# Video Large Language Model

Compressing visual tokens for video large language models costs feature comparisons in the compression decisions themselves. This method aligns reference-frame dependencies, motion vectors and residuals already present in compressed video to the visual token grid, uses them to propose small candidate sets, then verifies with local visual similarity. Across three video LLMs and four video-understanding benchmarks, performance matches the training-free baseline FlashVID and sometimes exceeds it, while comparisons for token protection and temporal matching drop substantially.

Compressing visual tokens for video large language models costs feature comparisons in the compression decisions themselves. This method aligns reference-frame dependencies, motion vectors and residuals already present in compressed video to the visual token grid, uses them to propose small candidate sets, then verifies with local visual similarity. Across three video LLMs and four video-understanding benchmarks, performance matches the training-free baseline FlashVID and sometimes exceeds it, while comparisons for token protection and temporal matching drop substantially.