PaperBanana
Automating Academic Illustration for AI Scientists
An agentic framework that orchestrates five specialized AI agents to transform raw scientific content into publication-ready methodology diagrams and statistical plots — no design skills required.
Dawei Zhu· PKU*Rui Meng· GoogleYale Song· GoogleXiyu Wei· PKUSujian Li· PKUTomas Pfister· GoogleJinsung Yoon· Google
🍌 Create Illustration arXiv Paper View Code
292
Test Cases
from NeurIPS 2025
5
Agents
specialized AI agents
4/4
Benchmarks Won
faithfulness, conciseness, readability, aesthetics
Abstract
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.
VLMs
State-of-the-art vision-language models
Multi-Agent
5 specialized collaborative agents
Self-Critique
Iterative quality refinement loop
NeurIPS 2025
Benchmark from top venue
Multi-Agent Architecture
Five Agents, One Mission
Each agent specializes in a critical stage of the illustration pipeline, working in concert to deliver publication-quality results.
🔍 Retriever
Identifies relevant reference examples from academic databases to guide style and content alignment.
→
📐 Planner
Translates scientific content into a detailed visual plan, decomposing structure and layout.
→
🎨 Stylist
Enforces academic aesthetic standards — color palettes, typography, line weights, and visual hierarchy.
→
🖼️ Visualizer
Renders the initial image or generates Python/Matplotlib plotting code from the visual plan.
→
🔬 Critic
Performs iterative self-critique, inspecting generated results against source content and triggering refinement.
Generated Illustrations
MethodologyStatistical Plot
InputLLM CorePlannerStylistOutputself-critique loopMulti-Agent Pipeline
✓Generated by PaperBanana · Multi-agent pipeline with iterative Critic refinement
Capabilities
What PaperBanana Can Do
🗂️ NeurIPS-ready
Methodology Diagrams
Neural networks, flowcharts, multi-agent pipelines, and complex system architectures — all rendered to publication standards.
📊 Data-exact
Statistical Plots
Accurate data visualization via Matplotlib code generation. Bar charts, ablation studies, accuracy comparisons — zero hallucination guaranteed.
✏️ Sketch input
Sketch-to-Pro
Transform rough hand-drawn sketches into clean, harmonious academic figures with consistent fonts and styling.
✨ Polish mode
Aesthetic Refinement
Upload existing diagrams to upgrade fonts, colors, and spacing without altering underlying content or structure.
📚 Context-aware
Reference-Driven
Retrieves relevant papers to align style with academic conventions — your figures will match the venue aesthetic.
🔄 Auto-refine
Iterative Refinement
Self-critique loop ensures publication-quality output. The Critic agent reviews and forces regeneration until quality passes.
PaperBananaBench
Consistently Outperforms All Baselines
Evaluated on 292 test cases from NeurIPS 2025, PaperBanana leads across all four dimensions: faithfulness to source content, conciseness, readability, and visual aesthetics.
Faithfulness
Baseline: 61%PaperBanana: 87%
Conciseness
Baseline: 58%PaperBanana: 82%
Readability
Baseline: 70%PaperBanana: 91%
Aesthetics
Baseline: 63%PaperBanana: 85%
PaperBananaBench
292
Test Cases
NeurIPS
Source Venue
2025
Publications
4 Dims
Evaluation Axes
The benchmark covers diverse research domains and illustration styles, representing the breadth of modern AI research publications.
How It Works
From Text to Publication Figure in Seconds
Describe your methodology or paste your data. PaperBanana's agents collaborate to deliver a figure that passes peer review.
STEP 01
✍️ Describe Input your methodology, data, or sketch. PaperBanana accepts text, PDFs, or images.
STEP 02
🔍 Retrieve The Retriever agent scans reference databases to find style-aligned academic examples.
STEP 03
📐 Plan & Style Planner and Stylist agents create a detailed visual plan with academic-grade aesthetics.
STEP 04
✨ Render & Refine Visualizer renders the figure; Critic reviews and iterates until quality is publication-ready.
Citation
Cite This Work
@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}
}
🏛️ Peking University
☁️ Google Cloud AI Research
Corresponding authors: dwzhu@pku.edu.cn · lisujian@pku.edu.cn · jinsungyoon@google.com
🍌 PaperBanana
arXiv:2601.23265 · Peking University × Google Cloud AI