New benchmark tests physical consistency of video world models; best scores 57.76/100
An arXiv paper introduces World Models' Last Exam in Physics, a measurement-based benchmark for physical consistency in video world models. It covers 40 controlled tasks spanning mechanics, optics, fluids, thermal and phase-change phenomena, electromagnetism and surface tension, each pairing an initial image and generation prompt with predefined physical criteria; the evaluator combines task-observability screening with task-specific quantitative measurements. Across eight video generation models and 1,280 videos, physical inconsistencies persisted with wide variation between tasks, and the best model scored 57.76 out of 100. The authors report that on synthetic videos with known physical relationships, the evaluator agreed with human judgments more than a direct vision-language-model baseline in both within-task rankings and pairwise comparisons.
Why it matters: It offers an interpretable, reference-video-free way to diagnose physical inconsistencies and track progress toward physically consistent world models.
Computer Science > Computer Vision and Pattern Recognition
arXiv:2610.08791 (cs)
[Submitted on 6 Oct 2026]
Title:World Models' Last Exam in Physics
Authors:Mingju Gao, Qingle Liu, Yuzhao Peng, Xinjie Lin, Ziming Qin, Zheng Jiang, Wenyi Li, Calvin Xiao, Youjie Zheng, Kaisen Yang, Qinhuai Na
View a PDF of the paper titled World Models' Last Exam in Physics, by Mingju Gao and 10 other authors
View PDF HTML (experimental)Abstract:Video world models can produce visually convincing yet physically inconsistent sequences, raising concerns about their reliability for prediction and planning in embodied AI systems. Existing evaluations often rely on model-based judgments or reference videos, while direct physical tests largely focus on mechanics. We introduce World Models' Last Exam in Physics, a measurement-based benchmark for evaluating physical consistency in video world models. The benchmark comprises 40 controlled tasks spanning mechanics, optics, fluids, thermal and phase-change phenomena, electromagnetism, and surface tension. Each task pairs an initial image and a generation prompt with predefined physical criteria, enabling interpretable tests of observable physical relationships without requiring reference videos. Its evaluator combines task-observability screening with task-specific quantitative physical measurements. Experiments on eight video generation models across 1,280 videos reveal persistent physical inconsistencies and substantial variation across tasks, with the best model achieving an overall score of 57.76 out of 100. Evaluation on synthetic videos with known physical relationships provides evidence for the validity of the measurement module under controlled conditions. The evaluator also achieves higher agreement with human judgments than a direct vision-language model baseline in both within-task rankings and pairwise comparisons. By combining coverage across physical domains with scores grounded in measurable evidence and explicit measurement limitations, the benchmark provides an interpretable basis for diagnosing physical inconsistencies and tracking progress toward physically consistent video world models.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.08791 [cs.CV] |
| (or arXiv:2610.08791v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08791 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mingju Gao [view email][v1] Tue, 6 Oct 2026 17:59:56 UTC (2,281 KB)
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View a PDF of the paper titled World Models' Last Exam in Physics, by Mingju Gao and 10 other authors
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