Abstract
We present Probabilistic Structure Integration (PSI), a system for learning richly controllable and flexibly promptable world models from data. PSI consists of a three-step cycle. The first step, Probabilistic prediction, involves building a probabilistic graphical model Psi of the data, in the form of a random-access autoregressive sequence model. Psi supports a complete set of learned conditional distributions describing the dependence of any variables in the data on any other set of variables. In step 2, Structure extraction, we show how to extract underlying low-dimensional properties in the data, corresponding to a diverse set of meaningful "intermediate structures", in a zero-shot fashion via causal inference on Psi. Step 3, Integration, completes the cycle by converting these structures into new token types that are then continually mixed back into the training diet as conditioning signals and prediction targets. Each such cycle augments the capabilities of Psi, both allowing it to model the underlying data better, and creating new control handles -- akin to an LLM-like universal prompting language. We train an instance of Psi on 1.4 trillion tokens of internet video data; we use it to perform a variety of useful video prediction and understanding inferences; we extract state-of-the-art optical flow, self-supervised depth and object segmentation; and we use these structures to support a full cycle of predictive improvements.
Abstract (translated)
我们提出了概率结构集成（PSI），这是一个从数据中学习丰富可控且灵活可提示的世界模型的系统。PSI由一个三步循环组成。 第一步，概率预测，涉及构建数据的概率图模型Psi，形式为随机访问自回归序列模型。Psi支持一组完整的学习条件分布，描述了数据中的任何变量对其他任意集合变量的依赖关系。 第二步，结构提取，在此步骤中我们展示了如何通过在Psi上进行因果推理来以零样本方式抽取数据中的底层低维属性，对应于一系列有意义的“中间结构”。 第三步，集成，该循环通过将这些结构转换为新的标记类型完成，然后将其不断混合回训练过程中作为条件信号和预测目标。每次这样的循环都会增强Psi的能力，使其更好地建模基础数据，并创建新的控制手柄——类似于类似大型语言模型（LLM）的通用提示语言。 我们在1.4万亿个互联网视频令牌的数据上对Psi的一个实例进行了训练；我们使用它来执行各种有用的视频预测和理解推理；提取最先进的光流、自我监督深度和对象分割技术；并利用这些结构支持整个预测改进循环。
URL
https://arxiv.org/abs/2509.09737
