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Entropy-Aware Dense Pruning

techniqueactiveprovisionalentropy-aware-dense-pruning-2f60508a·1 events·first seen 7h ago

Aliases: Entropy-Aware Dense Pruning

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4arXiv · cs.AI·7h ago·source ↗

EADP: Entropy-aware visual token pruning for efficient VLMs under dense instructions

A new arXiv preprint introduces Entropy-Aware Dense Pruning (EADP), a framework for compressing visual tokens in vision-language models (VLMs) that addresses two failure modes in existing methods: textual noise corrupting cross-modal scoring and feature fragmentation from naive Top-K selection. EADP uses statistical entropy to filter textual noise and reformulates token selection as a submodular maximization problem with a spatial prior. The authors report state-of-the-art accuracy-efficiency trade-offs on multimodal benchmarks under strict token budgets.