| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| tags: |
| - omni |
| - vlm |
| - llm |
| - reasoning |
| - benchmark |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| <div align="center"> |
| <br> |
| <h1>FysicsReason: Benchmarking Verifiable World-State Reasoning Across Omni-Modalities</h1> |
|
|
| <a href="https://github.com/Fysics-AI/FysicsReason">🏠 Project Page</a> |
| |
| <a href="PAPER_LINK">📖 Paper</a> |
| |
| <a href="https://huggingface.co/datasets/Fysics-AI/FysicsReason">🤗 Dataset</a> |
| |
|
|
| </div> |
|
|
| ## Dataset Overview |
| FysicsReason is a five-task omni-modal benchmark designed to evaluate physical reasoning across images, audio, video, and text. The benchmark contains five complementary tasks that cover visual understanding, audio-visual grounding, temporal reasoning, physical property comparison, and quantitative physical inference. Each task is provided as a separate Parquet file under `task{1..5}/`, together with its associated media files. |
|
|
| ## Task Composition |
|
|
| | Task | Samples | Input | Prediction Target | |
| | ----- | ------: | -------------------------------------------------- | ---------------------------------------- | |
| | task1 | 562 | Image and text question | Target quantity and final answer | |
| | task2 | 779 | Image, scene audio, and text question | Object bounding box and answer option | |
| | task3 | 250 | Video and multiple-choice text question | Evidence interval and answer option | |
| | task4 | 350 | Two object images, object audio, and text question | Property, materials, and selected object | |
| | task5 | 387 | Video and text question | Physical property and numerical answer | |
|
|
| ## Data Organization |
|
|
| Media paths stored in the Parquet files are relative to the dataset root. For example: |
| `data/task2/media/...` |
| When loading the dataset locally, these paths should therefore be resolved relative to the directory containing the `data/` folder. Each task directory contains its corresponding annotation file and linked media resources, allowing the benchmark to be used directly for task-specific or unified omni-modal evaluation. |
|
|
| ## Sample Identification |
| The `index` field serves as the stable, zero-based row identifier within each task. For evaluation and result submission, a sample can be uniquely identified using the combination of: |
| - `task_source` |
| - `index` |
| This convention provides a consistent mapping between dataset samples and inference outputs. |
|
|
| ## Evaluation |
| Inference results produced on FysicsReason can be evaluated using the official workflow provided in the FysicsReason repository. |
| [FysicsReason Evaluation Repository](https://github.com/Fysics-AI/FysicsReason) |
