BFA++: Hierarchical Best-Feature-Aware Token Prune for Multi-View Vision Language Action Model
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；LimX Dynamic（LimX动态）；Nanjing University（南京大学）；Nanyang Technological University（南洋理工大学）；Zhejiang University（浙江大学）
Vision-Language-Action (VLA) models have achieved significant breakthroughs by leveraging Large Vision Language Models (VLMs) to jointly interpret instructions and visual inputs. However, the substantial increase in visual tokens, particularly from multi-view inputs, poses serious challenges to real-time robotic manipulation. Existing acceleration techniques for VLMs, such as token pruning, often result in degraded performance when directly applied to VLA models, as they overlook the relationships between different views and fail to account for the dynamic and task-specific characteristics of robotic operation. To address this, we propose BFA++, a dynamic token pruning framework designed specifically for VLA models. BFA++ introduces a hierarchical pruning strategy guided by two-level importance predictors: an intra-view predictor highlights task-relevant regions within each image to suppress spatial noise, while an inter-view predictor identifies critical camera views throughout different manipulation phases to reduce cross-view redundancy. This design enables efficient token selection while preserving essential visual cues, resulting in improved computational efficiency and higher manipulation success rates. Evaluations on the RoboTwin benchmark and real-world robotic tasks demonstrate that BFA++ consistently outperforms existing methods. BFA++ improves the success rate by about 10% on both the π0 and RDT models, achieving speedup of 1.8X and 1.5X, respectively. Our results highlight that context-sensitive and task-aware token pruning serves as a more effective strategy than full visual processing, enabling faster inference and improved manipulation accuracy in real-world robotic systems.
RCP: Representation Consistency Pruner for Mitigating Distribution Shift in Large Vision-Language Models
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学机器学习系）；School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）机器人与先进制造学院）
E2S-Pruner: Progressive Two-Stage Evidence Fusion for Visual Token Pruning in Vision-Language Models
Digital Environment Research Institute (DERI), Queen Mary University of London（伦敦大学玛丽女王学院数字环境研究所（DERI））；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（华东师范大学上海人工智能教育研究院）；National Institute of Education, Nanyang Technological University（南洋理工大学国家教育学院）；School of Computer Science and Engineering, Jiangsu University of Science and Technology（江苏科技大学计算机科学与工程学院）
The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
LiAuto Inc.（LiAuto公司）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Where and How to Prune: An Empirical Study of Visual Token Pruning for GUI Agent Navigation
Sichuan University（四川大学）；Sun Yat-sen University（中山大学）；Australian National University（澳大利亚国立大学）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）
ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Carizon；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Carnegie Mellon University（卡内基梅隆大学）；Sichuan University（四川大学）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）
SafePTR: Token-Level Jailbreak Defense in Multimodal LLMs via Prune-then-Restore Mechanism
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳研究院）；Southwestern University of Finance and Economics（西南财经大学）；Engineering Research Center of Intelligent Finance, Ministry of Education（教育部智能金融工程研究中心）；Tongji University（同济大学）
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机学院）；Cognitive AI Lab, Shanghai Huawei Technologies, China（上海华为技术有限公司认知人工智能实验室）
ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models
Bosch Corporate Research, Bosch (China) Investment Ltd.（博世企业研究，博世（中国）投资有限公司）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）
SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Samsung AI Center Toronto（三星多伦多人工智能中心）；Stanford University（斯坦福大学）；University of Waterloo（滑铁卢大学）；University of British Columbia（不列颠哥伦比亚大学）；Alan Turing Institute（艾伦·图灵研究所）；Tsinghua University（清华大学）；York University（约克大学）；NVIDIA（英伟达）
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院，北京，中国）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（人工智能学院，中国科学院大学，北京，中国）；Fanyu AI Laboratory, Zhongke Fanyu Technology Co., Ltd, Beijing, China（凡语AI实验室，中科创始人技术有限公司，北京，中国）
Accelerating Multimodal Large Language Models with Prior-Corrected Token Reduction
School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字化服务技术重点实验室）；Hong Kong Polytechnic University（香港理工大学）；The University of Southern Queensland（南昆士兰大学）
One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination
National Key Laboratory for Novel Software Technology, Nanjing University, China（南京大学新型软件技术国家重点实验室）；School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院）
ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs
School of Future Technology, Dalian University of Technology（大连理工大学未来技术学院）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）；OPPO Research Institute（OPPO研究院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）
