NTH

Does Physics Live in the Activations? Localizing Physical Quantities in Video Diffusion Models

AuthorsJonas Kneifl, Jakub Skalski, Bartłomiej Twardowski, Kamil Deja

AffiliationsIDEAS Research Institute, Warsaw, Poland · Computer Vision Center, Universitat Autònoma de Barcelona, Spain · Oct 2026 · Warsaw University of Technology, Poland

October 16, 2026 2 min read
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The one-line take

This study looks inside video diffusion models to find where they represent physics—and shows those hidden representations can even be used to steer what the model generates.

Key results

0.876
Wan 2.1 transfer median R²

Median recalibrated probe score across scene variations.

0.905
CogVideoX-1.5 transfer median R²

Median recalibrated probe score across scene variations.

0.295
Open-Sora 2.0 transfer median R²

Median recalibrated probe score across scene variations.

0.45
Wan 2.1 direction-flip ratio

Share of tested left-rolling clips steered to roll right.

0.35
CogVideoX-1.5 direction-flip ratio

Share of tested left-rolling clips steered to roll right.

0.31
Open-Sora 2.0 direction-flip ratio

Share of tested left-rolling clips steered to roll right.

What the paper found

This study asks whether video diffusion models represent physical quantities internally or merely reproduce familiar motion. Using simulator-labeled rolling, collision, and projectile videos, the researchers train ridge-regression probes on frozen activations from Wan 2.1, CogVideoX-1.5, and Open-Sora 2.0. Speed, direction, momentum, and gravity are linearly readable, often before the noisy input contains enough information; gravity reaches an activation R² of 0.41–0.81 from a single latent frame, compared with 0.07–0.12 from the clean latent. The signal is concentrated around the relevant object: an object-centered region covering roughly 1–2% of tokens beats a global readout, even though the model aggregates information across frames. Probes also transfer across scene changes, though Open-Sora generalizes less reliably: median recalibrated R² is 0.876 for Wan 2.1, 0.905 for CogVideoX-1.5, and 0.295 for Open-Sora 2.0. Finally, injecting full-resolution probe directions during generation can change the ball’s rolling direction, flipping 0.45 of tested Wan 2.1 clips, versus 0.35 for CogVideoX-1.5 and 0.31 for Open-Sora 2.0. The results show that physical state is both constructed within the denoising network and causally usable, but decodability alone does not establish that a model follows physical laws.

Original abstract

Video generation models produce strikingly realistic sequences and are increasingly proposed as world models, yet recent benchmarks reveal pronounced deficits in their physical reasoning. This raises the question of whether these models internalize physical principles or merely reproduce familiar motion patterns. We address this by probing internal representations of video Diffusion Transformers (DiTs) for simulator-derived ground-truth physical quantities spanning kinematic motion and rigid-body dynamics under gravity and contact. We find that these quantities are linearly decodable with high accuracy early in the denoising process, substantially outperforming a baseline decoded directly from the model's own noised latents, indicating that the relevant physical information is actively constructed during denoising rather than already present in the input. Additionally, we show that activations at on-object tokens carry the relevant physical information and that quantities defined over multiple frames are readable from single latent frames. Hence, information is sharply localized within the token sequence and is computed globally but stored locally. The probes further show partial extrapolation, transferring to scene variations and object configurations outside their training regime, so what they read is not simply a correlate of the scenes they were fit on. When fitted directly in the full-resolution activation space, the probing directions can serve as steering vectors to change the model's output.

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