README.md

---
pretty_name: PAWBench
size_categories:
  - n<1K
tags:
  - benchmark
  - video-generation
  - physical-reasoning
  - arxiv:2608.27345
---

# PAWBench

### A benchmark for distributional physical realism in generated videos

PAWBench asks whether a video model reproduces the *range and frequency* of
outcomes that a physical scene can produce. It evaluates repeated rollouts from
the same source image and action, rather than treating one plausible-looking
video as sufficient evidence of physical realism.

[Paper](https://arxiv.org/abs/2608.27345) ·
[Code](https://github.com/Andrew0613/PAWBench) ·
[Project website](https://pawbench.github.io/)

## At a glance

| Component | Contents |
| --- | --- |
| Scenes | 50 physical scenes |
| Calibration track | 25 scenes with reference outcome distributions |
| Coverage track | 25 scenes with supported outcome labels |
| Source images | 50 first-frame images |
| Prompt material | One base prompt per scene and the `gt_guided` prompt bank |
| Evaluator | [PAWBench code](https://github.com/Andrew0613/PAWBench) |

## Repository layout

```text
.
├── manifest.json
├── scenes.jsonl
├── source_images/
│   └── <scene source image>.png|jpg
└── prompts/
    └── gt_guided/
        ├── prompt_bank.jsonl
        ├── README.md
        └── review.md
```

`manifest.json` declares the package schema and points consumers to the scene
table. `scenes.jsonl` is the benchmark contract: each line is one scene and
paths are relative to this repository root.

## Scene format

Every scene row contains the model input needed to generate a rollout and the
outcome contract needed to evaluate it.

| Field | Meaning |
| --- | --- |
| `scene_id` | Stable scene identifier |
| `split` | `calibration` or `coverage` |
| `source_image_path` | Local first-frame image path |
| `action` | Physical action to be performed |
| `base_prompt` | Baseline image-to-video prompt |
| `outcome_labels` | Canonical labels for observable outcomes |
| `reference_distribution` | Calibration-only target distribution; `null` for Coverage |

Illustrative Calibration record:

```json
{
  "scene_id": "A-01",
  "split": "calibration",
  "source_image_path": "source_images/A01.png",
  "action": "Flick the coin once.",
  "outcome_labels": ["heads", "tails"],
  "reference_distribution": {"heads": 0.5, "tails": 0.5}
}
```

## Using the data

Download this dataset, generate the complete rollout grid described by the
[PAWBench evaluator](https://github.com/Andrew0613/PAWBench), and run the
repository's command-line workflow:

```bash
hf download Andrew613/PAWBench \
  --repo-type dataset \
  --local-dir /path/to/PAWBench-data

git clone https://github.com/Andrew0613/PAWBench.git
cd PAWBench
pip install -r requirements.txt

python evaluate.py \
  --benchmark /path/to/PAWBench-data \
  --videos /path/to/my-model-rollouts \
  --output runs/my-model/evaluation \
  --model my-model \
  --vlm-base-url https://openrouter.ai/api/v1 \
  --vlm-model google/gemini-3.5-flash \
  --vlm-api-key-env OPENROUTER_API_KEY
```

The evaluator derives the full 50-scene × 50-rollout grid from the scene
table. Missing or malformed items remain visible as blockers and do not shrink
the benchmark denominator.

## Scope

This dataset repository contains benchmark inputs only:

- scene definitions, source images, and official prompt material are included;
- PAWEval code and its rubric implementation live in the GitHub repository;
- generated model videos, provider responses, and experiment result bundles are
  intentionally excluded.

This public repository preserves the materialized 50-scene benchmark package.
Record the exact Hugging Face revision together with the evaluator commit for
reproducible evaluations.

## Citation

If you use PAWBench, please cite the paper and record the exact Hugging Face
revision and GitHub commit used for your evaluation:

```bibtex
@article{pu2026pawbench,
  title={PAWBench: How Far Are We from Probabilistically Aligned World Modeling?},
  author={Yuandong Pu and Le Zhuo and Sayak Paul and Gabriel Jorge Menezes and Avram Đorđević and Shiyang Li and Yifan Zhou and Bin Fu and Wenlong Zhang and Junjun He and Yu Qiao and Yihao Liu and Jinbo Xing and Xi Chen},
  journal={arXiv preprint arXiv:2608.27345},
  year={2026},
  eprint={2608.27345},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2608.27345}
}
```

## License and usage

The Apache-2.0 license in the
[PAWBench code repository](https://github.com/Andrew0613/PAWBench) applies to
the evaluator source code, not automatically to this dataset's images, prompt
material, or scene metadata. No separate license has been selected for this
dataset repository. Do not infer permission to redistribute or create
derivatives of its assets without permission from the project owners. Source
and package provenance is recorded in `scenes.jsonl` and `manifest.json`.