MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models
arXiv:2606.11792v1 Announce Type: cross Abstract: Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated responses are not faithfully supported by the input video. In this paper, we propose MultiToP, a multimodal-context-aware visual token patching framework that mitigates hallucinations by refining unreliable visual tokens before language generation. MultiToP introduces a lightweight Visual Token Patcher to predict token-level replacement distributions and selectively substitute unreliable visual tokens
As Visual Large Multimodal Models (VLMMs) become more sophisticated, addressing fundamental limitations like hallucinations is critical for their widespread adoption and reliability.
This development enhances the trustworthiness and utility of advanced AI models, which is crucial for applications that demand high factual accuracy, like autonomous systems and critical decision support.
The ability to mitigate hallucinations directly improves the reliability of outputs from VLMMs, reducing the need for extensive human oversight and increasing their functional autonomy.
- · AI developers
- · Autonomous vehicle companies
- · Content generation platforms
- · Critical infrastructure monitoring
- · Companies reliant on human data verification
- · Low-quality content farms
VLMMs become significantly more reliable in generating factual and contextually accurate information from video inputs.
Increased trust in AI-generated visual content could accelerate adoption in regulated industries and sensitive applications.
More robust VLMMs could underpin the development of truly autonomous AI agents capable of complex decision-making based on real-world visual data.
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Read at arXiv cs.CL