具有反事实可控性的自主视频生成用于自进化世界模型
Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models
AI总结：
本文提出反事实可控性是自进化世界模型的关键，通过自主视频生成实现可控性，使模型能测试动作后果并反馈改进生成。
摘要：
现有文献声称视频生成本质上是世界建模。一方面，这一说法富有成效，因为它将生成式AI推向静态图像之外，扩展到时间延展的物理场景。另一方面，这一说法危险地依赖于一种信念，即仅靠扩展视觉预测就能自动产生物理智能体。我们更倾向于一个更准确的表述：视频生成模型学习了一个部分的、隐式的时空世界模型，但并非完全接地或可控的。原因如下：一个模型可能生成一个无人机穿越森林或机器人手臂操纵杯子的合理视频，但仍然不知道哪些变量是可控制的，哪些约束属于特定身体，以及哪些未来在干预下仍然有效。前沿本质上不仅仅是预测现实主义，而是强调一种自进化的生成性质，需要决定性的标准是反事实可控性：即询问在某个动作下会发生什么的能力，测试生成的未来能否承受具身约束，并将由此产生的动作知识反馈到未来的想象（生成）中。因此，在本文中，我们提出一个新的视角，即具有反事实可控性的自主视频生成是实现自进化世界模型的一种有前景的方式。
英文摘要：
Large-scale video generation models are increasingly described as world models because they can learn rich spatiotemporal regularities from visual data. However, we argue that an ideal world model should benefit in a self-evolving generative character. Traditional visually plausible predictions alone are not enough to establish whether an imagined future is physically actionable for a particular embodied agent, failing to provide informative feedback from environments for self-evolving improvement. To realize self-evolving world models, this article proposes the concept of autonomous video generation, which is evaluated through counterfactual controllability, i.e., the ability to i) generate intervention-conditioned futures, ii) bind these future frames to embodiment constraints, iii) verify them under distribution shifts, and iv) distil surviving branches into compact variables for decision-making. We formalize a four-stage closed-loop optimization of Generation, Binding, Verification and Distillation, together with four corresponding evaluation metrics: novelty, consistency, out-of-distribution (OOD) and efficiency. We further discuss two examples, i.e., drones and manipulators, as early embodied testbeds where wind, sensing limits, actuation delay, contact dynamics and recovery constraints can be systematically perturbed and verified. The central claim is that the framework of autonomous video generation for self-evolving world models should not be judged by video fidelity alone, but by whether the generated frames improve valid action under counterfactual interventions and various embodiment constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
