SIVIA-RSI: Source-Grounded Adaptation of Diagramming Skills

2026-09-27

AI Summary

This study addresses the challenge of transferring figure-generation expertise from a single paper to the first figure of a new paper in scientific chart generation. To this end, it proposes the SIVIA-RSI framework, which enables effective cross-paper reuse of plotting skills through source-text anchoring. Methodologically, this work pioneers linking critique feedback to source paragraphs, constructing a persistent skill library with constrained editing, and decoupling candidate competition from skill acceptance mechanisms. The evaluation integrates comprehensive candidate assessment, source-grounding analysis, and automated preference selection. Experimental results demonstrate that the optimal candidate achieves an accuracy of 87.5%, outperforming the baseline at 83.3%. Furthermore, the findings reveal the limitation that local improvements alone cannot guarantee consistent transferability across papers.

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.

Problem

• diagram generation
• skill transfer
• reusable skills
• source-grounded evaluation
• scientific diagrams

Innovation

• Source-Grounded Adaptation
• Reusable Diagramming Skills
• Persistent Skill Library
• Complete-Candidate Evaluation
• Transfer Learning