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arXiv · 2609.03158

Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization

Qingchan Zhu·Weihang You·Hanqi Jiang·Changdi Yang·Tianming Liu·Geng Yuan
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Abstract

Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask which tokens to keep. This retained-token view can keep redundant high-scoring tokens while leaving discarded evidence without a close representative. We propose CoverPruner, a training-free pruner that asks the complementary demand-side question: after a token is removed, which surviving original token represents it for the target VLM? CoverPruner formulates pruning as Representational Coverage Maximization (RCM), covering the full projected visual-token set with query-weighted demand. It instantiates RCM with projector-space coverage and a lightweight first-layer attention probe. Across multiple VLM architectures and compression rates, CoverPruner achieves the best average accuracy among all compared methods, with the largest gains usually appearing under aggressive compression.

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Qingchan Zhu, Weihang You, Hanqi Jiang, Changdi Yang, Tianming Liu, Geng Yuan. 2026-09-02. Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization. https://arxiv.org/abs/2609.03158

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