Efficient VLM Inference

VLM: Vision-Language Model

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38 papers in the last four weeks, up 100% on the four weeks before. 0.4% of all new papers.

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Dynamic Execution Commitment of Vision-Language-Action Models
May 12, 2026Feng Chen, Xianghui Wang, Yuxuan Chen +4VLM Robustness Efficient VLM Inference
University of Adelaide · Sichuan University · Shanghai Jiao Tong University +3
LDDR: Linear-DPP-Based Dynamic-Resolution Frame Sampling for Video MLLMs
May 12, 2026Jingfeng Chen, Jiawen Qian, Wendi Deng +5Efficient VLM Inference Multimodal Large Language Models
1Carnegie Mellon University · 2Individual Researcher · 3National University Singapore +2
GridProbe: Posterior-Probing for Adaptive Test-Time Compute in Long-Video VLMs
May 11, 2026Mohamed Eltahir, Lama Ayash, Ali Habibullah +2Efficient VLM Inference Long-Video Understanding
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Department of Computer Science, Edge Hill University, Ormskirk, England
LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models
May 11, 2026Nikolaos Gkalelis, Vasileios MezarisVision-Language Models Efficient VLM Inference
CERTH-ITI, Thessaloniki, Greece, 57001
EchoPrune: Interpreting Redundancy as Temporal Echoes for Efficient VideoLLMs
May 11, 2026Jiameng Li, Minye Wu, Jiezhang Cao +2Efficient VLM Inference Token Pruning
KU Leuven · Shanghai Jiaotong University · Weill Cornell Medicine
ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning
May 11, 2026Yuna Lee, Kyoungho Min, Yulhwa KimEfficient VLM Inference Large Vision-Language Models
Department of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea · Department of Semiconductor Systems Engineering, Sungkyunkwan University, Republic of Korea
LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
May 11, 2026Boyang Shen, Kaixiang Yang, Hao Wang +4Efficient VLM Inference Efficient VLA Model Inference
1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)
Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT
May 10, 2026Alaa Asfour, Christopher Indris, Leihan Chen +2Cross-Modal Knowledge Distillation 3D Spatial Reasoning
Department of Computer Science, Toronto Metropolitan University, Toronto, ON, Canada
Evading Visual Aphasia: Contrastive Adaptive Semantic Token Pruning for Vision-Language Models
May 10, 2026Jie Ma, Yihang Liu, Zhike Qiu +2Efficient VLM Inference Vision-Language Grounding
Xiamen University
Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs
May 10, 2026Yigui Feng, Qinglin Wang, Yang Liu +1Efficient VLM Inference Visual Tokenization
The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China · College of Computer Science, National University of Defense Technology, Deya Road 109, Changsha, Hunan 410073, China · The Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, China +1
LLaVA-UHD v4: What Makes Efficient Visual Encoding in MLLMs?
May 9, 2026Kechen Fang, Yihua Qin, Chongyi Wang +3Efficient VLM Inference Multimodal Large Language Models
Tsinghua University · ModelBest
How Many Visual Tokens Do Multimodal Language Models Need? Scaling Visual Token Pruning with F^3A
May 9, 2026YiJie Huang, Yiqun Zhang, Zhuoyue Jia +7Efficient VLM Inference Large Vision-Language Models
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · Shanghai Artificial Intelligence Laboratory · School of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, China
DAPE: Dynamic Non-uniform Alignment and Progressive Detail Enhancement Techniques for Improving the Performance of Efficient Visual Language Models
May 9, 2026Mengyuan Tian, Qiyan Zhao, Yanan Wang +1VLM Adaptation Efficient VLM Inference
The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen 361005, China · Fujian Key Laboratory of Pattern Recognition and Image Understanding, School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference
May 9, 2026Wei Cao, Hao Xu, Xiaolei DiaoZero-Shot Learning Efficient VLM Inference
1Jilin University, China · University College London, UK
Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding
May 8, 2026Hang Wu, Sherin Mary Mathews, Yujun Cai +2Streaming Video Understanding Efficient VLM Inference
University of California, Merced · US Bank · University of Queensland
TTF: Temporal Token Fusion for Efficient Video-Language Model
May 8, 2026Simin Huo, Ning LIToken Merging Efficient VLM Inference
Shanghai Jiao Tong University
GeoStack: A Framework for Quasi-Abelian Knowledge Composition in VLMs
May 7, 2026Pranav Mantini, Shishir K. ShahVision-Language Models VLM Adaptation
Department of Computer Science University of Houston · The University of Oklahoma School of Computer Science
VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading
May 7, 2026Cheng Xu, Xiaofeng Hou, Jiacheng Liu +1Expert Offloading Vision-Language Models
Shanghai Jiao Tong University Minhang Qu, Shanghai, China
VideoRouter: Query-Adaptive Dual Routing for Efficient Long-Video Understanding
May 7, 2026Kuanwei Lin, Wenhao Zhang, Ge LiEfficient VLM Inference Long-Video Understanding
School of Electronic and Computer Engineering, Peking University
Visual Text Compression as Measure Transport
May 6, 2026Lv Tang, Tianyi Zheng, Yang Liu +2Efficient VLM Inference Visual Tokenization
University of Alberta1 · vivo Mobile Communication Co., Ltd2 · Tsinghua University3
ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation
May 6, 2026Wei Li, Jizhihui Liu, Li Yixing +3Multi-View Consistency Spatiotemporal Reasoning
Harbin Institute of Technology, Shenzhen · State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE, China · Shenzhen Loop Area Institute
VLMaxxing through FrameMogging Training-Free Anti-Recomputation for Video Vision-Language Models
May 5, 2026JF Bastien, Sam D'AmicoEfficient VLM Inference Video-Language Models
Look, they said attention was all you needed, well we got your attention with this title.
Understanding Asynchronous Inference Methods for Vision-Language-Action Models
May 4, 2026Ayoub AgouzoulRobotic Control Efficient VLM Inference
École polytechnique · Systems Group, ETH Zürich
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
May 4, 2026Yudong Liu, Yuan Li, Zijia Tang +12Efficient VLM Inference Efficient VLA Model Inference
1Duke University · 2Qualcomm AI Research · University of Florida
LAGO: Language-Guided Adaptive Object-Region Focus for Zero-Shot Visual-Text Alignment
May 4, 2026Junyi Hu, Qiji Zhou, Lei Zhang +1VLM Robustness Efficient VLM Inference
Beijing Jiaotong University · Westlake University · Rochester Institute of Technology
WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization
May 4, 2026Wei Tao, Xiaoyang Qu, Peiqiang Wang +4Mixed-Precision Quantization VLM Quantization
Huazhong University of Science and Technology, Wuhan, Hubei, China · Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China · Tsinghua University, Shenzhen International Graduate School, Shenzhen, Guangdong, China
CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding
May 4, 2026Yuanyuan Jia, Shunpu Tang, Qianqian YangEfficient VLM Inference Edge Computing
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
Decouple and Cache: KV Cache Construction for Streaming Video Understanding
May 3, 2026Zhanzhong Pang, Dibyadip Chatterjee, Fadime Sener +1Streaming Video Understanding KV Caching
National University of Singapore.
Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models
May 3, 2026Martin Q. Ma, Willis Guo, Aditya Agrawal +4Active Perception Efficient VLM Inference
Carnegie Mellon University · MIT
Make Your LVLM KV Cache More Lightweight
May 1, 2026Xihao Chen, Yangyang Guo, Roger ZimmermannKV Caching Efficient VLM Inference
Integrative Sciences and Engineering Programme, National University of Singapore · School of Computing, National University of Singapore

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Dynamic Execution Commitment of Vision-Language-Action Models

Feng Chen, Xianghui Wang, Yuxuan Chen, Boying Li+3

University of Adelaide · Sichuan University · Shanghai Jiao Tong University+3

VLM Robustness Efficient VLM Inference Vision-Language-Action Models Action Chunking

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Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level actions in a single forward pass, to amortize the inference cost of large-scale backbones and reduce per-step latency. However, committing these multi-step predictions to real-world execution requires balancing success rate against inference efficiency, a decision typically governed by fixed execution horizons tuned per task. Such heuristics ignore the state-dependent nature of predictive reliability, leading to brittle performance in dynamic or out-of-distribution settings. In this paper, we introduce A3, an Adaptive Action Acceptance mechanism that reframes dynamic execution commitment as a self-speculative prefix verification problem. A3 first computes a trajectory-wise consensus score of actions via group sampling, then selects a representative draft and prioritizes downstream verification. Specifically, it enforces: (1) consensus-ordered conditional invariance, which validates low-consensus actions by judging whether they remain consistent when re-decoded conditioned on high-consensus actions; and (2) prefix-closed sequential consistency, which guarantees physical rollout integrity by accepting only the longest continuous sequence of verified actions starting from the beginning. Consequently, the execution horizon emerges as the longest verifiable prefix satisfying both internal model logic and sequential execution constraints. Experiments across diverse VLA models and benchmarks demonstrate that A3 eliminates the need for manual horizon tuning while achieving a superior trade-off between execution robustness and inference throughput.

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