Datasets:

shi-labs
/

physical-ai-bench-understanding

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
Code: GitHub Repository

Citation

If you use Physical AI Bench in your research, please cite:

@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}, \
}