AI视频模型缺一个婴儿都有的能力：物体不会凭空消失
婴儿在几个月大时就懂“物体被挡住还在那里”，但最强的视频生成模型未必懂——它们可能让球穿墙、让物体凭空消失。研究者造了一套150个认知科学任务、150万样本的训练集，专门教模型“物体恒存”和“固体不可穿透”这两条物理直觉。用这套数据训练出的16B模型，在盲测中击败了多数同类视频模型。它不是你明天能用上的工具，但它指向一个关键趋势：AI的“物理直觉”不是靠堆算力长出来的，而是可以像教小孩一样，用精心设计的认知任务直接训练出来。
📄 原文摘要(英文)
Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a paradigmatic class of current world models, have begun to show emerged reasoning abilities, making them ideal candidates for building human-like physical intelligence. Do video models have emerged object permanence in them? If not, could we train them with a core-cognition inspired dataset? We introduce WROP (World Reasoning with Object Permanence), a data infrastructure of 150 hand-designed cognitive science inspired tasks, divided into six cognitive categories. We build Blender generators that randomize speed, lighting, camera angle, and other nuisance parameters while preserving each task's cognitive structure, yielding 10,000+ samples per task. We release a 1.5M-sample training corpus and a 300-question exam. On this exam we evaluate 14 video models: 3 reference-to-video, 7 edit, and 4 continuation, among which PWM-WROP, our 16B world model. In a blind pairwise Elo study, PWM-WROP ranks first among continuation models and third overall, behind only a statistical tie between two reference-to-video models. We release the data, exam, model answers, scores, weights, and PWM, our native-PyTorch training stack on AWS Trainium2.