SciDiagramEdit - Image Generation Image Aesthetics: 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. Direct raster image generation; no code editor or skill evolution. Table 2 condition: Image Generation. 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: UniPercept score (0–100).

Metric: UniPercept score (0–100). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 2 models tracked.

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Interactive version: theaggregate.ai/benchmark?slug=scidiagramedit-image-generation-image-aesthetics · How It Works · Data refreshed daily, snapshot 2026-10-26.