Hallucination Mitigation

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 18

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OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination
Oct 2, 2026Huiqiang Rong, Haoran Luo, Hui Feng +4Multimodal Large Language Models Multimodal Hallucination
Beijing University of Posts and Telecommunications · Nanyang Technological University, Singapore
MEND: Label-Free Detection, Localisation, and Correction of Latent Hallucination in World Models
Sep 30, 2026Ali J Alrasheed, Aryan Yazdan Parast, Basim Azam +2Hallucination Detection Latent World Models
University of Melbourne Melbourne, Australia · Monash University Melbourne, Australia
MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models
Sep 27, 2026Zhi Wen Soi, Giulio Segalini, Jian-Jia Chen +1Audio-Language Model Evaluation Audio-Language Model Hallucination Detection
University of Neuchâtel · TU Dortmund University · RWTH Aachen University +1
REVE: Efficient Hallucination Correction for Large Audio-Language Models via Reused Encoder States
Sep 22, 2026Hongjin Song, Jiasheng Kuang, Xinyu Yang +4Audio-Language Model Hallucination Detection Sound Event Detection
Beijing Institute of Technology, Zhuhai, Guangdong, China
RVSD: Retrieval Vision Sparse Decoding for Mitigating Visual Hallucinations in Large Vision-Language Models
Sep 2, 2026Canjie Liu, Jiawen Kang, Jinbo Wen +1Hallucination Mitigation Large Vision-Language Models
School of Automation, Guangdong University of Technology, Guangzhou, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China · The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
Experience-Calibrated Contrastive Decoding for Mitigating Hallucinations in LM-Based Text-to-Speech
Aug 1, 2026Chenlin Liu, Minghui Fang, Zhonghao Bi +3TTS Synthesis Contrastive Decoding
Harbin Institute of Technology, China · Zhejiang University, China
HALLELUAI: A Hallucination-Aware AI System for Ultra-Realistic Image-to-Video Generation at Scale
Jul 25, 2026Aniket Sakpal, Yang Jiang, Rouzbeh Davoudi +2Image-to-Video Generation Video Content Moderation
Expedia Group
Statistically Meaningful Geometry (SMG) Beyond the Euclidean Paradigm, with Application to Generative AI
Jul 3, 2026Bing Cheng, Yi-Shuai Niu, Howell Tong +1Neural Network Generalization Information Geometry
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · AMSS Center for Forecasting Science, Chinese Academy of Sciences, Beijing, China · Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing, China +4
Hallucination in Medical Imaging AI: A Cross-Modality Analytical Framework for Taxonomy, Detection, and Mitigation under Regulatory Constraints
Jun 11, 2026Omar Alshahrani, Muzammil BehzadMedical Imaging Foundation Models Hallucination Detection
King Fahd University of Petroleum & Minerals, Saudi Arabia · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Saudi Arabia
Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates
Jun 1, 2026Pengfei Jin, Yiqi Tian, Kailong Fan +2Diffusion-Based Inverse Problems Hallucination Mitigation
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 · Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261
TIGER: Traceable Inference with Graph-Based Evidence Routing for Mitigating Hallucinations in Multimodal Generation
May 29, 2026Kaixiang Zhao, Tianrun Yu, Shawn Huang +3Multimodal Grounding Multimodal Generation
Brigham Young University · Florida State University
TIAR: Trajectory-Informed Advantage Reweighting for LLM Abstention Learning
May 25, 2026Muyu Pan, Shu Zhao, Nan Zhang +4RL for Language Models Hallucination Mitigation
Department of Computer Science, The Pennsylvania State University State College, PA, USA
Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination
May 19, 2026Jinrui Jiang, Zhangtai Wu, Zhen Wu +1Hallucination in Language Models Multimodal Large Language Models
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models
May 6, 2026Bartlomiej Sobieski, Matthew Tivnan, Dawid Płudowski +4Intrinsic Dimensionality Hallucination Mitigation
Centre for Credible AI, Warsaw University of Technology · University of Warsaw · Massachusetts General Hospital +1
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
HalluClear: Diagnosing, Evaluating and Mitigating Hallucinations in GUI Agents
Apr 19, 2026Chao Jin, Wenkui Yang, Hao Sun +6GUI Agents Hallucination in Language Models
MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences
FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models
Apr 16, 2026Zixuan Weng, Jinghuai Zhang, Kunlin Cai +3Language Model Steering LLM Safety
University of California, Los Angeles
Silence is Golden: Mitigating Hallucinations in Large Audio-Language Models via Layer-Weighted Vector Steering
Oct 14, 2025Tsung-En Lin, Kuan-Yi Lee, Hung-Yi LeeAudio QA Hallucination in Language Models
National Taiwan University, Taipei, Taiwan · ASUS Open Cloud Infrastructure Software Center, Taipei, Taiwan

Oct 2, 2026, cs.CLJ/K move · Enter open · S save

OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination

Huiqiang Rong, Haoran Luo, Hui Feng, Zhonghong Ou+3

Beijing University of Posts and Telecommunications · Nanyang Technological University, Singapore

Multimodal Large Language Models Multimodal Hallucination Hallucination Mitigation Multimodal Model Evaluation

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Omni-modal large language models (OmniLLMs) unify text, images, audio, and video, yet hallucinate when generation relies on the wrong evidence. Existing inference-time methods can reduce hallucinations, but rarely reveal which evidence sustains a generated commitment. We introduce OmniConfess, a training-free method for mitigating omni-modal hallucinations. It fixes a candidate response and re-scores it at token resolution under controlled channel-wise evidence interventions, producing a structured token-by-channel confession that reveals the response's evidential dependence. OmniConfess uses this confession to preserve grounded content and correct commitments driven by irrelevant or contradictory evidence. To evaluate OmniConfess, we construct OmniHalluBench, a 3,540-example benchmark built from six datasets spanning text, image, audio, and video settings and both judgment and free-form generation. Experiments show that OmniConfess mitigates hallucinations across heterogeneous modality and task settings. Our code and benchmark are publicly available at https://github.com/RongHuiQiang/OmniConfess.

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