Transform your research into publication-ready diagrams and statistical plots in minutes. Our 7-agent AI pipeline — Retriever, Planner, Stylist, Visualizer, Critic, Vanilla, and Polish — collaborates to produce figures that meet top-venue standards like NeurIPS, ICML, and ICLR.
PaperBanana Paper Visualizer is an AI-powered tool that converts research paper text and raw data into publication-ready scientific diagrams and statistical plots. Using a multi-agent pipeline with iterative AI critique, it generates methodology flowcharts, architecture diagrams, bar charts, scatter plots, and more — designed for researchers, PhD students, and academics who need professional figures for papers, theses, posters, and presentations.
Paste your paper text and caption in the left panel, then hit Generate. The pipeline will extract concepts, plan a diagram, generate and refine it through AI critique loops.
For diagrams: paste the methodology or algorithm section from your paper. For plots: paste your raw data in JSON or CSV format and describe the visualization you want.
Seven specialized agents collaborate: Retriever finds references from 200+ papers, Planner designs the layout, Stylist enhances academic aesthetics, Visualizer generates the figure, and Critic refines it through multi-round review.
Get your publication-ready diagram or plot in high resolution. Each generation includes iterative refinement — the AI critiques and improves the figure across multiple rounds.
Generate model architectures, system pipelines, encoder-decoder frameworks, algorithm flowcharts, and research framework overviews from text descriptions.
Create bar charts, line plots, scatter plots, heatmaps, and more from raw data. Powered by matplotlib with AI-generated code and automatic execution.
The Critic agent scores each generation and provides detailed feedback. The pipeline iterates until publication quality is reached, typically 2-3 rounds.
The Retriever agent searches 200+ academic papers to find similar figures, informing style and layout decisions for authentic academic aesthetics.
Every generation includes a free first draft. Purchase credits for multi-round AI critique refinement to achieve publication-quality results.
Diagram mode generates visual illustrations (like architecture diagrams, flowcharts, and pipeline visualizations) from text descriptions using AI image generation. Plot mode creates statistical charts (bar charts, line plots, scatter plots, etc.) from raw data by generating and executing matplotlib code.
The pipeline consists of seven specialized AI agents: Retriever finds reference figures from academic papers, Planner designs the layout, Stylist applies academic aesthetics, Visualizer generates the image or code, Critic reviews and suggests improvements, Vanilla provides a baseline version, and Polish applies final refinements. They work iteratively to maximize quality.
Most figures are generated within 2-5 minutes depending on the number of critic rounds. Each round involves multiple agents collaborating, which takes longer but produces significantly better results than single-pass generation.
Yes, all generated figures are yours to use in papers, theses, posters, presentations, and other academic publications. We recommend checking your target venue's AI usage policy. The figures are generated at 300 DPI for print-quality output.
Credits are charged upfront based on the number of critic rounds you select. If the pipeline completes in fewer rounds than requested, unused credits are automatically refunded. The first draft of every generation is free.
Plot mode accepts raw data in JSON format (e.g., {"Model": ["GPT-4", "Claude"], "Accuracy": [92.1, 89.5]}) or CSV format. You describe the visualization intent in natural language, and the AI generates the appropriate matplotlib code.