---
title: "Visual Prompt Engineering: A Simple Way to Make Video Models Smarter"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.25537v1"
permalink: "https://feed.astralor.com/items/itm_f5f6c609ed2ea06370af64ec"
published: "2026-07-28T10:18:58.000Z"
score: 81
channel: "tech"
tags: ["arXiv","Foundation Models","Prompt Engineering","Visual Prompt Engineering","Video Models"]
---

# Visual Prompt Engineering: A Simple Way to Make Video Models Smarter

## AI commentary

> This paper caught my eye: prompt engineering can also work on video model image inputs, and with such significant effects. If you're a researcher in video understanding or visual reasoning, or if you're optimizing multimodal model applications, this paper is worth reading—it might change how you approach input data.

## At a glance

This study introduces Visual Prompt Engineering (VIPE), which automatically modifies task images to enhance video models' visual reasoning capabilities. Experiments show that operations like converting abstract sketches into photorealistic scenes significantly improve performance on tasks such as physics reasoning, even outperforming traditional text-based prompt engineering and test-time scaling. VIPE offers a compute-efficient alternative, akin to how text prompts boost language models.

## Key points

- Visual Prompt Engineering (VIPE) automatically optimizes input images via image editing tools to improve video model reasoning performance.
- Converting abstract sketches into photorealistic scenes yields significant gains in physics reasoning tasks.
- VIPE is more effective than traditional text prompts and test-time scaling, with lower computational cost.
- It provides a unified framework analogous to text prompt engineering for language models.

## Full text

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 https://visual-prompt-engineering.github.io/.

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