PaperBanana: AI Now Generates Publication-Quality Academic Illustrations

PaperBanana Framework Overview

What's the most time-consuming part of writing a paper? Experiments? Writing? Many researchers point to "creating figures." Methodology diagrams, architecture schematics, result visualizations... each one can take hours or even days.

Researchers from Google and Peking University have released PaperBanana to solve this problem. It's an agentic system that automatically generates publication-ready academic illustrations from paper text alone.

Why Is This Needed?

We're in an era where LLMs help with paper writing, code generation, and experiment design. But figures? We still manually create them in PowerPoint, Figma, or TikZ.

• Half a day for one methodology diagram
• Start over when revision requests come in
• Maintaining consistent style is challenging

PaperBanana addresses this bottleneck.

How Does It Work?

Agent Pipeline Details

PaperBanana uses four specialized agents working in collaboration:

1. Reference Retrieval Agent

• Searches figure styles from similar papers
• Identifies visual conventions in the field
• Collects reference layout patterns

2. Content Planning Agent

• Extracts key concepts from paper text
• Decides which elements to include
• Designs information hierarchy

3. Image Rendering Agent

• Leverages Vision-Language Models
• Generates actual images
• Places text, arrows, boxes, etc.

4. Iterative Refinement Agent

• Self-evaluates generated figures
• Identifies areas for improvement
• Iterates until quality criteria are met

Performance: PaperBananaBench

Evaluation Results Comparison

The team created a benchmark with 292 test cases collected from NeurIPS 2025 papers.

Evaluation criteria:

• Faithfulness: Does it match the paper content?
• Conciseness: Is it free of unnecessary elements?
• Readability: Is it easy to understand?
• Aesthetics: Does it look visually appealing?

PaperBanana outperformed existing methods across all criteria.

Extended Capabilities

Generated Illustration Examples

Beyond methodology diagrams, it can also generate statistical plots:

• Experimental result graphs
• Comparison charts
• Data distribution visualizations

Limitations and Outlook

Current limitations:

• Complex 3D structures remain challenging
• Limited support for domain-specific notations
• Generation takes several minutes

However, it's significant as the final piece of AI research workflow automation. We're approaching an era where the entire paper writing process can be AI-assisted.

Resources

• Paper: PaperBanana: Automating Academic Illustration for AI Scientists
• Authors: Google Research, Peking University