@inproceedings{collins-etal-2026-sketchvlm,
title = "{S}ketch{VLM}: Vision language models can annotate images to explain thoughts and guide users",
author = "Collins, Brandon and
Bolton, Logan and
Nguyen, Hung and
Taesiri, Mohammad Reza and
Bui, Trung and
Nguyen, Anh Totti",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2449/",
pages = "55534--55600",
ISBN = "979-8-89176-459-0",
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 can be found at https://sketchvlm.github.io/."
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<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 can be found at https://sketchvlm.github.io/.</abstract>
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%0 Conference Proceedings
%T SketchVLM: Vision language models can annotate images to explain thoughts and guide users
%A Collins, Brandon
%A Bolton, Logan
%A Nguyen, Hung
%A Taesiri, Mohammad Reza
%A Bui, Trung
%A Nguyen, Anh Totti
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F collins-etal-2026-sketchvlm
%X 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 can be found at https://sketchvlm.github.io/.
%U https://aclanthology.org/2026.emnlp-main.2449/
%P 55534-55600
Markdown (Informal)
[SketchVLM: Vision language models can annotate images to explain thoughts and guide users](https://aclanthology.org/2026.emnlp-main.2449/) (Collins et al., EMNLP 2026)
ACL