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README.md

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+---
+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/}
+}
+```