AI 学物理，先写“物理语言”再画视频
现在的 AI 预测视频，是直接猜下一帧的像素，像蒙着眼画画——它不知道画面里发生了什么，只是硬记颜色和形状。这篇换了个思路：先让 AI 从海量视频里自学出一套“物理语言”，把“球滚过去”“杯子倒下”这类状态变化写成简短的符号序列，再用这套语言推理未来，最后才把推理结果渲染成视频。等于先想清楚“接下来会发生什么”，再动手画。结果在生成和理解两类任务上都更符合物理规律，还能做动作控制的模拟和零样本动作迁移。它不是你明天能用上的东西，但这是 AI 从“会画”走向“懂世界”的一个关键转向。
📄 原文摘要(英文)
We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing physical world models typically predict future videos directly in pixel space, leaving the underlying world dynamics implicit within high-dimensional visual predictors. Motivated by humans' ability to abstract predictive structure from visual experience and organize it in natural language for explicit reasoning, we learn physical language from in-the-wild videos through self-supervision and use it to explicitly reason about how the physical world evolves. Accordingly, PhiZero adopts a reason-then-render paradigm: it first infers future world evolution as a physical-language sequence and then renders the inferred transitions into videos. Extensive experiments across generation and understanding benchmarks validate the ability of PhiZero to model physically coherent world evolution. We further show its potential for realistic and interactive world modeling, fine-grained action-conditioned simulation, and zero-shot motion transfer.