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Video-LLMs can perfectly label every object in a scene yet collapse to near-random chance when simply asked which of two identical actions moved faster.
Forget brute-force scaling: intelligently selecting just 1% of video frames can actually *improve* video QA accuracy and cut compute by 93%.
Visual language models can now explicitly reason about object trajectories in videos, thanks to a simple yet effective method that augments training data and uses discrete motion tags.
