VLM Quantization

VLM: Vision-Language Model

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11 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Latest papers 32

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ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference
Oct 6, 2026Zou Qingyun, Bin Gao, Wenju Zhao +3Energy-Efficient ML VLM Quantization
National University of Singapore · Huazhong University of Science and Technology
Beyond In-Distribution Preservation: Recovering Generalization in Quantized VLAs via Vulnerability-Oriented Tuning
Oct 5, 2026Shen Ruan, Wenchang Gao, Jin Wang +4VLM Quantization OOD Generalization
Soochow University · Independent Researcher · Shanghai Jiao Tong University +2
Calibrate the Decisions That Change the Future: On-Policy Post-Training Quantization for Multimodal Large Language Models
Sep 29, 2026Wenxiao Fan, Jingling Fu, Lichen Ma +6LLM Quantization VLM Quantization
School of Computer Science and Technology, Beijing Institute of Technology · Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University
EdgeVLN: Runtime-Aware Deployment Ready Quantized Vision Language Navigation Model
Sep 28, 2026Rithvik Jonna, Man Namgung, Aakash Gurram +1Visual Navigation VLM Quantization
Department of Electrical and Computer Engineering Johns Hopkins University · Laboratory for Computational Sensing and Robotics Johns Hopkins University
SubRot: Signed Gradient Subspace Calibration for VLM Rotation Quantization
Sep 28, 2026Zhenhao Shang, Haizhao Jing, Haokui Zhang +4VLM Quantization Rotation-Based Quantization
Northwest Polytechnical University · Nanjing University of Science and Technology · Intellifusion +1
P4Q: Co-designing Token Pruning and Quantization for Vision-Language Model Acceleration
Sep 28, 2026Haizhao Jing, Zhenhao Shang, Haokui Zhang +2VLM Quantization Efficient VLM Inference
Northwest Polytechnical University · Intellifusion
Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models
Sep 28, 2026Minchan Kang, Kyeonghye Park, Seungyeon Sa +3VLM Quantization Large Vision-Language Models
Korea Advanced Institute of Science and Technology (KAIST) · Hanbat National University
GHOST-Q: Towards Studying Grounding Hallucinations Overlooked Under Same-score TradeOffs in Quantized VLMS
Sep 24, 2026Saim Rehman, Muhammad ShafiqueVLM Evaluation VLM Quantization
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE
RGSQ: Riemannian Geometry-Sensitive Quantization for Large Vision-Language Models
Sep 21, 2026Zhiping Wu, Dongdong Ren, Yangchengyu Zhou +4VLM Quantization Large Vision-Language Models
State Key Laboratory for Novel Software Technology and the School of Intelligence Science and Technology, Nanjing University, Suzhou Campus, Suzhou 215163, China
SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models
Sep 21, 2026Kewei Zhang, Zheng Chen, Haotong Qin +1VLM Quantization Efficient VLM Inference
Shanghai Jiao Tong University · The Hong Kong Polytechnic University
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
Sep 21, 2026Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen +5VLM Quantization Efficient VLA Model Inference
VinRobotics, Vietnam · AICV Lab, EECS Department, University of Arkansas · School of Advanced Manufacturing and Robotics, Peking University, China +2
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration
Sep 17, 2026Yuan Liao, Jae-sun SeoAI Accelerator Inference VLM Quantization
Cornell Tech, Cornell University New York, NY, USA
Efficient Quantization-Aware Distillation with Cross-Modal Alignment for Edge Vision-Language Models
Sep 15, 2026Jinwoo Jeon, GyuYeop Do, Yubin Lim +4VLM Quantization Efficient VLM Inference
Korea University · Seoul National University
Bigger or Cheaper? Scale and Quantization Effects on Uncertainty Signals in Vision-Language Models Under Image Degradation
Jul 27, 2026M M Asif FerdousVLM Robustness Selective Prediction
Independent Researcher
C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs
Jul 23, 2026Jiameng Li, Han Zhou, Matthew B. BlaschkoVLM Quantization Post-Training Quantization
1KU Leuven · 2Tiangong University
Beyond Independent Optimization: Compression, MoE Routing, and Quantization Interactions in Multimodal Edge Intelligence
Jul 23, 2026Jay Gor, Karm Dave, Akshita Abrol +3Efficient Multimodal Inference VLM Quantization
Nirma University · Singapore Institute of Technology, Singapore · Department of CE-AI and Big Data, Marwadi University
StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design
Jul 20, 2026Yin Wang, Haotian Hu, Jineng Han +4Vision-Language Models VLM Quantization
StepX Team
Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment
Jul 9, 2026Hyeju Shin, Chorwon Kim, Ryangsoo Kim +2Mixed-Precision Quantization Vision-Language Models
1ETRI, Republic of Korea.
SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models
Jul 2, 2026Qi Lyu, Jiahua Dong, Baichen Liu +7Model Compression Vision-Language Models
State Key Laboratory of Robotics and Intelligent Systems · Shenyang Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences +2
Mix-QVLA: Task-Evidence-Aware Mixed-Precision Quantization of Vision-Language-Action Models
Jun 17, 2026Navin Ranjan, Andreas SavakisMixed-Precision Quantization VLM Quantization
Rochester Institute of Technology Rochester, New York 14623, USA
MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs
Jun 15, 2026Yuanteng Chen, Peisong Wang, Zhilei Liu +9Mixed-Precision Quantization LLM Quantization
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · 3Zhongguancun Academy
Ω-QVLA: Robust Quantization for Vision-Language-Action Models via Composite Rotation and Per-step Scaling
May 27, 2026Xinyu Wang, Mingze Li, Sicheng Lyu +6VLM Quantization Efficient VLA Model Inference
McGill University · Université de Montréal · Mila – Quebec AI Institute +2
The Rescue Effect: Spatio-Semantic Early Exit Bypasses Quantization Collapse in CLIP
May 26, 2026Kahyeon Nam, Hyesong ChoiVLM Quantization Activation Quantization
Soongsil University Seoul, Republic of Korea
MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization
May 20, 2026Zhong Wang, Zukang Xu, Xing Hu +1Mixed-Precision Quantization Vision-Language Models
Bauman Moscow State Technical University · Houmo AI
ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
May 19, 2026Arash Akbari, Arman Akbari, Masih Eskandar +11Mixed-Precision Quantization VLM Quantization
1Northeastern University · University of Georgia · 3Cisco Systems
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models
May 19, 2026Yi Zhong, Haotong Qin, Xindong Zhang +2Vision-Language Models VLM Quantization
VCIP, College of Computer Science, Nankai University · D-ITET, ETH Zürich · OPPO Research Institute +1
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
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
DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
Mar 9, 2026Zihao Zheng, Hangyu Cao, Chibang Tao +9VLM Quantization Efficient VLA Model Inference
School of Computer Science, Peking University, Beijing, China. · School of Software Engineering, South China University of Technology, Guangzhou, China. · School of Artificial Intelligence, Beijing Normal University, Beijing, China. +1
Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
Jan 30, 2026Yanlong Chen, Amirhossein Habibian, Luca Benini +1Cross-Modal Knowledge Distillation Vision-Language Models
Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Qualcomm AI Research, Amsterdam, the Netherlands +1

Oct 6, 2026, cs.ROJ/K move · Enter open · S save

ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference

Zou Qingyun, Bin Gao, Wenju Zhao, Weng-Fai Wong+2

National University of Singapore · Huazhong University of Science and Technology

Energy-Efficient ML VLM Quantization Efficient VLA Model Inference

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Vision-language-action (VLA) policies repeatedly invoke inference to control robots, making graphics processing unit (GPU) energy a recurring cost of task execution. Reducing energy per inference call, however, may not reduce energy per successful task if numerical errors increase failures or slower inference prolongs execution. We therefore target GPU energy per successful task while preserving task success and keeping the inference-latency increase within 10%. Our approach builds on two observations: quantization sensitivity varies across action classes, model layers, and weights versus activations; and numerical precision changes the workload, shifting favorable GPU operating points. We introduce ActTune, an action-aware framework that connects layer-wise precision allocation with workload-dependent GPU operating-point selection over requested frequency--power-cap pairs. A lightweight decision tree learns its splits and leaf precision configurations directly from configuration action errors, then selects precision before each policy call. The controller forecasts the next workload and applies the selected GPU operating point asynchronously using a lookup table calibrated under a latency budget. A shared resident quantized weight bank enables configuration switching without weight reconstruction or additional policy evaluations. On LIBERO, a benchmark for lifelong robot learning, ActTune improves mean task success by up to 2.3% relative to state of the art. Relative to the original BF16 implementations, it delivers up to

2.02\times

faster inference and, with GPU operating-point adaptation, reduces energy per successful task by up to 76.8%.

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Latest papers 32