In this episode, we discuss PaperBanana: Automating Academic Illustration for AI Scientists by Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon. The paper presents PaperBanana, an autonomous framework that generates publication-ready academic illustrations using advanced vision-language and image generation models. It coordinates specialized agents to retrieve references, plan, render, and refine images through self-critique. Evaluated on a new benchmark from NeurIPS 2025 diagrams, PaperBanana outperforms existing methods in faithfulness, clarity, and aesthetics, and also effectively creates high-quality statistical plots.