Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning

• URL: http://arxiv.org/abs/2607.02484v1
• Date: Thu, 02 Jul 2026 17:50:57 GMT
• Title: Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
• Abstract summary: We propose Entropy-Aware Dense Pruning (EADP), a framework that reformulates pruning as a structured compression problem.<n>EADP first leverages statistical entropy to quantify and filter out textual noise, yielding a robust, fine-grained instruction score.
• Score: 16.79729969987944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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