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.

翻译：多模态大语言模型在视觉-语言推理方面取得了显著成效，但冗余视觉标记导致推理成本高昂。现有剪枝方法忽视了视觉表征的潜在分布结构，为此提出OTPrune——一种无训练框架，通过最优传输实现分布对齐式剪枝。通过最小化完整标记分布与剪枝标记分布之间的2-瓦瑟斯坦距离，OTPrune在降低推理成本的同时保持局部多样性与全局代表性。我们进一步推导出可处理的子模目标函数以实现高效优化，并从理论上证明其单调性与子模性，为稳定高效的剪枝奠定理论基础。通过深入分析，揭示了分布对齐如何促进稳定且语义保真的剪枝。在广泛基准上的综合实验表明，与最先进方法相比，OTPrune实现了更优的性能-效率权衡。代码已开源：https://github.com/xiwenc1/OTPrune