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English(EN) Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning

新框架使用TikZ代码合成科学图形,性能优于主要LLM

研究人员开发了一个新的框架,用于通过TikZ代码以编程方式合成科学图形。该框架包括SciTikZ-230K,一个专为可执行和视觉对齐的图像-TikZ对设计的大型数据集,以及SciTikZ-Bench,一个用于评估结构和视觉保真度的基准。还引入了一种新颖的双重自洽强化学习优化范式,以提高代码一致性并惩罚错误。由此产生的模型SciTikZer-8B展示了最先进的性能,超越了Gemini 2.5 Pro和Qwen3 VL 235B A22B Instruct等专有模型。 AI

影响 这项研究可能实现更自动化、更精确的科学可视化创建,从而可能改善科学交流和数据解释。

排序理由 该集群描述了一篇关于图形程序合成的新颖方法和数据集的新科学论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架使用TikZ代码合成科学图形,性能优于主要LLM

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该集群描述了一篇关于图形程序合成的新颖方法和数据集的新科学论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Juekai Lin, Yun Zhu, Honglin Lin, Sijing Li, Tianwei Lin, Zheng Liu, Xiaoyang Wang, Wenqiao Zhang, Lijun Wu ·

    基于双重自洽强化学习的科学图形程序合成

    arXiv:2604.06079v2 Announce Type: replace Abstract: 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 schem…