SciDiagramEdit - AutoFigure-Edit Aesthetic Win Rate: leaderboard
SciDiagramEdit paper v1 test split from 364 author-revision figure pairs. Semantic and aesthetic win rates use blind pairs against the author's revised figure, judged by Claude Opus 4.7. The train/validation/test split is 2:1:3. Native fractions are retained; superscript improvements are not the score. Single-pass code editing baseline; no Coach skill-evolution loop. Table 2 condition: AutoFigure-Edit. Checklist success is judged against atomic edit requirements. IAA and ISTA are UniPercept scores on a 0–100 scale; pairwise win rates and success are fractions. Metric: Rate (0–1).
Metric: Rate (0–1). Source: arxiv.org. Saturation forecast: Around March 2028. 3 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.5 | 0.4 | #31 |
| 2 | GPT-5.3 | 0.39 | #99 |
| 3 | GPT-5.4 | 0.32 | #84 |
No result here: #5 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=scidiagramedit-autofigure-edit-aesthetic-win-rate · How It Works · Data refreshed daily, snapshot 2026-10-26.