Datasets:

twanghcmut
/

wmbench

license: cc-by-4.0
language:
  - en
tags:
  - robotics
  - video
  - hallucination-detection
  - world-models
pretty_name: WMBench — World-Model video benchmark for hallucination detection

WMBench

Benchmark suite for evaluating hallucination / anomaly detection on video generated by world models (Cosmos, OpenSora, etc.) against real robot training distributions.

Datasets

Dataset	Source	Tasks	Videos
`gr-1/`	NVIDIA GR1 (`PhysicalAI-Robotics-GR00T-GR1`)	5 eval tasks	5 real + 24 Cosmos gens
`droid/` (planned)	Stanford DROID	—	—

Each dataset folder contains:

training/ Real training mp4s (one per task)
generated/ World-model generated mp4s, grouped by task
reference/ 50 SAM3-segmented PNG frames per task (for null calibration)
null_per_task/ Pre-computed cycle null distributions (.npz)
results/ WarpDyn scores, ranking, visualizations
method.md Per-dataset method notes / reproducibility

Method: WarpDyn

Pure feature-matching anomaly detector using RoMa cycle composition error, per-task multi-lag null distribution, and per-task ratio scoring.

See gr-1/method.md for the full step-by-step method.