arXiv AI June 2, 2026 By Hanze Li, Yaosong Du, Zhibo Yao, Mengyao Zeng, Xiuqi Ge, Xiande Huang

Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals

arXiv:2503. 06473v5 Announce Type: replace-cross Abstract: Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly advanced network architectures.

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 30

Data-Free Pruning of Self-Attention Layers in LLMs

arXiv:2512.20636v2 Announce Type: replace-cross Abstract: Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppr...

By Dhananjay Saikumar, Blesson Varghese

llms fine-tuning efficiency

arXiv Computer Vision

Sep 10

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Pruning

arXiv:2608.06411v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by...

By Yuyao Sun, Tao Deng, Shuang Li, Deqing Wang, Hao Geng, Minjun Yu

llms efficiency multimodal benchmarks

arXiv Machine Learning

Oct 7

DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models

arXiv:2610.08341v1 Announce Type: cross Abstract: Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redu...

By Shuo Yang, Changbai Li, Linlin Yang, Huobin Tan, Rongyu Chen, Tongfei Chen, Tian Wang, Sheng Xu, Baochang Zhang

llms efficiency multimodal benchmarks

arXiv AI

Aug 10

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin

arXiv:2608. 06411v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of processing numerous visual tokens.

By Yuyao Sun, Tao Deng, Shuang Li, Deqing Wang, Hao Geng, Minjun Yu

llms efficiency multimodal benchmarks

arXiv Machine Learning

Aug 11

High-Layer Attention Pruning with Rescaling

arXiv:2507. 01900v3 Announce Type: replace-cross Abstract: Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency.

By Songtao Liu, Peng Liu

llms efficiency

arXiv AI

Jun 29

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

arXiv:2604. 00757v2 Announce Type: replace-cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens.

By Dong-Jae Lee, Sunghyun Baek, Junmo Kim

llms efficiency

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Data-Free Pruning of Self-Attention Layers in LLMs

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Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin

High-Layer Attention Pruning with Rescaling

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models