Object Hallucination in VLMs

VLM: Vision-Language Model

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9 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models
May 12, 2026Runhe Lai, Xinhua Lu, Yanqi Wu +3Hallucination Detection in VLMs Hallucination Detection
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.
Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement
May 12, 2026Zhenxin Qin, Qiang Li, Qingzhuo Wang +3VLM Robustness VLM Hallucination
Tongji University, Shanghai, China
Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
May 9, 2026Tri Cao, Khoi Le, Thong Nguyen +7VLM Evaluation Object Hallucination in VLMs
National University of Singapore · VinUniversity · Nanyang Technological University
Object Hallucination-Free Reinforcement Unlearning for Vision-Language Models
May 8, 2026Kaidi Jia, Yujie Lin, Chengyi Yang +2VLM Unlearning Object Hallucination in VLMs
1Xiamen University
When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models
May 7, 2026Harshvardhan Saini, Samyak Jha, Yiming Tang +1Vision-Language Models Object Hallucination in VLMs
Indian Institute of Technology Dhanbad · National University of Singapore
Uncertainty-Aware Exploratory Direct Preference Optimization for Multimodal Large Language Models
May 6, 2026Huatian Zhang, Zhendong Mao, Lei Zhang +1LLM Alignment Direct Preference Optimization
University of Science and Technology of China
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
May 6, 2026Qiming Li, Zekai Ye, Xiaocheng Feng +9Hallucination in Language Models Large Vision-Language Models
1Harbin Institute of Technology · 2Peng Cheng Laboratory · 3Harbin Institute of Technology, Shenzhen
Information Coordination as a Bridge: A Neuro-Symbolic Architecture for Reliable Autonomous Driving Scene Understanding
May 6, 2026Shuo Liu, Lei Shi, Haowen Liu +3Object Hallucination in VLMs Autonomous Driving Perception
School of Computer and Artificial Intelligence, Zhengzhou University, 450001, Zhengzhou, China · School of Cyber Science and Engineering, Zhengzhou University, 450001, Zhengzhou, China · Songshan Laboratory, 450003, Zhengzhou, China
Perceptual Flow Network for Visually Grounded Reasoning
May 4, 2026Yangfu Li, Yuning Gong, Hongjian Zhan +8Object Hallucination in VLMs VLM Reasoning
1ECNU · 5Ant Group · 2SCU +3
Online Self-Calibration Against Hallucination in Vision-Language Models
May 1, 2026Minghui Chen, Chenxu Yang, Hengjie Zhu +3Vision-Language Models Hallucination in Language Models
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning
Apr 28, 2026Yashwant Pravinrao Bangde, Debaditya RoyObject Hallucination in VLMs Vision-Language Grounding
Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur, India 721302
Improving Vision-language Models with Perception-centric Process Reward Models
Apr 27, 2026Yingqian Min, Kun Zhou, Yifan Li +6Process Reward Models Object Hallucination in VLMs
Gaoling School of Artificial Intelligence, Renmin University of China. · Bytedance. · University of California, San Diego. +1
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation
Apr 27, 2026Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi +7Hallucination in Language Models Object Hallucination in VLMs
School of Astronautics, Beihang University, Beijing 102206, China · Longcat Interaction Team, Meituan, Beijing 100102, China · Tianmushan Laboratory, Beihang University, Hangzhou 311115, China
MIRAGE: A Micro-Interaction Relational Architecture for Grounded Exploration in Multi-Figure Artworks
Apr 26, 2026Jui-Cheng Chiu, Yu-Chao Wang, Shengyang Luo +4Visual Spatial Reasoning Object Hallucination in VLMs
Purdue University
R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMs
Apr 22, 2026Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2Hallucination Detection in VLMs Vision-Language Models
Max Planck Institute for Informatics, SIC · VIA Research Center · Google
Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
Apr 22, 2026Yubo Jiang, Yitong An, Xin Yang +7Vision-Language Models Hallucination in Language Models
School of Astronautics, Beihang University, Beijing 102206, China · Longcat Interaction Team, Meituan, Beijing 100102, China · Tianmushan Laboratory, Beihang University, Hangzhou 311115, China
VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing
Apr 21, 2026Yanbin Huang, Yisen Li, Guiyao Tie +6Large Vision-Language Models Object Hallucination in VLMs
1Huazhong University of Science and Technology · Institute of Artificial Intelligence (TeleAI), China Telecom
LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models
Apr 20, 2026Zhiyuan Jiang, Weihao Hong, Xinlei Guan +8VLM Evaluation Hallucination Detection in VLMs
Kean University, USA · North Dakota State University, Fargo, ND, USA · McGill University, Montreal, Quebec, Canada +1
DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
Apr 18, 2026JiYang Wang, Jiawei Chen, Mengqi Xiao +3VLM Evaluation Hallucination Detection in VLMs
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University · Zhongguancun Academy
Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
Apr 17, 2026Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian +1Spatial Reasoning Benchmarks Visual Spatial Reasoning
IIT, Hyderabad · Microsoft Research India
Semantic Misalignment in Vision-Language Models under Perceptual Degradation
Jan 13, 2026Guo Cheng, Huang LiVLM Evaluation VLM Robustness
Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping
Oct 10, 2025Dwip Dalal, Gautam Vashishtha, Utkarsh Mishra +6Object Hallucination in VLMs Multimodal Large Language Models
University of Illinois Urbana–Champaign · Skan AI · Texas A&M University +1
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
May 18, 2025Kai Tang, Jinhao You, Yichen Guo +8Vision-Language Models Hallucination in Language Models
Peking University · University of Pennsylvania · Nanyang Technological University +4
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

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Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models

Runhe Lai, Xinhua Lu, Yanqi Wu, Jinlun Ye+2

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.

Hallucination Detection in VLMs Hallucination Detection Hallucination in Language Models Large Vision-Language Models Object Hallucination in VLMs LLM Hallucination Detection+1

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Multimodal large language models (MLLMs) have achieved remarkable progress, yet the object hallucination remains a critical challenge for reliable deployment. In this paper, we present an in-depth analysis of instruction token embeddings and reveal that they implicitly encode visual information while effectively filtering erroneous information introduced by misleading visual embeddings. Building on this insight, we propose the Instruction Lens Score (InsLen), which combines a Calibrated Local Score with a Context Consistency Score that measures context consistency of the object tokens. The proposed approach serves as a plug-and-play object hallucination detector without relying on auxiliary models or additional training. Extensive experiments across multiple benchmarks and diverse MLLM architectures demonstrate that InsLen consistently outperforms existing hallucination detection methods, highlighting its effectiveness and robustness. The code is available at https://github.com/Fraserlairh/Instruction-Lens-Score.

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