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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) |