Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations. Powered by state-of-the-art VLMs and image generation models, PaperBanana orchestrates specialized agents to retrieve references, plan content and style, render images, and iteratively refine via self-critique. To rigorously evaluate our framework, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications, covering diverse research domains and illustration styles. Comprehensive experiments demonstrate that PaperBanana consistently outperforms leading baselines in faithfulness, conciseness, readability, and aesthetics. We further show that our method effectively extends to the generation of high-quality statistical plots. Collectively, PaperBanana paves the way for the automated generation of publication-ready illustrations.

翻译：尽管由语言模型驱动的自主AI科学家发展迅速，但在研究流程中，生成可直接用于发表的插图仍然是一个劳动密集型的瓶颈。为减轻这一负担，我们提出了PaperBanana，一个用于自动生成可直接发表的学术插图的智能体框架。该框架依托最先进的视觉语言模型和图像生成模型，通过协调多个专用智能体来完成参考文献检索、内容与风格规划、图像渲染以及基于自我批判的迭代优化。为严格评估我们的框架，我们构建了PaperBananaBench评测集，其中包含从NeurIPS 2025出版物中精心挑选的292个方法示意图测试用例，涵盖了不同的研究领域和插图风格。全面的实验表明，PaperBanana在忠实性、简洁性、可读性和美观性方面均持续优于主流基线方法。我们进一步证明，该方法能有效扩展至高质量统计图表的生成。总体而言，PaperBanana为自动化生成可直接发表的插图开辟了道路。