README.md

metadata

pretty_name: WRBench Evaluation Results
license: apache-2.0
language:
  - en
tags:
  - tabular
  - video-generation
  - world-models
  - benchmark
  - evaluation
  - leaderboard
  - wrbench
  - arxiv:2606.20545
task_categories:
  - image-to-video
size_categories:
  - n<1K
configs:
  - config_name: model_scores
    data_files:
      - split: default
        path: data/model_scores.parquet

WRBench Evaluation Results

Model-level WRBench scores for the current public WRBench release.

These are the aggregate scores shown in the WRBench Leaderboard. Scores are in [0, 1]; higher is better.

Version Note

The paper reports a frozen benchmark table. The Hugging Face datasets and leaderboard can be refreshed as new public results are released, so exact model, video, and support counts should be read from the data files.

What The Scores Mean

• Cam Align.: whether the generated video follows the requested viewpoint.
• Integ.: visual integrity and readability.
• Vis. spatial: spatial consistency while the relevant object is visible.
• Vis. state: action/state consistency while the relevant object is visible.
• Ret. spatial: spatial consistency after an object leaves view and returns.
• Ret. state: state consistency after an object leaves view and returns.
• Reobs. Support: how often the returned-object case is judgeable. This is a coverage statistic, not a quality score.
• Avg: the main WRBench average over the six score columns.

Load

from datasets import load_dataset

scores = load_dataset("WRBench/wrbench-results", "model_scores", split="default")
df = scores.to_pandas().sort_values("Avg", ascending=False)

Links

• Leaderboard: https://huggingface.co/spaces/WRBench/wrbench-leaderboard
• Videos: https://huggingface.co/datasets/WRBench/wrbench-videos
• Prompts: https://huggingface.co/datasets/WRBench/wrbench-natural25
• Human annotations: https://huggingface.co/datasets/WRBench/wrbench-human-annotations
• Collection: https://huggingface.co/collections/WRBench/wrbench-current-world-models-lack-a-persistent-state-core
• Paper: https://arxiv.org/abs/2606.20545
• Code: https://github.com/JinPLu/WRBench

Citation

@misc{lu2026currentworldmodelslack,
      title={Current World Models Lack a Persistent State Core},
      author={Jinpeng Lu and Dexu Zhu and Haoyuan Shi and Linghan Cai and Guo Tang and Yinda Chen and Jie Cao and Duyu Tang and Yi Zhang and Yong Dai and Xiaozhu Ju},
      year={2026},
      eprint={2606.20545},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.20545},
}