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| title: Code-as-World VL Demo | |
| emoji: π | |
| colorFrom: indigo | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 6.26.0 | |
| app_file: app.py | |
| short_description: Video-based quantitative physical reasoning VLM | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| license: apache-2.0 | |
| # Code-as-World VL Demo | |
| [Code as Worlds](https://arxiv.org/abs/2608.27549) introduces executable world | |
| representations for physical reasoning. This demo showcases the Code-as-World-VL | |
| vision-language model, which performs **quantitative physical reasoning** from | |
| videos β estimating object sizes, velocities, and distances from visual evidence. | |
| ## Usage | |
| 1. Upload a short video clip. | |
| 2. Enter a physics measurement question (e.g. "What is the length of the object in cm?"). | |
| 3. Optionally provide prior information (e.g. "ruler calibre = 1 cm") that helps ground the measurement. | |
| 4. Click **Analyze** and the model streams its numerical answer. | |
| ## Models | |
| | Label | Repo | Params | | |
| |---|---|---| | |
| | 9B (recommended) | `MirroS-Lab/Code-as-World-VL-9B` | 9B | | |
| | 4B (faster) | `MirroS-Lab/Code-as-World-VL-4B` | 4B | | |
| ## Example assets | |
| Example videos are from the [QuantiPhy validation set](https://huggingface.co/datasets/PaulineLi/QuantiPhy-validation) | |
| (CC-BY-4.0). Questions and priors are drawn from the same dataset. | |
| ## Acknowledgements | |
| - Model: [MirroS-Lab](https://huggingface.co/MirroS-Lab) (Apache 2.0) | |
| - Example videos: [QuantiPhy](https://quantiphy.stanford.edu/) validation set (CC-BY-4.0) | |
| - Base architecture: [Qwen3.5](https://huggingface.co/Qwen/Qwen3.5-9B) |