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arXiv · 2609.33386

SIVIA-RSI: Source-Grounded Adaptation of Diagramming Skills

Feng Yuan·Yifan Gao·Haoyue Li·Xin Gao
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Abstract

Scientific method diagrams express computations through entities, dependencies, and conditional routes. Although generated figures can be improved through repeated editing, it is less clear whether experience from one paper improves the first figure of another. We present \sys, a framework for source-grounded adaptation of reusable diagramming skills, and study transfer through a complete-candidate evaluation. The framework links critiques to source passages, proposes bounded edits to a persistent skill library, and separates candidate competition from skill acceptance. We evaluate the original skill and four learned candidates on two NLP and language-agent papers, with two fresh generations per condition. The strongest candidate attains 87.50\% required-relation accuracy compared with 83.33\% for the original, while candidate behavior differs across papers. Local improvements on a separate development paper and automatic selector preferences do not establish consistent transfer. Tracing all 22 non-correct relation judgments to their production prompts reveals both incomplete conditional specifications and ambiguities despite explicit instructions. All ten planning diagrams leave an already-terminal selected leaf's route unclear; none of their prompts explicitly binds that route. Our findings show why evaluating reusable diagram skills requires source-grounded relation assessment, complete candidate coverage, and inspection of both prompts and images. We provide all twenty transfer outputs, skill snapshots, assessment records, and executable analyses.

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Feng Yuan, Yifan Gao, Haoyue Li, Xin Gao. 2026-09-27. SIVIA-RSI: Source-Grounded Adaptation of Diagramming Skills. https://arxiv.org/abs/2609.33386

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