---
dataset_info:
  config_name: pbench
  features:
    - name: image_path
      dtype: string
    - name: prompt
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: domain
      dtype: string
  splits:
    - name: benchmark
      num_bytes: 237000000
      num_examples: 1044
  download_size: 226000000
  dataset_size: 237000000
configs:
  - config_name: default
    data_files:
      - split: benchmark
        path: "cosmos_predict2_bench_full_info.json"
task_categories:
  - visual-question-answering
  - text-generation
language:
  - en
license: cc-by-nc-4.0
size_categories:
  - 1K<n<10K
tags:
  - physical-ai
  - world-models
  - benchmark
  - multimodal
---

# Physical AI Bench - Predict

## Dataset Description

The PAI-Bench is a benchmark to measure the progress of world models quantitatively.
The predict task contains a list of 1044 samples of text prompts, conditioning images, and qa pairs, covering Physical AI target domains including autonomous vehicle (AV) driving, robotics, industry (smart space), physics, human, and common sense. All the questions are binary questions, and the answer is either Yes or No. Our dataset is a benchmark designed to evaluate world models for Physical AI.

This dataset is ready for non-commercial use.

## License/Terms of Use

The use of this dataset is governed by [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en).

## Intended Usage

This benchmark dataset is intended to demonstrate and facilitate the understanding and evaluation of world models for Physical AI. It should primarily be used for educational and demonstration purposes.

## Dataset Characterization

This dataset focuses on the following areas: Autonomous Vehicle (AV) driving, Robotics, Industry (smart space), Physics, Human, Common Sense.

### Data Collection Method

- AV: Automatic/Sensors
- Industry: Automatic/Sensors
- Physics: Automatic/Sensors
- Robotics: Automatic/Sensors
- Human: Automatic/Sensors
- Common Sense: Human

### Labeling Method

- AV: Hybrid: Human, Automated
- Industry: Hybrid: Human, Automated
- Physics: Hybrid: Human, Automated
- Robotics: Hybrid: Human, Automated
- Human: Hybrid: Human, Automated
- Common Sense: Hybrid: Human, Automated

## Folder Structure

```text
pbench/
├── condition_image/                       # Conditioning images for all domains
├── vqa/                                   # Visual Question Answering pairs
└── cosmos_predict2_bench_full_info.json   # Complete dataset metadata
```

## Dataset Format

- Modality: Image (jpg) and Text

## Dataset Quantification

The dataset is stored in JSON files. The quantity, including the conditioning images, text prompts, and qa pairs, of the Pbench dataset is described in the table below.

| Domain                 | Quantity   |
| ---------------------- | ---------- |
| AV                     | 118        |
| Common Sense           | 239        |
| Human                  | 299        |
| Industry               | 107        |
| Physics                | 107        |
| Robotics               | 174        |
| **Total Storage Size** | **226 MB** |

## Reference(s)

Not published yet. We will publish the paper later add the link to the paper when it’s ready.