| --- |
| pretty_name: Scry Design Diff Eval |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-text |
| - object-detection |
| tags: |
| - ui |
| - mobile |
| - visual-regression |
| - benchmark |
| - vlm |
| - screenshots |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test-* |
| --- |
| |
| # Scry Design Diff Eval |
|
|
| **Measuring VLMs as Mobile UI Regression Reviewers** |
|
|
| Scry Design Diff Eval is a benchmark built to evaluate vision-language models on |
| mobile UI diff review. Each example pairs a reference mobile screenshot |
| (`image_a`) with a generated implementation screenshot (`image_b`) and carries |
| human-drawn selection boxes plus explicit defect tags. A model must return a |
| structured list of UI defects — tags and normalized boxes — not a prose |
| description. |
|
|
| 📄 **Paper**: https://blog.scrymore.com/ |
|
|
| ## Dataset composition |
|
|
| | Quantity | Count | |
| |---|---:| |
| | Eval pairs | 311 | |
| | Visual-diff pairs | 234 | |
| | No-tagged controls | 77 | |
| | Counted tagged issues | 557 | |
|
|
| Issue-density buckets (the `split` column): |
|
|
| | Bucket | Definition | Pairs | |
| |---|---|---:| |
| | `single_issue` | 1 tagged issue | 107 | |
| | `multi_issue` | 2-3 tagged issues | 80 | |
| | `dense_issue` | 4 or more tagged issues | 47 | |
| | `no_diff` | 0 scored tagged issues | 77 | |
|
|
| The most common defect tags are `Icon/Nav`, `Color/Background`, |
| `Spacing/Layout`, `Shape/Size`, `Missing Content`, and `Typography`. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `id` | string | Pair id, e.g. `amazon-shopping__801` | |
| | `app_name` | string | Source app and capture batch | |
| | `app_slug` | string | Normalized app identifier | |
| | `screen_index` | int | Screen number within the app capture | |
| | `split` | string | Issue-density bucket (see above) | |
| | `task_type` | string | `visual_diff` or `no_diff` | |
| | `n_issues` | int | Number of scored tagged issues | |
| | `issue_labels` | list[string] | Union of defect tags on this pair | |
| | `ground_truth_issues` | string | JSON array of issues: `{issue_id, labels, box_a, box_b, note?, created_at}` with boxes normalized to `{x, y, w, h}` in [0, 1] | |
| | `image_a` | image | Reference screenshot | |
| | `image_b` | image | Generated implementation screenshot | |
|
|
| Parse `ground_truth_issues` with `json.loads`. A box may be present on side A, |
| side B, or both. |
|
|
| ## Task |
|
|
| Given `image_a` and `image_b`, return: |
|
|
| ```json |
| { |
| "issues": [ |
| { |
| "labels": ["Icon/Nav"], |
| "note": "The bottom navigation icon differs from the reference.", |
| "box_a": {"x": 0.10, "y": 0.90, "w": 0.12, "h": 0.07}, |
| "box_b": {"x": 0.10, "y": 0.90, "w": 0.12, "h": 0.07}, |
| "confidence": 0.80 |
| } |
| ] |
| } |
| ``` |
|
|
| ## Scoring |
|
|
| The primary metric is **known-issue recall**. A model issue matches a human |
| issue when they share at least one explicit defect tag AND the model box |
| overlaps the human box on the same image side with IoU >= 0.10. Matching is |
| one-to-one. The judge is deterministic — no LLM judging. |
|
|
| The protocol is recall-first because human annotations are known positives, not |
| exhaustive negatives: extra model findings may be valid and are reported |
| diagnostically (precision, no-tagged flag rate) rather than reducing the |
| primary score. |
|
|
| ## Baseline results (full 311-pair set) |
|
|
| | Model | Known-Issue Recall | Diagnostic Precision | Issue F1 | |
| |---|---:|---:|---:| |
| | Kimi K2.7 Code + Together recovery | 38.2% | 15.9% | 22.5% | |
| | Gemini 3.5 Flash | 37.5% | 20.2% | 26.3% | |
| | Codex GPT-5.5 xhigh | 37.3% | 13.6% | 19.9% | |
| | MiniMax M3 | 21.9% | 11.5% | 15.0% | |
| | Gemma 4 26B A4B | 20.3% | 12.4% | 15.4% | |
| | Gemma 4 31B | 17.8% | 13.3% | 15.2% | |
|
|
| See the paper for pilot results, density and category breakdowns, and static |
| controls. |
|
|
| ## Caveats |
|
|
| - **No-tagged controls are a proxy, not a guarantee**: pairs with zero scored |
| tagged issues are used as controls but were not exhaustively audited as |
| defect-free. |
| - **Annotations are known positives**: a model can find a real defect the |
| annotators did not tag. Treat precision and no-diff specificity as |
| operational diagnostics. |
| - **Screenshots**: reference images are captures of real mobile apps, included |
| for research and evaluation purposes; all app content remains the property |
| of its respective owners. The annotations (boxes, tags, metadata) are |
| released under CC BY 4.0. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{scrydesigndiffeval2026, |
| title={Scry Design Diff Eval: Measuring VLMs as Mobile UI Regression Reviewers}, |
| author={Pinnock, Ejiro}, |
| year={2026}, |
| url={https://blog.scrymore.com/} |
| } |
| ``` |
|
|
