PaperBanana

Key Features & Capabilities
1. Methodology Diagram Generation
Automates the creation of complex schemas for AI models, frameworks, and workflows. Unlike generic image models, PaperBanana focuses on visual abstraction, preventing the "box-ified" copy-pasting of text and ensuring structural coherence.

2. High-Precision Statistical Plots
For data visualization, PaperBanana generates executable Python code rather than raw pixels. This approach eliminates "numerical hallucinations"—where data points or bars are drawn at inaccurate heights—preserving the scientific integrity required for formal publication.

3. Aesthetic Enhancement of Sketches
The framework can elevate existing human-drawn sketches or whiteboard notes. By applying auto-summarized style guidelines, it polishes color schemes, typography, and graphical elements into professional-grade PDF illustrations.

4. Benchmarked Performance (PaperBananaBench)
Evaluated against 292 test cases from NeurIPS 2025, PaperBanana significantly outperforms leading non-agentic baselines (like GPT-4o and vanilla Nano-Banana-Pro) :

Conciseness: +37.2% improvement.

Readability: +12.9% improvement.

Overall Score: +17.0% improvement.

Intended Use & Integration
Publication-Ready Figures: Directly generating methodology figures for conference submissions.

Automated Research Dissemination: Creating visual summaries for technical blogs, posters, and presentation slides.

IDE Extensions: Integration with JupyterLab or VS Code for automated chart creation from CSV/JSON data.