoretically prove its monotonicity and submodularity, providing a principled foundation for stable and efficient pruning. We further provide a comprehensive analysis that explains how distributional alignment contributes to stable and semantically faithful pruning. Comprehensive experiments on wider benchmarks demonstrate that OTPrune achieves superior performance-efficiency tradeoffs compared to state-of-the-art methods. The code is available at https://github.com/xiwenc1/OTPrune. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20205 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport Chen, Xiwen Zhu, Wenhui Li, Gen Dong, Xuanzhao Xiong, Yujian Wang, Hao Qiu, Peijie Song, Qingquan Wang, Zhipeng Tang, Shao Wang, Yalin Razi, Abolfazl Computer Vision and Pattern Recognition Multi-modal large language models (MLLMs) achieve strong visual-language reasoning but suffer from high inference cost due to redundant visual tokens. Recent work explores visual token pruning to accelerate inference, while existing pruning methods overlook the underlying distributional structure of visual representations. We propose OTPrune, a training-free framework that formulates pruning as distribution alignment via optimal transport (OT). By minimizing the 2-Wasserstein distance between the full and pruned token distributions, OTPrune preserves both local diversity and global representativeness while reducing inference cost. Moreover, we derive a tractable submodular objective that enables efficient optimization, and theoretically prove its monotonicity and submodularity, providing a principled foundation for stable and efficient pruning. We further provide a comprehensive analysis that explains how distributional alignment contributes to stable and semantically faithful pruning. Comprehensive experiments on wider benchmarks demonstrate that OTPrune achieves superior performance-efficiency tradeoffs compared to state-of-the-art methods. The code is available at https://github.com/xiwenc1/OTPrune. |
| title | OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.20205 |