Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs

• URL: http://arxiv.org/abs/2606.18681v1
• Date: Wed, 17 Jun 2026 04:45:10 GMT
• Title: Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs
• Abstract summary: We introduce SPARE, a subspace reconstruction method that reformulates token pruning as a column subset selection problem.<n>We show that SPARE consistently achieves state-of-the-art performance, with strong gains on tasks.
• Score: 7.371189496638082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual tokens. While diversity maximization has become a dominant strategy for token reduction, existing methods rely on cosine-based normalized similarity that discards magnitude information, failing to faithfully approximate the original feature representation and leading to suboptimal performance, particularly on compositional multi-skill reasoning tasks. In this paper, we introduce SPARE, a subspace reconstruction method that reformulates token pruning as a column subset selection problem and explicitly minimizes reconstruction error. By iteratively selecting tokens with large projection residuals, SPARE performs reconstruction-driven pruning beyond angular diversity. Moreover, we reveal a counterintuitive anti-relevance phenomenon: tokens with lower image-text relevance score can better preserve contextual information. Based on this finding, we incorporate anti-relevance into SPARE as an additional selection criterion to promote context-aware token selection. Extensive experiments across multiple VLMs and benchmarks demonstrate that SPARE consistently achieves state-of-the-art performance, with strong gains on compositional tasks. When applied to LLaVA, SPARE removes up to 94% of visual tokens while retaining 95% of the baseline performance, all in a fully training-free manner.

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