ChatGPT/Gemini can now draw on your screen to help you navigate complex software
A new framework called SketchVLM allows vision-language models to create editable SVG overlays on images to enhance user understanding. This approach improves visual reasoning task accuracy significantly and offers better sketch quality compared to traditional methods. The framework demonstrates strong performance in both single-turn and multi-turn generation, facilitating improved human-AI collaboration.
- ▪SketchVLM enables VLMs to produce non-destructive, editable SVG overlays on images.
- ▪The framework improves visual reasoning task accuracy by up to +28.5 points.
- ▪SketchVLM enhances sketch quality by up to +48.3% over existing baselines.
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Record
| Original publisher | Github |
| Canonical URL | https://sketchvlm.github.io/ |
| Publication time | Wed, 29 Apr 2026 04:17:49 +0000 |
| Retrieval time | 2026-04-29T05:01:00.886Z |
| Last seen | 2026-04-29T05:01:00.886Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1QqnYwLGAG9g |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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 typically respond with only 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 six 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 points and sketch quality by up to +48.3% over image-editing and fine-tuned…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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