Hallucinations in world models are predictable failures tied to low data coverage—the same signals that detect them can guide targeted data collection to fix them with minimal real-world trajectories.
World models that predict future video frames often hallucinate—generating visually smooth but physically incorrect predictions. This paper shows hallucinations occur in under-explored parts of the state-action space and can be detected and prevented using data-centric signals.