VLM Robustness

VLM: Vision-Language Model

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26 papers in the last four weeks, up 117% on the four weeks before. 0.3% of all new papers.

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Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models
Feb 11, 2026Sethuraman T, Savya Khosla, Aditi Tiwari +12VLM Robustness Video-Language Model Evaluation
University of Illinois Urbana-Champaign, USA · Adobe Research, USA · Qualtrics, USA +6
VideoSTF: Stress-Testing Output Repetition in Video Large Language Models
Feb 11, 2026Yuxin Cao, Wei Song, Shangzhi Xu +2VLM Evaluation VLM Robustness
National University of Singapore, Singapore · University of New South Wales, Australia · CSIRO’s Data61, Australia
Robustness of Vision Language Models Against Split-Image Harmful Input Attacks
Feb 8, 2026Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu +1VLM Robustness Adversarial Attacks on VLMs
Pennsylvania State University · Bangladesh University of Engineering and Technology
Same Answer, Different Representations: Hidden instability in VLMs
Feb 6, 2026Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena +6VLM Evaluation VLM Robustness
Sapienza University of Rome · University of Edinburgh · University of Oxford +5
Semantic Misalignment in Vision-Language Models under Perceptual Degradation
Jan 13, 2026Guo Cheng, Huang LiVLM Evaluation VLM Robustness
CLIMP: Contrastive Language-Image Mamba Pretraining
Jan 11, 2026Nimrod Shabtay, Itamar Zimerman, Eli Schwartz +1Cross-Modal Learning VLM Robustness
Tel-Aviv University · IBM Research
Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning
Jan 6, 2026Guoqiang Liang, Jianyi Wang, Zhonghua Wu +2VLM Robustness VLM Reasoning
National University of Singapore · S-Lab, Nanyang Technological University, Singapore · SenseTime Research
RGBT-GroundBench: Visual Grounding Beyond RGB in Complex Real-World Scenarios
Dec 31, 2025Tianyi Zhao, Jiawen Xi, Linhui Xiao +4VLM Robustness Visual Grounding
Institute of Artificial Intelligence, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China · Pengcheng Laboratory, Shenzhen, China · Shanghai Jiao Tong University, Shanghai, China
High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models
Dec 26, 2025Mengqi He, Xinyu Tian, Xin Shen +4VLM Robustness Adversarial Attacks on VLMs
The Australia National University Canberra, Australia · The University of Queensland Brisbane, Queensland, Australia · GE research Niskayuna, New York, USA
Counterfeit Answers: Adversarial Forgery against OCR-Free Document Visual Question Answering
Dec 4, 2025Marco Pintore, Maura Pintor, Dimosthenis Karatzas +1Visual Question Answering VLM Robustness
University of Cagliari, Italy · CINI, Italy · Computer Vision Center, UAB, Spain
6 Fingers, 1 Kidney: Natural Adversarial Medical Images Reveal Critical Weaknesses of Vision-Language Models
Dec 3, 2025Leon Mayer, Piotr Kalinowski, Caroline Ebersbach +6VLM Evaluation VLM Robustness
German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Germany. · Medical Faculty, Heidelberg University, Germany. · Faculty of Mathematics and Computer Science, Heidelberg University, Germany. +9
Understanding the Effects of Distractors on Reasoning Vision-Language Models
Nov 26, 2025Jiyun Bae, Hyunjong Ok, Sangwoo Mo +1VLM Evaluation Visual Question Answering
Pohang University of Science and Technology (POSTECH)
Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models
Nov 24, 2025Xiangjie Sui, Songyang Li, Hanwei Zhu +3VLM Evaluation VLM Robustness
City University of Macau, Macau SAR, China · Jiangxi University of Finance and Economics, Nanchang, China · Nanyang Technological University, Singapore
BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models
Nov 24, 2025Juncheng Li, Yige Li, Hanxun Huang +5VLM Evaluation VLM Robustness
Fudan University · Singapore Management University · The University of Melbourne
Contrastive vision-language learning with paraphrasing and negation
Nov 20, 2025Kwun Ho Ngan, Saman Sadeghi Afgeh, Joe Townsend +1VLM Robustness VLM Reasoning
Fujitsu Research of Europe · United Kingdom · City St George’s, University of London
Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
Nov 18, 2025Zehao Liu, Weijieying Ren, Jipeng Zhang +4VLM Robustness Medical Diagnosis
Pennsylvania State University, University Park, PA 16802, USA · Stanford University, Stanford, CA 94305, USA · Hong Kong University of Science and Technology, Hong Kong, China
FoCLIP: A Feature-Space Misalignment Framework for CLIP-Based Image Manipulation and Detection
Nov 10, 2025Yulin Chen, Zeyuan Wang, Tianyuan Yu +2VLM Robustness Adversarial Attacks on VLMs
Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors
Oct 1, 2025Atif Belal, Heitor R. Medeiros, Marco Pedersoli +1Open-Vocabulary Object Detection VLM Robustness
LIVIA, ILLS, Dept. of Systems Engineering, ETS Montréal, Canada
TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models
Sep 29, 2025Zhifang Zhang, Qiqi Tao, Jiaqi Lv +3VLM Robustness Adversarial Attacks on VLMs
School of Computer Science and Engineering, Southeast University, China · School of Electrical Engineering and Computer Science, University of Queensland, Australia · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore +2
VISOR: Visual Input-based Steering for Output Redirection in Vision-Language Models
Aug 11, 2025Mansi Phute, Ravikumar BalakrishnanVLM Robustness Adversarial Attacks on VLMs
Georgia Institute of Technology · HiddenLayer, Inc
Can Argus Judge Them All? Comparing VLMs Across Domains
Jun 23, 2025Harsh Joshi, Gautam Siddharth Kashyap, Rafiq Ali +5VLM Evaluation VLM Robustness
Bharati Vidyapeeth, New Delhi, India · Macquarie University, Sydney, Australia · DSEU-Okhla, New Delhi, India +3
SpurLens: Automatic Detection of Spurious Cues in Multimodal LLMs
Mar 11, 2025Parsa Hosseini, Sumit Nawathe, Mazda Moayeri +2VLM Evaluation VLM Robustness
Department of Computer Science University of Maryland
Image-based Geo-localization for Robotics: Are Black-box Vision-Language Models there yet?
Jan 28, 2025Sania Waheed, Bruno Ferrarini, Michael Milford +2VLM Robustness Cross-View Geo-Localization
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ · MyWay srl, Via Osti, 6 - 29010 Vernasca (PC) · School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia +1
Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models
Dec 11, 2024Hee-Seon Kim, Minbeom Kim, Seokil Ham +1VLM Robustness Adversarial Attacks on VLMs
School of Electrical Engineering, KAIST, Daejeon 34141, Republic of Korea
VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild
Oct 1, 2024Junlin Fang, Wenyu Chen, Reshmi Ghosh +7VLM Robustness Vision-Language Models
College of Computing and Data Science Nanyang Technological University · School of Physical and Mathematical Sciences Nanyang Technological University · Microsoft Corp. +1

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Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models

Sethuraman T, Savya Khosla, Aditi Tiwari, Vidya Ganesh+11

University of Illinois Urbana-Champaign, USA · Adobe Research, USA · Qualtrics, USA+6

VLM Robustness Video-Language Model Evaluation Video-Language Models Multimodal Model Evaluation Temporal Video Understanding

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Video-Language Models (VidLMs) achieve strong benchmark scores, yet these scores often hide whether models use the video at all. We show that VidLM failures follow two pathways: some visual signals are never reliably encoded, while others are encoded but overridden by model priors. We introduce REVEAL, a diagnostic stress-test benchmark for quantifying when and why VidLMs under-use visual evidence. REVEAL contains five controlled probes: camera-motion sensitivity, cross-frame integration, video sycophancy, language-only shortcuts, and temporal expectation bias. Together, they test whether models encode basic video signals, combine evidence across frames, and preserve visual evidence against user assertions, language cues, and learned event expectations. Across 12 VidLMs we find systematic failures along both pathways, with most models falling below chance on the binary and six-way probes that humans solve at 78--100% accuracy. Under assertive prompts, a model's output distribution becomes nearly invariant to whether it is shown a real video or random noise, making visual evidence effectively causally inert. We further carry out mechanistic probes to identify where these failures arise in the model pipeline and why visual evidence is lost. REVEAL provides a scalable, human-verified framework for moving beyond aggregate scores toward structured, reproducible evaluation of multimodal reliability.

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