[2607.25537] Visual prompt engineering for video models

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arXiv:2607.25537 (cs)

[Submitted on 28 Jul 2026]

Title:Visual prompt engineering for video models

Authors:Robert Geirhos, Yuxuan Li, Thaddäus Wiedemer, Neha Kalibhat, Zi Wang, Mani Malek, Oyvind Tafjord, Kevin Swersky, Been Kim, Priyank Jaini<br>View a PDF of the paper titled Visual prompt engineering for video models, by Robert Geirhos and 9 other authors

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Abstract:In the age of foundation models, a model is only as good as its prompt. For this reason, prompt engineering has become an essential technique for improving language model performance. Since video models are currently becoming foundation models for visual tasks (e.g., visual reasoning), we here ask whether they similarly benefit from visual prompt engineering: automatically modifying the task image to improve model performance. For example, for a visual physics reasoning task ("Where does the ball land, after passing a set of obstacles?"), an abstract sketch-like scene can be turned into a photorealistic version with a simple call to an image editing model. We find that visual prompt engineering, or VIPE for short, improves video reasoning performance across tasks. In fact, for video models, visual prompt engineering can be even more effective than classic text-based prompt engineering or test-time scaling. Ultimately, just as text-based prompt engineering systematically improves language model performance, visual prompt engineering can serve as a simple, compute-efficient approach to elicit better visual reasoning performance from video models. Example videos on our project page at this https URL.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2607.25537 [cs.CV]

(or<br>arXiv:2607.25537v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.25537

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Robert Geirhos [view email]<br>[v1]<br>Tue, 28 Jul 2026 10:18:58 UTC (19,551 KB)

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