Abstract
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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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Rong, H., Luo, H., Feng, H., Ou, Z., Xue, K., Zhang, G., & Zhu, Y. (2026). OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination. https://omanscience.com/en/articles/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination
MLA 9
Rong, Huiqiang, et al. "OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination." https://omanscience.com/en/articles/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.
Chicago (author–date)
Rong, Huiqiang, Haoran Luo, Hui Feng, Zhonghong Ou, Kaiwen Xue, Guoxin Zhang, and Yifan Zhu. 2026. "OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination." https://omanscience.com/en/articles/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.
Harvard
Rong, H., Luo, H., Feng, H., Ou, Z., Xue, K., Zhang, G. and Zhu, Y. (2026) 'OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination', Available at: https://omanscience.com/en/articles/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.
Vancouver
Rong H, Luo H, Feng H, Ou Z, Xue K, Zhang G, et al. OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination. https://omanscience.com/en/articles/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination
IEEE
H. Rong, H. Luo, H. Feng, Z. Ou, K. Xue, G. Zhang, and Y. Zhu, "OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination," https://omanscience.com/en/articles/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.