| title | YoCausal: How Far is Video Generation from World Model? A Causality Perspective |
|---|---|
| authors | You-Zhe Xie, Yu-Hsuan Li, Jie-Ying Lee, Kaipeng Zhang, Yu-Lun Liu, Zhixiang Wang |
| date | 2026-05-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.30346 |
| affiliation | Alaya Lab |
| upvotes | 49 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | World models |
| abstract | As video diffusion models (VDMs) advance toward world models, a key question arises: do they truly understand causality, or merely overfit to statistical temporal patterns? Existing benchmarks mostly rely on synthetic data, limiting real-world generalization due to the sim-to-real gap. We present YoCausal, a two-level benchmark inspired by the Violation of Expectation (VoE) paradigm from cognitive science. By temporally reversing real-world videos at zero cost as natural counterfactual samples, YoCausal establishes an arbitrarily extensible evaluation protocol. Level 1 introduces the Reverse Surprise Index (RSI), quantifying arrow-of-time perception via denoising loss. Level 2 introduces the Causality Cognition Index (CCI), which leverages a VLM to stratify datasets into causal and non-causal subsets, disentangling genuine causal reasoning from temporal bias. Evaluation of 13 state-of-the-art VDMs reveals that perceiving the arrow of time does not imply understanding causality, and a significant gap persists relative to human-level causal cognition. |
cite
@misc{xie2026yocausal,
title={YoCausal: How Far is Video Generation from World Model? A Causality Perspective},
author={You-Zhe Xie and Yu-Hsuan Li and Jie-Ying Lee and Kaipeng Zhang and Yu-Lun Liu and Zhixiang Wang},
year={2026},
eprint={2605.30346},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.30346}
}