Instead of describing or reinserting video regions during reasoning, VideoLVR reconstructs visual tokens from relevant moments—letting models "see" evidence again while staying efficient.
VideoLVR enables video understanding models to reason over video content by reconstructing visual evidence as latent tokens during reasoning steps, rather than relying on text descriptions or expensive visual crops. This approach keeps visual information accessible without bloating the context, improving performance on fine-grained video reasoning tasks.