Teaching Vision-Language Models to Use the Scale They Are Given: Label-Free Equivariance Training for Metric Physical Reasoning
Metric questions about video, such as the speed of a moving object, require a vision-language model to convert visual measurements into physical units using a real-world reference supplied in the prompt. We find that current models use this reference only partially. When every world-space quantity in the prompt is multiplied by a common factor, the prompt still describes the same video and the correct answer changes by exactly that factor, but the predictions of eight models change by less, and their accuracy stays concentrated near the scale that the depicted objects usually have. Asked the same physics in a scale-free form, the two models we test recover the closed-form scaling laws on ...
| Authors | Kaizhen Tan, Yang Feng, Heqing Du et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 September 2026 |
| Last updated at source | 4 October 2026 |
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