multimodalart's picture
multimodalart HF Staff
Upload folder using huggingface_hub
b13c0ac verified
|
Raw
History Blame Contribute Delete
1.55 kB
---
title: Code-as-World VL Demo
emoji: 🌍
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)