DeepMind发布了Physics-IQ验证版，专门评测视频模型对物理世界的理解。现有基准有缺陷，他们修正后让模型排名更可信了。
该研究系统审计了Physics-IQ视频物理理解基准，发现其提示质量和真实标注存在缺陷。作者提出三项改进措施，包括优化提示与真值、引入样本级评分系统，并应用六种图像到视频生成模型验证。新版Physics-IQ Verified改进了57.6%的样本和34.8%的提示，模型排名变化中度显著（Kendall's τ=0.46）。
Physics-IQ Verified
Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To advance these tasks, a good video model must understand the physical reality of the world. Evaluating this understanding is an emerging field and has led to the Physics-IQ benchmark, which quantifies this explicitly by comparing model-generated videos to real-world videos of physical experiments. In this work, we present a systematic audit of the Physics-IQ benchmark, expose shortcomings and propose three solutions that sharpen how we can measure physical understanding of VGMs. Specifically, we improve prompt and ground-truth quality to reduce the influence of confounding factors and further introduce a sample-level scoring system that weights each sample and metric equally. Our resulting benchmark, Physics-IQ Verified, refines 57.6\% of all samples and improves over 34.8\% of prompts. In a comparison study using six image-to-video generative models, we observe moderate but meaningful ranking changes (Kendall's $τ= 0.46$). We hope Physics-IQ Verified advances the community by providing a more reliable signal toward physically accurate VGMs. The code for the benchmark can be accessed at https://github.com/google-deepmind/physics-iq-benchmark