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PaperBanana

PaperBanana automates academic illustration for AI researchers. It uses a team of specialized agents, powered by modern VLMs and image generators, to retrieve reference figures, plan content, design a consistent style, render diagrams or code-based plots, then self-critique and refine them. The authors introduce PaperBananaBench, a NeurIPS-derived benchmark with 292 test and 292 reference diagrams, and show that PaperBanana improves faithfulness, conciseness, readability and aesthetics versus baselines while also generating high-quality statistical plots and enhancing human-drawn figures.
New Multimodal Gen 3
Released: February 5, 2026

Overview

PaperBanana is an agentic framework that turns raw scientific content into publication-ready methodology diagrams and plots, orchestrating multiple AI agents plus a dedicated benchmark, PaperBananaBench.

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Last updated: February 5, 2026
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