{"slug": "omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination", "title": "OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination", "summary": "Researchers introduced OmniConfess, a training-free inference-time method that elicits token-level confessions from omni-modal large language models (OmniLLMs) to reveal which evidence sustains a generated commitment and to mitigate hallucination. The work targets OmniLLMs that unify text, images, audio, and video but hallucinate when generation relies on the wrong evidence, a gap existing inference-time methods rarely address.", "body_md": "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 tra", "url": "https://wpnews.pro/news/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination", "canonical_source": "https://aiflash.com/news/131764/", "published_at": "2026-10-06 05:00:13+00:00", "updated_at": "2026-10-06 05:17:44.423771+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety"], "entities": ["OmniConfess", "OmniLLMs"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination", "markdown": "https://wpnews.pro/news/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.md", "text": "https://wpnews.pro/news/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.txt", "jsonld": "https://wpnews.pro/news/omniconfess-eliciting-token-confessions-to-mitigate-omni-modal-hallucination.jsonld"}}