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arXivMultimodal hallucination

OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination

OmniConfess mitigates omni-modal hallucinations by producing token-level confessions of evidential dependence to correct unsupported commitments across text, image, audio, and video.

Huiqiang Rong, Haoran Luo, Hui Feng, Zhonghong Ou and 3 more

Published Oct 2, 2026 · 0 citations · ▲ 6 on Hugging Face · Code ★ 1

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NeurIPS 2026U Science and Technology of ChinThe Hong Kong University of ScieThe Hong Kong University of ScieMultimodal hallucination

Correction Space Steering for Hallucination Mitigation in Large Vision-Language Models

Songbo Yang, Shuliang Liu, Sihang Jia, Xuming Hu

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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69%Highly rated
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NeurIPS 2026Florida AtlanticMultimodal hallucination

Do Robust LVLMs Hallucinate More? Uncovering Robustness-Induced Hallucination in Large Vision-Language Models

Md Zarif Hossain, Awal Ahmed Fime, Ahmed Imteaj

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026TsinghuaThe University of SydneyStanfordBaichuanMultimodal hallucination

VIGOR: Visual Gain Ordering for Hallucination Mitigation in Multimodal Discrete Diffusion Language Models

Junzhe Chen, Tian Qin, Eugenie Shi, Tianshu Zhang and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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67%Highly rated
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NeurIPS 2026Huazhong University of Science aNanyang TechnologicalShanghai Artificial IntelligenceMohamed bin Zayed University of WuhanMultimodal hallucination

Object Hallucination Mitigation in Large Vision-Language Models via Self-Vision Dual Masking and Uncertainty-Triggered Assembly

Ziji Sheng, Guiyao Tie, Weidong Wang, Jiawen Shi and 7 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026TongjiIEIT SYSTEMS (Beijing) Co., Ltd.Multimodal hallucination

HALMES: Knowing When to Intervene in LVLM Hallucination Mitigation

Ruining Hu, Jiaqi Lu, Xiao Liu, Ying Shen and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Sorbonne Université - Faculté deISIR, Sorbonne UniversitéLIP6SorbonneMultimodal hallucination

When Prompts Override Vision: Instruction-Induced Hallucinations in LVLMs

Pegah KHAYATAN, Jayneel Parekh, Arnaud Dapogny, Mustafa Shukor and 2 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026MonashDST GroupMonash University, Qualcomm AI RMultimodal hallucination

PAVE: Prefill-Conditioned Activation Editing for Hallucination Mitigation in LVLMs

Jingmin Zhu, Junae Kim, Dinh Phung, Trung Le and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SoutheastInstitute of automation, ChineseNational University of SingaporeMultimodal hallucination

Attention Heads are Complementary Visual Units: Mitigating Hallucinations in LVLMs via Adaptive Visual Cues Focusing

Zhenglin Hua, Yutong Xie, Yaxin Hou, Jiawei Tang and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PrincetonMultimodal hallucination

An In-Depth Analysis of Hallucination Detection Methods for Vision-Language Models

Allison Chen, William Yang, Salma Abdel Magid, Jonathan Williams and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PekingMultimodal hallucination

EVA: Evidence-seeking Visual Agent for Hallucination-Resistant Multimodal Reasoning

Ting Lei, Yu Chen, Yang Liu

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026TsinghuaHarbin Institute of Technology, Tsinghua University, TsinghuaMultimodal hallucination

Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models

Hao Fang, Jinyu Li, Jiawei Kong, Tianqu Zhuang and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026School of Computer Science, PekiPekingMultimodal hallucination

Cross-Layer Evolution Graph Learning for Fine-grained VLM Hallucination Detection

Shaoxuan Li, Shiliang Zhang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Pusan NatioinalPukyong NationalPusan NationalMultimodal hallucination

Focus Matters: Attention-Value Dynamics for Hallucination Mitigation in Vision-Language Models

Sohyeon Kim, Sang Yeon Yoon, Kyeongbo Kong

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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67%Highly rated
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NeurIPS 2026ShandongCity University of Hong KongMultimodal hallucination

From Squeezing to Grounding: Visual Guided DPO for Multimodal Hallucination Mitigation

Wenqi Liu, Hongxin He, Yunxiao Wang, Xuemeng Song and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026S-lab, Nanyang TechnologicalNanyang TechnologicalU Hong KongA*STARMultimodal hallucination

Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs

IMAVB reveals omnimodal LLMs encode sensory-text mismatches yet rarely reject false premises due to a representation-action gap, which probe-guided adjustments partly fix.

Trung Nguyen, Yiming Gao, Fanyi Pu, Kaichen Zhang and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face · Code ★ 1

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lenient 4/5
medium 9/10
strict 4/5
78%Highly rated
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NeurIPS 2026U Central FloridaMultimodal hallucination

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

CORAL controls false object hallucinations in vision-language models via mirror statistics and uncertainty-aware visual splitting without retraining.

Chang Liu, Yu Tian, Rui Xie

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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86%Must read
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NeurIPS 2026City University of Hong KongU SydneySoutheastHuawei Noah's Ark LabCity University of Hong KongMultimodal hallucination

Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking

Causal-Invariant Masking decomposes MLLM uncertainty via semantic divergence to capture epistemic limitations, and Expected Embedding Drift accelerates quantification by nearly 50%.

Haoyang Luo, Linwei Tao, Jie Gui, Xinghao Chen and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
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