# NVIDIA Researchers Introduce Physis-Lang: Self-Evolving Physical Language That Lifts Cosmos 3 Past Veo 3.1 on Physics Benchmarks > Source: > Published: 2026-09-30 07:32:34+00:00 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](https://physis-intelligence.github.io/physis-lang-web/), 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](https://physics-iq-verified.anates.ai/) snapshot dated September 29, 2026, Physis-Lang on Cosmos3-Super ranks first at 48.2 ± 1.4. The Cosmos3-Nano version ranks second at 43.3 ± 1.5. ## **What Problem Does Physis-Lang Solve?** Conventional captions describe what happens, not why. ‘Butter melts as the temperature rises’ says nothing about heat transfer or gravity. Physis-Lang adds a `physics_reasoning` field to each base caption. It spells out entities, causes, interactions, governing principles, temporal evolution and effects. The pipeline also writes a scene-specific `physics_negative_prompt`. This text describes likely implausible outcomes, such as a stone floating on water. It acts as negative conditioning at inference time. ## **How Does the Self-Evolving Caption Loop Work?** The loop keeps the captioner frozen and evolves only its instruction. A GPT-5.5 captioner writes captions for a fixed 20-video development set with 273 human-verified assertions. Gemini-3.1-Pro acts as a physics-aware critic. An evolution agent reads the scores and claim-level failures, then rewrites the prompt. **The critic scores 2 dimensions:** - **Precision:** the caption is split into atomic claims, and each claim is checked against the video. - **Recall:** each human-curated physical assertion must be explicitly stated or entailed by the caption. Every revised prompt is validated on **PhysCapBench**, a new benchmark of 246 videos and 3,794 human-verified assertions. Caption F1 rose from 78.64 at iteration 1 to 87.82 at iteration 9. The path was not smooth. Iteration 2 made captions overly cautious and dropped F1 to 76.28. Iteration 9 required every visible causal step and raised frame sampling from 2 fps to 4 fps. ## **How Does Language-Guided Data Curation Work?** A GPT-5.5 diagnosis agent maps generated-video failures to physics categories like rigid-body motion, collision and fluid dynamics. That deficiency profile is matched against physics tags on a large video gallery. Retrieval targets physical content, not visual appearance. The final training set holds 183K videos: 71K filtered from WISA-80K plus 112K retrieved clips. Retrieval alone added 3.01 points on average across 3 benchmarks. On VideoPhy-2, chemical and thermal processes each gained 8.00 points. ## **How Does Physis-Lang Compare With Veo 3.1?** Fine-tuning uses LoRA on attention projections, with no architecture or objective change. **Physis-Lang on Cosmos3-Nano versus Google's Veo 3.1:** - [PhyGenBench](https://github.com/OpenGVLab/PhyGenBench) : 71.04 vs 65.63 - [Physics-IQ](https://github.com/google-deepmind/physics-IQ-benchmark) Verified: 43.41 vs 34.99 - [PhyGround](https://arxiv.org/abs/2605.10806) : 69.90 vs 69.24 - [VideoPhy-2](https://videophy2.github.io/) : 68.02 vs 68.87 on the full set, 62.36 vs 58.43 on the Hard split Gains hold across backbones: +7.05 on Wan2.1-14B, +3.24 on Cosmos3-Edge-4B, +6.22 on Cosmos3-Nano-16B and +5.02 on Cosmos3-Super-64B. General quality held steady on VBench-I2V, where Cosmos3-Nano moved from 88.32 to 88.69. Prompting alone also helps. Physics reasoning plus negative prompts lifted a frozen Cosmos3-Nano on PhyGenBench from 61.67 to 67.29. ## **Can It Run Without Commercial APIs?** The research team distilled the GPT pipeline into 2 Qwen3-VL-4B-Instruct models: PhysThinker-C for captioning and PhysThinker-U for prompt upsampling. On Wan2.1-14B, the commercial pipeline gave +7.05 at about $24.12K in API cost. Swapping in PhysThinker-C kept +6.76 at about $0.12K. A fully local setup cost $0 and still added +4.76. ## **Physis-Lang vs Closest Competitors** | Feature | [Physis-Lang](https://physis-intelligence.github.io/physis-lang-web/) | [PhiZero](https://arxiv.org/abs/2607.28624) | [PhyGDPO](https://arxiv.org/abs/2512.24551) | [Self-Refinement](https://arxiv.org/abs/2511.20280) | |---|---|---|---|---| | **Developer** | NVIDIA, MIT, Oxford | CASIA (NLPR) | [Meta](https://ai.meta.com/research/publications/phygdpo-physics-aware-groupwise-direct-preference-optimization-for-physically-consistent-text-to-video-generation/) (ECCV 2026) | Liu et al. | | **Core idea** | Self-evolving natural-language physics captions and negative prompts | Learned discrete "physical language", reason-then-render | Groupwise DPO with VLM physics rewards | Multimodal chain-of-thought prompt refinement from VLM feedback | | **Where physics enters** | Data curation, training captions and inference prompts | Qwen3-VL-4B reasoner feeding a diffusion decoder | Preference training on PhyVidGen-135K | Inference prompts only | | **Training needed** | LoRA SFT (prompt-only mode also helps) | Yes | Yes (DPO) | No, training-free | | **Physics-IQ Verified** | **43.41** | 40.91 | n/r | 27.20 | | **PhyGenBench** | **71.04** | n/r | 48.96 | 49.17 | | **VideoPhy-2 (All / Hard)** | **68.02 / 62.36** | n/r | 59.56 / 44.94 | 47.88 / 28.09 | | **PhyGround** | **69.90** | 57.85 | n/r | 58.22 | | **Code / weights public** | [Paper only](https://github.com/Physis-Intelligence/Physis-Lang) | ["Coming soon"](https://github.com/yaoyao-jpg/PhiZero) | ["Released soon"](https://github.com/caiyuanhao1998/Open-PhyGDPO) | [Paper](https://arxiv.org/abs/2511.20280) | *Scores are from the [Physis-Lang paper](https://physis-intelligence.github.io/physis-lang-web/assets/Physis-Lang.pdf), Tables 1 to 4, run under one protocol per benchmark (PhyGenBench and VideoPhy-2 use a GPT-5.5 evaluator). Physis-Lang numbers use the Cosmos3-Nano backbone. n/r = not reported in that comparison. Release status checked September 30, 2026.* ## **Key Takeaways** - Physis-Lang evolves physics captions with a critic-guided agent while the captioner stays frozen. - PhysCapBench scores captions on 3,794 human-verified cause, law and effect assertions. - Cosmos3-Nano with Physis-Lang beats Veo 3.1 on 3 of 4 benchmarks. - Physics prompts alone lift a frozen model by 5.62 points on PhyGenBench. - No code or weights are public yet; only the paper is released. Check out the [Paper](https://physis-intelligence.github.io/physis-lang-web/assets/Physis-Lang.pdf),[**Project Page**](https://physis-intelligence.github.io/physis-lang-web/) and [** GitHub Repo**](https://github.com/Physis-Intelligence/Physis-Lang). All credit goes to the researcher of this project. 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