YoCausal：从因果视角看视频生成与世界模型的差距

摘要

随着视频扩散模型（VDM）向世界模型迈进，一个关键问题随之浮现：它们是否真正理解因果关系，抑或只是过度拟合了统计性的时间模式？现有的基准测试大多依赖合成数据，但由于模拟到真实的差距，限制了其在现实世界中的泛化能力。我们提出YoCausal，这是一个受认知科学中“预期违反”（VoE）范式启发的两级基准测试。通过以零成本对真实世界视频进行时间反转，将其作为自然的反事实样本，YoCausal建立了一种可任意扩展的评估协议。第一级引入了反转惊奇指数（RSI），通过去噪损失量化时间箭头感知。第二级引入了因果关系认知指数（CCI），利用视觉语言模型（VLM）将数据集分层为因果与非因果子集，从而将真正的因果推理与时间偏差区分开来。对13个最先进的VDM的评估表明，感知时间箭头并不等同于理解因果关系，并且与人类水平的因果认知相比，仍存在显著差距。

English

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.