epinnock/scry-design-diff-eval

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:

{
  "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

@misc{scrydesigndiffeval2026,
  title={Scry Design Diff Eval: Measuring VLMs as Mobile UI Regression Reviewers},
  author={Pinnock, Ejiro},
  year={2026},
  url={https://blog.scrymore.com/}
}