Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning
Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals into editable TikZ code. While TikZ is the de facto standard for scientific schematics due to its programmatic flexibility, its requirement for rigorous spatial precision presents a significant challenge for Multimodal Large Language Models. Progress is currently stifled by two primary gaps: (1) Data Quality Gap: existing image-TikZ corpora often lack strict executability and reliable visual alignment; (2) Evaluation Gap: a lack of benchmarks for both structural and visual fidelity. To address these, we present a closed-loop framework featuring...
| Authors | Juekai Lin, Yun Zhu, Honglin Lin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 April 2026 |
| Last updated at source | 28 August 2026 |
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