AI RESEARCH
Refining Multidimensional Video Reward Models via Disentangled Influence Functions
arXiv CS.LG
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ArXi:2605.28203v1 Announce Type: new As Text-to-Video (T2V) generation models continue to evolve, the complexity of video evaluation necessitates a fine-grained assessment across various axes. To address this, recent works have focused on developing Multidimensional Video Reward Models (MVRMs), which decompose the evaluation process to better align with the multifaceted nature of human visual perception. However