arXiv Machine Learning August 6, 2026 By Chen Zhong, Xiao An, Zijie Wang, Jiepan Li, Guangyi Yang, Wei He

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

Read the original on arXiv Machine Learning →

arXiv:2608. 04496v1 Announce Type: cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 9

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

arXiv:2607. 07033v1 Announce Type: cross Abstract: Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are redundant for a given query.

By Kyuan Oh, Bumsoo Kim

llms efficiency multimodal benchmarks

Hugging Face Trending Papers

Aug 11

When Vision Becomes Text: Visual Token Pruning via Cross-Modal Residual Guidance in VLMs

Abundant visual information strengthens vision-language model (VLM) perception, yet massive visual tokens raise inference costs. Existing visual token pruning methods rely on similarity-based guidance, which exploits pairwise text-vision and vision-vision token correlations for compression.

llms efficiency multimodal

arXiv AI

Jun 3

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics

arXiv:2606. 03569v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference.

By Jiahui Wang, Kai Zhang, Mai Han, Huanghe Zhang

llms efficiency multimodal safety

arXiv AI

Jun 2

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

arXiv:2605. 13178v2 Announce Type: replace-cross Abstract: In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead.

By Sangin Lee, Yukyung Choi

llms fine-tuning efficiency multimodal

arXiv Machine Learning

Aug 4

DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models

arXiv:2608. 01821v1 Announce Type: cross Abstract: Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial recurring inference cost.

By Yongkang Zhou, Xiang Xia, Cheng Yan, Fan Xu, Wuyang Zhang

llms diffusion efficiency multimodal benchmarks

arXiv AI

Aug 11

Evidence-RL: Towards Evidence-intensive Visual Reasoning

arXiv:2608. 08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.

By Haojie Huang, Xinlei Yu, Chengming Xu, Zhangquan Chen, Cheng Yang, Qingdong He, Yu Yang, Jiangning Zhang, Xiaobin Hu

llms reinforcement-learning multimodal benchmarks

Related stories

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

When Vision Becomes Text: Visual Token Pruning via Cross-Modal Residual Guidance in VLMs

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models

Evidence-RL: Towards Evidence-intensive Visual Reasoning