arXiv AI July 3, 2026 By Xuehui Wang, Xuankun Yang, Wei Shen

Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning

arXiv:2607. 02484v1 Announce Type: cross Abstract: Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instructions and fine-grained queries.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 2

SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models

SinkPruner is a training‑free framework that prunes visual tokens for multimodal large language models by first removing high‑norm redundant tokens with a visual sanitizer and then selectively keeping tokens that align with the text query using a text‑guided pruner. The coarse‑to‑fine design reduces attention sink and dispersion, enabling an 89% token reduction while preserving 96.5% of LLaVA‑1.5’s performance and 91.8% of Qwen2.5‑VL’s performance across twelve image‑language and four video‑language benchmarks. The visual sanitizer also improves existing pruning methods, showing strong transferability.

By Shiyu Li, Zi-Yuan Hu, Shijia Huang, Yanyang Li, Yiwu Zhong, Liwei Wang

llms efficiency multimodal benchmarks

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

arXiv AI

Jul 10

Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models

arXiv:2604. 11530v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information.

By Yvon Apedo, Martyna Poreba, Michal Szczepanski, Samia Bouchafa

llms efficiency multimodal safety

arXiv AI

Jun 26

TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference

arXiv:2606. 27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens, which introduces substantial computational overhead.

By Tinghao Wang, Yichen Guo, Rui Huang, Zheng Lu, Qizhe Zhang, Chenxi Li, Yuan Zhang, Jiajun Cao, Zhirong Shen, Yaosong Du, Guangyan Gan, Wenya Wang, Lin William Cong, Shanghang Zhang

llms efficiency multimodal benchmarks safety

arXiv Computer Vision

Sep 22

Layer-Aware Position Embeddings for Visual Token Pruning in Multimodal Large Language Models

arXiv:2609.23715v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) incur substantial computational overhead due to the reliance on hundreds of visual tokens to represent images....

By Yahong Wang, Zhangkai Ni, Juncheng Wu, Yuyin Zhou, Ying Wen, Lianghua He

llms rag efficiency multimodal benchmarks

arXiv Computer Vision

Sep 10

Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs

arXiv:2609.10346v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existin...

By Haiji Liang, Pengfei Zhou, Zhenglin Wan, Wei Wang, Yang You, Wangbo Zhao

llms efficiency multimodal benchmarks

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SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models

TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference

Layer-Aware Position Embeddings for Visual Token Pruning in Multimodal Large Language Models

Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs