这篇论文用熵过滤噪声、子模选择保留细节，在VLM加速上效果不错，适合想优化推理效率的人看看。
视觉Token剪枝是加速VLM的关键策略，但现有方法在密集指令和细粒度查询下难以保留关键线索。本文提出EADP框架，首先利用统计熵量化并过滤文本噪声，得到细粒度的指令相关性分数；然后通过子模最大化问题与空间先验实现非冗余的Token选择。实验表明EADP在严格Token预算下提高了VLM的精度-效率平衡，在多个多模态基准上达到SoTA性能。
Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instructions and fine-grained queries. In this paper, we investigate this failure and identify two underlying bottlenecks: the widespread dispersion of textual noise that corrupts dense cross-modal scoring, and the feature fragmentation inherent to standard token selection. To address these issues, we propose Entropy-Aware Dense Pruning (EADP), a framework that reformulates pruning as a structured compression problem. EADP first leverages statistical entropy to quantify and filter out textual noise, yielding a robust, fine-grained instruction relevance score. Subsequently, instead of naive Top-K selection, EADP casts token selection as a submodular maximization problem with a spatial prior, explicitly ensuring a holistic and non-redundant visual representation. Extensive experiments demonstrate that EADP improves the accuracy-efficiency trade-off of VLMs, robustly preserving fine-grained visual cues under strict token budgets while achieving SoTA performance on challenging multimodal benchmarks.