The Invisible Hand of Physics: When Video Diffusion Models Know More Than They Show
Researchers probed video diffusion models by reversing their generation process to access internal states. They found that physical plausibility is linearly decodable from these transformer states, achieving 81.27% accuracy and outperforming dedicated representation learning methods. This suggests physically meaningful representations emerge as a byproduct of the generative denoising process, not explicit training objectives.