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.
Each agent specializes in a critical stage of the illustration pipeline, working in concert to deliver publication-quality results.
Neural networks, flowcharts, multi-agent pipelines, and complex system architectures — all rendered to publication standards.
Accurate data visualization via Matplotlib code generation. Bar charts, ablation studies, accuracy comparisons — zero hallucination guaranteed.
Transform rough hand-drawn sketches into clean, harmonious academic figures with consistent fonts and styling.
Upload existing diagrams to upgrade fonts, colors, and spacing without altering underlying content or structure.
Retrieves relevant papers to align style with academic conventions — your figures will match the venue aesthetic.
Self-critique loop ensures publication-quality output. The Critic agent reviews and forces regeneration until quality passes.
Evaluated on 292 test cases from NeurIPS 2025, PaperBanana leads across all four dimensions: faithfulness to source content, conciseness, readability, and visual aesthetics.
The benchmark covers diverse research domains and illustration styles, representing the breadth of modern AI research publications.
@article{zhu2026paperbanana,
title={PaperBanana: Automating Academic
Illustration for AI Scientists},
author={Zhu, Dawei and Meng, Rui and
Song, Yale and Wei, Xiyu and
Li, Sujian and Pfister, Tomas and
Yoon, Jinsung},
journal={arXiv preprint arXiv:2601.23265},
year={2026}
}