PaperBanana：為AI科學家自動化生成學術插圖

PaperBanana: Automating Academic Illustration for AI Scientists

January 30, 2026

作者: Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon

cs.AI

摘要

儘管基於語言模型的自動化AI科研工具快速發展，生成符合出版標準的插圖仍是研究流程中勞動密集的瓶頸環節。為解決此問題，我們提出PaperBanana——一個用於自動生成出版級學術插圖的智能體框架。該框架融合前沿視覺語言模型與圖像生成技術，通過協調專業智能體實現文獻檢索、內容風格規劃、圖像渲染及基於自我批判的迭代優化。為系統評估框架性能，我們構建了PaperBananaBench基準數據集，包含從NeurIPS 2025論文中精選的292個方法論圖示測試案例，涵蓋多元研究領域與插圖風格。綜合實驗表明，PaperBanana在準確性、簡潔性、可讀性與美學品質上均顯著優於主流基準方法。我們進一步驗證了該方法在高質量統計圖表生成任務中的擴展能力。總體而言，PaperBanana為實現出版級插圖的自動化生成開闢了新途徑。

English

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.