Sivia: Learning to Draw, Learning to Learn

Abstract

A paper's overview figure is often the first thing readers see: it must explain the method correctly and look polished. We present Sivia, an overview-figure system that reads the paper, plans its relations, adapts visual organization from recent top-conference papers, and composes a design for image generation. Sivia improves with use through human-guided recursive self-improvement (RSI) at two levels: learning to draw revises its drawing skill from feedback on accuracy and presentation, and learning to learn revises the updater that proposes those revisions. A personal profile adapts presentation to its user's aesthetic preferences while the scientific content stays fixed. A diagnostic study of 1,964 overviews for 445 papers (17,973 relation judgments) shows that most errors are omissions and that source-derived relation plans raise required-relation accuracy (RRA) by up to 19.85 points. On 48 test papers, learning both levels reaches 93.54\% RRA, 15.31, 9.38, and 8.33 points above the initial skill, the Fixed-updater, and the GEPA baselines, while readability and visual quality rise from 3.95 and 4.00 to 4.70 and 4.78 on five-point scales. Learned presentation profiles raise liking from 4.02 to 4.73 and adoption from 34 to 45 of 48 tasks. Code and released artifacts are available at https://github.com/exsinger-hub/Sivia