README.md

metadata

pretty_name: WRBench Human Annotations
license: apache-2.0
language:
  - en
tags:
  - video
  - video-generation
  - world-models
  - benchmark
  - evaluation
  - human-evaluation
  - human-feedback
  - wrbench
  - arxiv:2606.20545
task_categories:
  - image-to-video
size_categories:
  - 1K<n<10K
configs:
  - config_name: pairs
    data_files:
      - split: default
        path: data/pairs.parquet
  - config_name: annotation_to_video
    data_files:
      - split: default
        path: data/annotation_to_video.parquet
  - config_name: exclusions
    data_files:
      - split: default
        path: data/exclusions.parquet

WRBench Human Annotations

Human pairwise-comparison metadata that calibrates the WRBench automated diagnostic metrics in Current World Models Lack a Persistent State Core. Each clean pair is scrubbed to public fields and mapped to the stable video_asset_id values used in WRBench/wrbench-videos.

Release boundary: 1,156 clean pairs with 2,547 annotator verdict records. Maintenance-queue pairs are withheld and listed in the exclusions config so the boundary is auditable.

Configs & schema

pairs

Field	Type	Description
pair_id	string	Stable pair identifier
dimension	string	Evaluation dimension the comparison targets (D1-D6)
video_asset_id_a / _b	string	sha256:<hash> of the two compared clips
verdict	string	Aggregated pairwise outcome (A / B / tie)
verdict_annotators	list	Anonymized annotator ids (p1, p2, …)
n_verdict_annotators	int	Number of annotators contributing a verdict
created_at	string	Annotation timestamp

annotation_to_video

video_asset_id → pair_id index for joining annotations back to clips.

exclusions

Maintenance-queue pairs withheld from the release, with a reason code.

Load

from datasets import load_dataset

pairs = load_dataset("WRBench/wrbench-human-annotations", "pairs", split="default")

Privacy

Public fields only: pair_id, anonymized verdict_annotators, timestamps, video_asset_id, and release-safe endpoint metadata. No internal paths, raw annotator identities, or other PII are included. See datasheet.md for the full data statement.

Links

• 🎞️ Videos: https://huggingface.co/datasets/WRBench/wrbench-videos
• 📊 Results table: https://huggingface.co/datasets/WRBench/wrbench-results
• 🖼️ Natural-25 prompts: https://huggingface.co/datasets/WRBench/wrbench-natural25
• 🏆 Leaderboard: https://huggingface.co/spaces/WRBench/wrbench-leaderboard
• 🧭 Release collection: https://huggingface.co/collections/WRBench/wrbench-current-world-models-lack-a-persistent-state-core-6a365c717251293c9fc2cc26
• 💻 GitHub: https://github.com/JinPLu/WRBench
• 🌐 Project page: https://jinplu.github.io/WRBench/
• 📄 Paper: https://arxiv.org/abs/2606.20545
• 🤗 HF paper page: https://huggingface.co/papers/2606.20545

Citation

@article{wrbench2026,
  title   = {Current World Models Lack a Persistent State Core},
  author  = {WRBench Team},
  journal = {arXiv preprint arXiv:2606.20545},
  year    = {2026},
  url     = {https://arxiv.org/abs/2606.20545}
}