AI 预测世界下一步画面，最强模型刚及格
现在的多模态 AI 不只是看图说话，开始被要求预测世界下一步会变成什么样：比如看完一段厨房操作，接着画出下一步的画面。研究者把这种能力分成四级，发现越高级的任务越没人做，于是做了个 320 道题的基准测试，考 12 个模型。结果很分裂：OpenAI 的 GPT-Image-2.5 系统拿到 72.8 分，已经能在真实任务上做出早期预测；但最强的开源模型只有 20.1 分，复杂推理和多重条件一上就崩。这不是你明天能用的东西，但它划出了一条清晰的分界线：闭源前沿模型正在从生成静态图走向动态世界模拟，而开源阵营还差着好几代。
📄 原文摘要(英文)
Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integrate multimodal context and synthesize visual outputs that depict changes in the world. To characterize this emerging trajectory, we organize model capabilities into a four-level hierarchy and discover that higher-level tasks remain relatively underexplored despite model progress. To bridge this gap, we explore model performance on multi-step world state prediction, a high-level task that requires models to forecast successive future world states from multimodal observations and diverse references. Such a task requires models to possess entangled understanding and generation capabilities, as well as extensive world knowledge to translate human action sequences, physical transformations, and abstract visual reasoning into coherent visual outputs. We introduce WorldCast, a benchmark comprising 320 manually designed cases across eight categories, and evaluate 12 representative models. The conducted experiments yield three key insights: (a) advances in frontier models are rapidly pushing the boundaries of intelligent world modeling, with the GPT-Image-2.5 System scoring 72.8 and demonstrating early-stage visual state prediction capabilities on real-world tasks; (b) WorldCast reveals substantial gaps between models: even Nano Banana Pro reaches only 42.8 and the best open-source model scores 20.1, with deficits in handling complex context, entangled references, and reasoning-heavy tasks; (c) strong generative models effectively draw on the capabilities of their underlying language models. These findings highlight the need for multimodal generative models that are capable of dynamic planning and reasoning while also being deeply unified to faithfully simulate real-world states.