Hallucination Detection in VLMs

VLM: Vision-Language Model

Hallucination detection studies when vision-language models produce claims that are not supported by their visual inputs, such as inventing objects, attributes, or events. Approaches include probing hidden states and attention, measuring uncertainty or consistency under image perturbations, and verifying claims against specific regions or with counterfactual evidence. Benchmarks test object hallucinations, caption and answer errors, fine-grained spans, and specialized settings such as medical images and video. A central challenge is that confidence and accuracy do not guarantee visual grounding, while detectors must generalize across models and tasks without excessive abstention or added inference cost.

Latest papers

72 papers5 papers in the last four weeks, down 58% on the four weeks before. 0.0% of all new papers.

Robustness of Internal Signals for Hallucination Detection in Vision-Language Models
Oct 19, 2026Allison Chen, William Yang, Salma Abdel Magid +3Hallucination Detection in VLMs VLM Robustness
Department of Computer Science, Princeton University, Princeton, NJ 08544
PLANE: Plug-and-Play Fine-Grained Visual Experts for Hallucination Mitigation in Large Vision-Language Models
Oct 18, 2026Jianghao Yin, Lang Yu, Qianxi Yu +4VLM Hallucination Mitigation Object Hallucination in VLMs
School of Computer Science and Technology, East China Normal University · Shanghai Qiji Zhifeng Co., Ltd.
Not Every Mention Is a Claim: Rethinking Object Hallucination Detection in Vision-Language Models
Oct 16, 2026Hsin-Ling Hsu, Neeraja Kirtane, Kuan-Hao HuangVLM Evaluation Hallucination Detection in VLMs
National Chengchi University · Texas A&M University
alkhder at ImageEval 2026 Shared Tasks: Constrained Selection and Rotation-Averaged Logit Scoring for Multimodal Hallucination Detection
Oct 15, 2026Maher Pasa, Maria Hanini, Amro Najjar +1Position Bias in VLMs Hallucination Detection in VLMs
Altınbaş University, Türkiye · Imperial College London · Luxembourg Institute of Science and Technology +1
From Routing Signals to Selective Review: Visual regrounding in MoE VLMs
Sep 29, 2026Hongzhu Guo, Mohsen Fayyaz, Nanyun PengHallucination Detection in VLMs Vision-Language Models
University of California, Los Angeles · Peking University
Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems
Sep 22, 2026Malsha Ashani Mahawatta Dona, Konstantinos Rokanas, Alexander Säfström +2Cultural Safety Evaluation of VLMs VLM Evaluation
University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden
Do LiDAR Language Models Really Understand Spatio-temporal Relationships?
Sep 21, 2026Runyi Yang, Murat Akkoyun, Di Wen +8VLM Evaluation Hallucination Detection in VLMs
INSAIT, Sofia University “St. Kliment Ohridski”, Bulgaria · Karlsruhe Institute of Technology, Germany · Hunan University, China
SKstars at SHROOM: Visions Agreement-Guided Ensembling of Zero-Shot and LoRA-Adapted Vision--Language Models
Sep 21, 2026Ali Athar, Imran Ahsan, Joon-Yong JungHallucination Detection in VLMs Vision-Language Models
Department of Radiology, Seoul St. Mary’s Hospital, The Catholic University of Korea · Department of Smart City, Chung-Ang University
Vroom-Vroom at SHROOM-Visions: A Multi-Judge Committee for Detecting Hallucinated Spans in Vision-Language Outputs
Sep 15, 2026Toqeer Ehsan, Nico Penttilä, Richard Schmidt +2VLM Evaluation Hallucination Detection in VLMs
Reliable Intelligence Team, Physical AI, VTT Technical Research Centre of Finland
Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models
Sep 15, 2026Ziheng Ren, Qian Gao, Jun Fan +3Hallucination Detection in VLMs Object Hallucination in VLMs
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China · Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China · Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan 250014, China +2
What Do Hallucinations Reveal About Multimodal Reasoning? Diagnosing Visual Grounding Failures via Contrastive Decoding Probes
Sep 15, 2026Zhipeng Zhao, Wenxu Wang, Peishun Liu +1Hallucination Detection in VLMs Multimodal Grounding
1Ocean University of China
OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
Sep 10, 2026Jianjiang Yang, Peihang Li, Shanqing Xu +3Hallucination Detection in VLMs Hallucination Detection
The University of Manchester · The University of Hong Kong · Huazhong University of Science and Technology +2
HALDETECT at ImageEval 2026 Shared Tasks: Answer-First Contrastive Grounding with QLoRA
Sep 10, 2026Syed Mohaiminul Hoque, Md Sakhawat HossainVLM Evaluation Hallucination Detection in VLMs
Independent Author
Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection
Sep 9, 2026Eli SchwartzHallucination Detection in VLMs Vision-Language Models
IBM Research
Can We Trust Video Hallucination Detectors? VidHalLoc for Evaluating the Evaluators
Sep 9, 2026Xinyu Chen, Adnan Mahmood, Mark DrasHallucination Detection in VLMs Hallucination Detection
Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification
Sep 2, 2026Xuanbing Wen, Boxu Chen, Le Yang +4Hallucination Detection in VLMs Hallucination Detection
Xi’an Jiaotong University, Xi’an 710049, China · Zhongguancun Laboratory, Beijing 100194, China
VisER: Visual Evidence and Reliance for Object Hallucination Detection in LVLMs
Aug 31, 2026Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +1Hallucination Detection in VLMs VLM Hallucination
School of Computing and Information Systems The University of Melbourne Melbourne, Australia
SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models
Aug 30, 2026Amanuel Gizachew Abebe, Yasmin MoslemHallucination Detection in VLMs Hallucination Detection
Shaggar Institute of Technology Shaggar City, Ethiopia · Trinity College Dublin Dublin, Ireland
HalluPrism: When Multimodal Uncertainty Should Diagnose, Not Decide
Aug 29, 2026Aman Prakash, Sourish Dasgupta, Tanmoy ChakrabortyHallucination Detection in VLMs Multimodal Model Evaluation
LCS2 Lab, Indian Institute of Technology Delhi, New Delhi, India · KDM Lab, Dhirubhai Ambani University, Gujarat, India
PROVE: Proof-guided Regime-aware Operator Verification for Hallucination Detection in Medical Visual Question Answering
Aug 28, 2026Keyang Zhou, Siyi Li, Zhongnan Shi +3Hallucination Detection in VLMs Medical VQA
Fudan University Shanghai, Shanghai, China · Fudan University Shanghai Cancer Center Shanghai, Shanghai, China
Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models
Aug 26, 2026Raúl Vázquez, Aman Sinha, Chuyuan Li +12Hallucination Detection in VLMs Hallucination Detection
University of Helsinki · Politecnico di Torino · Universiteit Utrecht +5
Catching Hallucinated Citations in Video-LLM Question Answering: A Self-Verification Pipeline and Verifier Ablation Study
Aug 16, 2026Yogesh KumarHallucination Detection in VLMs Video QA
LookBack: Where and How to Score LVLM Responses via Visual Reference Usage
Aug 12, 2026Beomsik Cho, Jinhyeong Kim, Dongseok Lee +1VLM Evaluation Hallucination Detection in VLMs
Yonsei University
When Visual Signals Mislead: A Mechanistic Study of Attribute Hallucination in Vision-Language Models
Aug 11, 2026Yufei Zhang, Chenlu Zhan, Hongwei WangHallucination Detection in VLMs VLM Adaptation
Zhejiang University
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
Aug 11, 2026Dvir Samuel, Guy Bar-Shalom, Fabrizio Frasca +4Hallucination Detection in VLMs VLM Hallucination
NVIDIA Research, Tel Aviv, Israel · Technion, Haifa, Israel · Bar-Ilan University, Ramat Gan, Israel +1
Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination
Aug 7, 2026Zichuan Wang, Songlin Yang, Bo Peng +4Hallucination Detection in VLMs Vision-Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences · 3Hong Kong University of Science and Technology · 2New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science
TruthLens: Object Hallucination Detection via Self-Evaluating Truthfulness Scores in LVLMs
Aug 6, 2026Yanqi Wu, Runhe Lai, Xinhua Lu +5VLM Evaluation Hallucination Detection in VLMs
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Peng Cheng Laboratory, Shenzhen, China · Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China +2
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
Aug 4, 2026Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia +4Hallucination Detection in VLMs Vision-Language Models
Sharif University of Technology · Michigan State University · Shahid Beheshti University +2
Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking
Aug 2, 2026Timothee Mickus, Claudio Savelli, Eduardo Calò +10VLM Evaluation Hallucination Detection in VLMs
University of Helsinki
Role-Break in Attention Heads: Understanding and Detecting Hallucinations in VLMs
Jul 31, 2026Mingyu Wang, Weilin Jin, Wenbo Li +5Hallucination Detection in VLMs Attention Head Analysis
Peking University, Beijing, China · Joy Future Academy, Beijing, China

Oct 19, 2026J/K move · Enter open · S save

Robustness of Internal Signals for Hallucination Detection in Vision-Language Models

Allison Chen, William Yang, Salma Abdel Magid, Jonathan Williams+2

Department of Computer Science, Princeton University, Princeton, NJ 08544

Hallucination Detection in VLMs VLM Robustness Object Hallucination in VLMs Visual Evidence Sensitivity in VLMs

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When given an image and a text prompt, vision-language models (VLMs) can produce seemingly sophisticated text responses. However, they are also prone to hallucinate objects in their output, which can have detrimental consequences in safety-critical applications. To detect these hallucinations, certain methods interestingly leverage internal signals from the VLM as it generates the text response; potentially, these methods use the same signals that led to a hallucination to detect it. We conduct an in-depth analysis to understand the robustness of these detection methods. First, we show that a naive baseline of a generated word's token position in the VLM text response achieves competitive performance. Further, we reveal how some detection methods rely on token position and use this heuristic as a shortcut. Second, we explore detection methods' sensitivity to language and vision modalities. Our experiments suggest that strong detection performance is often driven by detecting language-based hallucinations, and when methods rely on token position, they tend to be less visually grounded and exhibit reduced robustness when hallucinations occur earlier in a text response. Lastly, we demonstrate that different detection methods can carry complementary signals; when ensembling methods together, adding weaker methods to an ensemble of higher performing ones still offers performance improvements. Altogether, while there may be useful internal VLM signals to detect hallucinations, our investigation reveals important limitations of the current methods that try to leverage them.