| title | CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models |
|---|---|
| authors | León Begiristain, Olaf Dünkel, Adam Kortylewski |
| date | 2026-05-22 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.23699 |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | World models |
| abstract | Video prediction is increasingly viewed as a path toward generalizable world models, yet it remains unclear whether these systems learn underlying causal structure or merely exploit superficial visual correlations for future prediction. We introduce CRONOS, an intervention-based benchmark designed to evaluate counterfactual physical consistency: whether a model's predictions of physical events respond appropriately to controlled changes in the visual input, such as variations of scene context, viewpoint, object appearance, and object category. Built in a photorealistic Unreal Engine environment, CRONOS enables controlled, high-fidelity generation of videos across diverse scenes and dynamics. In contrast to previous benchmarks, CRONOS systematically intervenes on four key factors - viewpoint, scene, object category, and object appearance - while keeping the underlying physical event type, such as a collision, occlusion, or fall, fixed. Our evaluation of recent open-source video generators reveals substantial failures in counterfactual physical consistency: prediction quality for the same physical event type is affected by appearance, environment, and, particularly by viewpoint changes. CRONOS provides a controlled and reproducible testbed for diagnosing how the quality of generated videos changes for different interventions, establishing a concrete target for developing models that perform consistently across changes of multiple conditions. The dataset and code are available at our project page. |
cite
@misc{begiristain2026cronos,
title={CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models},
author={León Begiristain and Olaf Dünkel and Adam Kortylewski},
year={2026},
eprint={2605.23699},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.23699}
}