NVIDIA Researchers Introduce Physis-Lang: Self-Evolving Physical Language That Lifts Cosmos 3 Past Veo 3.1 on Physics Benchmarks
Video world models can render convincing clips that still break physics. Butter spreads like paint. Balls pass through walls. A team from NVIDIA, MIT and the University of Oxford argues the fix can come from language itself, not from extra visual, latent or numerical signals. Their framework, Physis-Lang, treats physical language as a shared, optimizable representation. The same text drives data curation, model training and inference. On the public Physics-IQ Verified leaderboard snapshot da
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Video generation models can create realistic-looking clips that still violate the laws of physics, such as butter spreading like paint or balls passing through walls. Physis-Lang addresses this by using language-based physics reasoning rather than additional visual or numerical signals, where text descriptions explain not just what happens in a scene but why, including entities, causes, interactions, and governing principles. The framework uses a self-evolving loop where a critic model scores captions on their accuracy and completeness against human-verified physical assertions, with the instruction prompt being refined across iterations while the base captioner remains frozen. When applied to video generation, Physis-Lang achieved top performance on physics benchmarks and outperformed a competing system on most measured tests.
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