SketchVLM: Vision language models can annotate images to explain thoughts and guide users

• URL: http://arxiv.org/abs/2604.22875v2
• Date: Tue, 28 Apr 2026 04:48:22 GMT
• Title: SketchVLM: Vision language models can annotate images to explain thoughts and guide users
• Abstract summary: We present SketchVLM, a training-free, model-agnostic framework that enables vision-language models to produce non-destructive, editable overlays on the input image to visually explain their answers.<n>We find that single-turn generation already achieves strong accuracy and annotation quality, and multi-turn generation opens up further opportunities for human-AI collaboration.
• Score: 16.25722200375932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models (VLMs) such as Gemini-3-Pro and GPT-5 only respond with text, which can be difficult for users to verify. We present SketchVLM, a training-free, model-agnostic framework that enables VLMs to produce non-destructive, editable SVG overlays on the input image to visually explain their answers. Across seven benchmarks spanning visual reasoning (maze navigation, ball-drop trajectory prediction, and object counting) and drawing (part labeling, connecting-the-dots, and drawing shapes around objects), SketchVLM improves visual reasoning task accuracy by up to +28.5 percentage points and annotation quality by up to 1.48x relative to image-editing and fine-tuned sketching baselines, while also producing annotations that are more faithful to the model's stated answer. We find that single-turn generation already achieves strong accuracy and annotation quality, and multi-turn generation opens up further opportunities for human-AI collaboration. An interactive demo and code are at https://sketchvlm.github.io/.

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