MultiToP mitigates hallucinations in video multimodal models by training a Visual Token Patcher with information-guided rank calibration to selectively replace unreliable tokens, yielding 50.60% F1 gain on Vript-HAL and 18.58% accuracy gain on ActivityNet-QA.
arXiv preprint arXiv:2512.24271 , year=
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MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models
MultiToP mitigates hallucinations in video multimodal models by training a Visual Token Patcher with information-guided rank calibration to selectively replace unreliable tokens, yielding 50.60% F1 gain on Vript-HAL and 18.58% accuracy gain on ActivityNet-QA.