DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models

• URL: http://arxiv.org/abs/2608.04496v1
• Date: Wed, 05 Aug 2026 06:32:25 GMT
• Title: DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models
• Abstract summary: Visual inputs in vision-language models are often encoded into substantially longer token sequences than text.<n>Recent methods address this bottleneck by scoring token importance and pruning low-scoring tokens in a single pass.<n>Motivated by this insight, we introduce DIVE, a training-free framework that recasts visual-token pruning as dynamic evidence construction.
• Score: 13.866355425347384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference. Abundant recent methods address this bottleneck by scoring token importance and pruning low-scoring tokens in a single pass. However, one-shot scoring is insufficient because a token's prompt-relevant usefulness depends on the evidence already retained. Motivated by this insight, we introduce DIVE (Dynamic Iterative Visual Evidence Construction), a training-free framework that recasts visual-token pruning as dynamic evidence construction. DIVE repeatedly selects the remaining token with the highest residual-conditioned score, updates the visual and prompt residuals to discount the evidence already explained, and re-evaluates the remaining tokens. This select-update-re-evaluate process builds a retained set of complementary, prompt-relevant evidence. Experiments across eight image-understanding benchmarks show that DIVE consistently preserves performance across token budgets. With an 88.9% reduction in visual tokens, DIVE retains 98.2% of the uncompressed model's average performance. Code is available at https://github.com/Zhong-Chenchen/DIVE.git.

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