PAWBench：我們距離機率對齊的世界建模還有多遠？

摘要

近期的影片生成模型越來越常被視為世界模型。許多物理過程可能以多種有效方式展開。因此，世界模型不僅應重現合理的軌跡，還應重現相同初始觀測與動作下可能行為的分布。我們將這種分布層級的要求稱為機率對齊。然而，現有的評測大多評估單一影片的合理性，並未檢驗重複生成是否能還原正確的分布。這引出一個核心問題：目前的影片生成器距離機率對齊的世界建模還有多遠？為了解答這個問題，我們將機率對齊形式化為世界模型的分布性標準，並引入 PAWBench，一個用於評測影片生成器作為世界動態隨機採樣器的基準。我們進一步提出 PAWEval，一個結果層級的協議，將重複的影片推演轉換為可能物理行為上的經驗分布。在五十個情境與十一個現有系統中，沒有任何模型能在還原有效行為範圍的同時，一致地匹配參考機率。在確立此差距後，我們測試語言提示、初始雜訊採樣或模型訓練是否能重塑模型的預測分布。我們相信我們的工作可作為未來邁向機率對齊世界建模的基礎。

English

Recent video generation models are increasingly framed as world models. Many physical processes can unfold in more than one valid way. Therefore, a world model should reproduce not only a plausible trajectory, but also the distribution of possible behaviors under the same initial observation and action. We call this distribution-level requirement probabilistic alignment. However, existing evaluations largely assess individual-video plausibility and do not test whether repeated generations recover the correct distribution. This raises a central question: how far are current video generators from probabilistically aligned world modeling? To answer it, we formalize probabilistic alignment as a distributional criterion for world models and introduce PAWBench, a benchmark for evaluating video generators as stochastic samplers of world dynamics. We further introduce PAWEval, an outcome-level protocol that converts repeated video rollouts into empirical distributions over possible physical behaviors. Across 50 scenarios and eleven current systems, no model consistently matches the reference probabilities while recovering the range of valid behaviors. Having established this gap, we test whether language prompts, initial noise sampling, or model training can reshape the model's predictive distribution. We believe our work can serve as a foundation for future efforts to move towards probabilistically aligned world modeling.

PAWBench：我們距離機率對齊的世界建模還有多遠？

PAWBench: How Far Are We from Probabilistically Aligned World Modeling?

摘要

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