PaperBanana: Automating Academic Illustration for AI Scientists
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.
698024266676f933227065e5
Dawei Zhu
698024266676f933227065e6
Rui Meng
698024266676f933227065e7
Yale Song
698024266676f933227065e8
Xiyu Wei
claimed_verified
2026-02-03T10:08:48.856Z
66e01e9a0447b0c09906a84a
/avatars/5b30840a17d61943f714fc7d6be45ece.svg
Xiyu
Lemon-prog
user
Lemon-prog
698024266676f933227065e9
Sujian Li
698024266676f933227065ea
Tomas Pfister
698024266676f933227065eb
Jinsung Yoon
PaperBanana: Automating Academic Illustration for AI Scientists
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.
_paperbanana is an agentic framework that automates the creation of publication-ready academic illustrations using advanced vision-language models and image generation techniques.
VLMs image generation models agentic framework publication-ready illustrations methodology diagrams PaperBananaBench self-critique statistical plots