shi-labs/physical-ai-bench-understanding
收藏Hugging Face2025-12-10 更新2026-01-03 收录
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https://hf-mirror.com/datasets/shi-labs/physical-ai-bench-understanding
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资源简介：
---
task_categories:
- video-text-to-text
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: index2ans
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
- name: video_path
dtype: string
- name: category
dtype: string
- name: subcategory
dtype: string
splits:
- name: test
num_bytes: 400189
num_examples: 1214
download_size: 105256
dataset_size: 400189
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Physical AI Bench - Understanding
PAI-Bench (Physical AI Bench) is a comprehensive benchmark designed to evaluate physical AI generation and understanding capabilities across various real-world scenarios. This particular dataset, **PAI-Bench-U**, focuses specifically on **Video Understanding** tasks, comprising 2,808 real-world cases with task-aligned metrics.
- **Paper:** [PAI-Bench: A Comprehensive Benchmark For Physical AI](https://huggingface.co/papers/2512.01989)
- **Code:** [GitHub Repository](https://github.com/SHI-Labs/physical-ai-bench)
## Citation
If you use Physical AI Bench in your research, please cite:
```bibtex
@misc{zhou2025paibenchcomprehensivebenchmarkphysical,
title={PAI-Bench: A Comprehensive Benchmark For Physical AI},
author={Fengzhe Zhou and Jiannan Huang and Jialuo Li and Deva Ramanan and Humphrey Shi},\
year={2025},\
eprint={2512.01989},\
archivePrefix={arXiv},\
primaryClass={cs.CV},\
url={https://arxiv.org/abs/2512.01989}, \
}
```
提供机构：
shi-labs
