镜头之外，世界还在继续
视频生成模型能把画面做得越来越真，但它只是在画像素，不会想「接下来该发生什么」。这篇把这件事挑明了：野兽出现后，模型该画出野兽，也该推断旁边的人是逃、躲还是反击，再把结果演给你看——哪怕镜头已经移开。他们给这个能力起了个名字叫「视野外因果演化」，做法是让一个语言模型当「世界大脑」:它看已生成的片段，维护一份世界当前状态的账本，推断事件会带来什么后果，再交给视频渲染器把后果画出来，两段之间还能接得上。他们同时建了训练数据和评测集，测三件事：事件有没有真的发生、反应是否合乎因果、镜头转回来时世界是否还对得上。结果是这类因果演化明显好于现有模型，画面质量没掉。它不是你明天能用上的东西，但它是「视频模型从画得像，走向想得通」的一个参照点。
📄 原文摘要(英文)
Generative video models are increasingly used as visual world simulators, yet their primary strength lies in rendering plausible pixels rather than reasoning about how a world should evolve. Coherent world evolution requires more than visual fidelity: when an event occurs, a model must infer its consequences for the entities in the world and then render the resulting state. For example, after a wild beast appears, the model should not only depict the wild beast, but also infer whether a nearby person should flee, take cover, or fight back based on the realized scene. Existing diffusion-based video models lack an explicit mechanism for maintaining world state and reasoning over such event-conditioned consequences, often producing visually plausible but behaviorally static or causally inconsistent outputs. To make this reasoning gap concrete and measurable, we propose a new problem, Out-of-Sight Causal Evolution, which requires a model to render an event, reason about its consequences for the evolving world state, and reveal a causally plausible outcome as the world continues to evolve, even out of sight. As a baseline reference solution, we introduce EvoWorld, a brain-renderer collaboration framework in which a vision-language model serves as the world brain: it observes generated segments, maintains an evolving account of the world, and reasons about plausible state transitions, while a segment-autoregressive video renderer realizes them visually. We further introduce EvoWorld-Data and EvoWorld-Bench for training and evaluating event realization, causal reactions, and revisit consistency. Experiments show that EvoWorld substantially improves event-conditioned causal evolution over recent models while preserving visual quality and consistency, establishing a reference point for reasoning-guided, autonomous world evolution beyond the current field of view.