让AI视频自己发现物理错误并修正
现在的AI视频生成，画面越来越真，但物理常识还是硬伤：杯子掉下来会穿桌、人走路会飘。这篇的思路是让大语言模型当“物理老师”，去检查AI生成的视频哪里不对，再指导它重拍。具体做法是把生成、挑选、纠错串成一条流水线，LLM在每一步都盯着物理合理性，产出100万条物理正确的训练视频。它不是你明天能用的工具，但这是AI视频从“看着像”走向“真懂物理”的关键一步。
📄 原文摘要(英文)
Video generation models produce visually realistic videos but still struggle with physical dynamics and causal consistency. Learning from generated videos requires fine-grained physical supervision, yet reliable quality judgments, correction pairs, and controlled variations are difficult to obtain. Large language models (LLMs) possess physical knowledge, but struggle to reliably guide video generation models in producing their training videos. We present PhysLoop, a unified harness designed to support physically grounded self-improvement in video generation models. The harness guides and constrains LLMs throughout video generation, selection, and correction, distilling their physical knowledge into physically consistent videos. The framework supports controllable training-data construction and inference-time video repair. By identifying and correcting failures, PhysLoop marks a key milestone toward recursive self-improvement (RSI) in video generation models with limited human intervention. We release 100K videos spanning diverse, realistic indoor and outdoor scenes, along with generation tools and two evaluation datasets: PhysLoop-Gen and PhysLoop-Repair. We evaluate controllable generation and localized repair on these datasets, respectively, and assess physical behavior on the external Physics-IQ Verified benchmark.