LVLM Pruning

LVLM: Large Vision-Language Model

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31 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models
Oct 6, 2026Shuo Yang, Changbai Li, Linlin Yang +6Multimodal Large Language Models Token Pruning
Beihang University · Communication University of China · National University of Singapore
ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs
Sep 28, 2026Xu Li, Yuxuan Liang, Yi Zheng +6Efficient VLM Inference Large Vision-Language Models
College of Computer Science and Artificial Intelligence Fudan University, Shanghai, China
VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
Sep 21, 2026Guangchuan Lv, Dianxing Shi, Dingjie FuEfficient VLM Inference Large Vision-Language Models
Northeastern University · Beihang University · Huazhong University of Science and Technology
QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning
Sep 17, 2026Shengli He, Yongchao Liang, Roumeng He +4Multimodal Large Language Models Visual Token Pruning
Guizhou University · Shanghai Ocean University
StepPrune: Adaptive Sequential Visual Token Selection across Multimodal Large Language Models
Sep 12, 2026Hansen Zhang, Landi He, Mingde Yao +1Efficient VLM Inference Visual Token Pruning
Shenzhen University of Advanced Technology, Shenzhen, China · School of Computer Science, Nanjing University, Nanjing, China · CUHK MMLab, CPII under InnoHK, Hong Kong, China
Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization
Sep 2, 2026Qingchan Zhu, Weihang You, Hanqi Jiang +3Efficient VLM Inference Visual Token Pruning
School of Computing, University of Georgia · College of Engineering, Northeastern University
S $^2$ Prune: Spatially Structured Visual Token Pruning for Multimodal Large Language Models
Sep 1, 2026Yuanyuan Jia, Shunpu Tang, Qianqian YangEfficient VLM Inference Multimodal Large Language Models
College of Information Science and Electronic Engineering, Zhejiang University Hangzhou, China
An AI4AI Framework for Visual Token Pruning
Aug 7, 2026Zhen Liu, Wenli Huang, Wei Song +3Automated Algorithm Discovery Efficient VLM Inference
Xi’an Jiaotong University · Ningbo University of Technology · 3North China University of Technology +3
Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
Aug 7, 2026Minseok Kang, Hyunwoo Kim, Chanyoung Kim +3Vision-Language Models Efficient VLM Inference
Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea · School of Computer Science and Engineering, Soongsil University, Seoul, Korea · Department of Computer Science, Kookmin University, Seoul, Korea
SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models
Aug 4, 2026Yaozhi Wen, Jialong Guo, Zhenliang Ni +2Efficient VLM Inference Structured Pruning
Huawei Technologies
DiffPrune: differentiable information throttling for token pruning in vision-language models
Aug 3, 2026Landi He, Mingde Yao, Shawn Young +1Vision-Language Models Efficient VLM Inference
Shenzhen University of Advanced Technology · CUHK MMLab, CPII under InnoHK
SepPrune:A Separator-based Pruning Framework for Efficient Multimodal Large Language Models
Jul 28, 2026Yuchen Wang, Qihui Zhu, Yang Liu +2Efficient Multimodal Inference Multimodal Large Language Models
MoE Key Lab of BIPC, NEL-BITA, University of Science and Technology of China, Hefei, China · ChangXin Memory Technologies, Hefei, China · APKL of BIIP, IAI, Hefei Comprehensive National Science Center, Hefei, China
Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding
Jun 23, 2026Qitong Wang, Fan Du, Pranav Maneriker +2Efficient VLM Inference Egocentric Vision
Dolby Laboratories, Inc., 1275 Market St, San Francisco, California, 94103, USA · Department of Computer and Information Sciences at the University of Delaware, Newark, DE 19711, USA · Dolby Laboratories, Inc.
OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning
May 28, 2026Geng Li, Guohao Chen, Ting Chen +6Efficient VLM Inference Large Vision-Language Models
1Nanyang Technological University (NTU)
AsymVLM: Asymmetric Token Pruning for Efficient Vision-Language Model Inference
May 28, 2026Yilin Feng, Ahmed Burak Gulhan, Mahmut Taylan KandemirLLM Pruning Vision-Language Models
The Pennsylvania State University
MuCRASP: Multimodal Chain-of-thought Reasoning aware Structured Pruning
May 25, 2026Aritra Dutta, Somak AdityaVision-Language Models Efficient VLM Inference
Indian Institute of Technology, Kharagpur
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Apr 27, 2026Yiran Huang, Lukas Thede, Massimiliano Mancini +2Large Vision-Language Models Structured Pruning
Technical University of Munich, Germany · Munich Center for Machine Learning, Germany. · Helmholtz Munich, Germany. +3
Towards Joint Quantization and Token Pruning of Vision-Language Models
Apr 19, 2026Xinqing Li, Xin He, Xindong Zhang +3Vision-Language Models VLM Quantization
VCIP, College of Computer Science, Nankai University · School of Computer Science and Engineering, Tianjin University of Technology · OPPO Research Institute +3
Topology-Aware Layer Pruning for Large Vision-Language Models
Apr 14, 2026Pengcheng Zheng, Chaoning Zhang, Ya Wen +10LVLM Pruning Neural Network Pruning
University of Electronic Science and Technology of China · Kyung Hee University · Nota Inc. +2
Collaborative Multi-Mode Pruning for Vision-Language Models
Apr 3, 2026Zimeng Wu, Yunhong Wang, Donghao Wang +1LVLM Pruning Efficient VLM Inference
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China · School of Computer Science and Engineering, Beihang University, Beijing, China
QAPruner: Quantization-Aware Vision Token Pruning for Multimodal Large Language Models
Apr 3, 2026Xinhao Wang, Zhonyu Xia, Zhiwei Lin +2Visual Token Pruning LVLM Pruning
Wangxuan Institute of Computer Technology, Peking University
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Apr 1, 2026Dong-Jae Lee, Sunghyun Baek, Junmo KimEfficient VLM Inference Large Vision-Language Models
KAIST, South Korea
Mechanistically Interpreting Compression in Vision-Language Models
Mar 26, 2026Veeraraju Elluru, Arth Singh, Roberto Aguero +3Cultural Safety Evaluation of VLMs LVLM Pruning
Indian Institute of Technology Jodhpur · AIM Intelligence · National Institute of Technology Agartala +3
ReDiPrune: Relevance-Diversity Pre-Projection Token Pruning for Efficient Multimodal LLMs
Mar 25, 2026An Yu, Ting Yu Tsai, Zhenfei Zhang +3Efficient VLM Inference Token Pruning
University at Albany, SUNY · Peking University
Understanding Pruning Regimes in Vision-Language Models Through Domain-Aware Layer Selection
Mar 17, 2026Saeed Khaki, Nima Safaei, Kamal GinotraLVLM Pruning Neural Network Pruning
Ohio State University
Mostly Text, Smart Visuals: Asymmetric Text-Visual Pruning for Large Vision-Language Models
Mar 16, 2026Sijie Li, Biao Qian, Jungong HanLVLM Pruning Neural Network Pruning
University of Sheffield, UK · Tsinghua University, China
ASAP: Attention-Shift-Aware Pruning for Efficient LVLM Inference
Mar 15, 2026Surendra Pathak, Bo HanEfficient VLM Inference Visual Token Pruning
George Mason University, Fairfax, VA, USA
Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
Mar 11, 2026Sihan Cao, Jianwei Zhang, Pengcheng Zheng +7Efficient VLM Inference Visual Token Pruning
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen, China · College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, China
HiPP-Prune: Hierarchical Preference-Conditioned Structured Pruning for Vision-Language Models
Mar 6, 2026Lincen Bai, Hedi Tabia, Raul Santos-RodriguezLVLM Pruning Neural Network Pruning
Université Paris-Saclay · University of Bristol
OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport
Feb 22, 2026Xiwen Chen, Wenhui Zhu, Gen Li +9Visual Token Pruning Efficient VLM Inference
Morgan Stanley · Clemson University · Arizona State University +4

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

Shuo Yang, Changbai Li, Linlin Yang, Huobin Tan+5

Beihang University · Communication University of China · National University of Singapore

Multimodal Large Language Models Token Pruning Multimodal Token Compression Visual Token Pruning LVLM Pruning

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Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redundancy or text-vision attention. However, they frequently suffer from semantic degradation due to their task-agnostic design or unreliable attention estimates. Based on our empirical analysis, we have found that this issue arises because salient tokens in shallow layers persistently suppress emerging semantic ones through numerical inertia, leading to premature discarding of signals crucial for deep reasoning. To address the aforementioned issue, from the task-oriented aspects, we first reformulate training-free pruning as a minimization of the distortion in the final task loss and derive a tractable, token-wise upper bound to serve as a surrogate objective. Specifically, this formulation inherently reveals a previously neglected inter-layer term that accounts for gradients across layers. Accordingly, for the implementation, we propose DIPrune, a rank-based framework that employs a dual importance scoring mechanism to jointly optimize intra-layer static feature saliency and inter-layer dynamic semantic evolution. Extensive experiments on LLaVA and Qwen-VL demonstrate that DIPrune consistently achieves state-of-the-art results.