From Text to Figure: How to Use PaperBanana to Automate Your Methodology Diagrams

In the fast-paced world of AI research, autonomous agents are already helping us write code, conduct literature reviews, and even generate research hypotheses. However, one stubborn bottleneck remains: academic illustration. Crafting a methodology diagram that is both technically faithful and aesthetically pleasing usually takes hours of manual labor in tools like PowerPoint or Visio.

Enter PaperBanana, a novel agentic framework developed by researchers at Peking University and Google Cloud AI Research. It is specifically designed to transform raw scientific text into publication-ready illustrations. Here is a guide on how you can utilize this framework to automate your methodology diagrams.

Understanding the PaperBanana Framework

Unlike standard text-to-image models that often hallucinate connections or produce "messy" labels, PaperBanana uses a collaborative team of five specialized AI agents to ensure academic rigor :

1. Retriever Agent: Identifies relevant reference examples from high-quality publications to guide the structure and style.

2. Planner Agent: Acts as the "cognitive core," translating your complex methodology text into a structured visual plan.

3. Stylist Agent: Synthesizes academic aesthetic standards (color palettes, typography) from references to ensure your figure looks professional.

4. Visualizer Agent: The "artist" that renders the plan into a visual output using state-of-the-art models like Nano-Banana-Pro.

5. Critic Agent: Inspects the generated figure against your original text, providing feedback for iterative refinement through self-critique.

Step-by-Step: Generating Your Methodology Diagram

Step 1: Prepare Your Input Content

PaperBanana requires three primary inputs to build a faithful diagram :

• Source Context: The raw text of your methodology section. The framework is built to handle long-context modeling, with benchmark tests using an average of 3,020 words.

• Figure Caption: A concise title for the image (averaging around 70 words).

• Visual Intent: A brief description of what you want the diagram to emphasize (e.g., "Overview of our encoder-decoder architecture with sparse routing").

Step 2: Running the Framework

If you are using the community-driven implementation, you can generate a diagram using a simple CLI command :

paperbanana generate \
  --input path/to/your_methodology.txt \
  --caption "Your Figure Caption Here"

Step 3: The Iterative Refinement Phase

Once the Visualizer produces an initial draft, the Critic Agent takes over. It checks for "factual misalignments" or "visual glitches" (such as arrows pointing the wrong way). The system typically runs through three rounds of feedback () to refine the draft until it meets scholarly standards.

Why Use PaperBanana Instead of Generic AI?

Generic models like DALL-E 3 or standard GPT-Image-1.5 often struggle with the "box-and-arrow" logic of AI papers. According to the PaperBananaBench—a benchmark of 292 cases from NeurIPS 2025 publications—PaperBanana significantly outperforms leading baselines :

• Conciseness (+37.2%): It removes the "clutter" and focuses on the most important logical modules.

• Readability (+12.9%): It ensures fonts and layouts follow academic conventions.

• Faithfulness (+2.8%): It maintains a higher level of accuracy to the source text than non-agentic models.

Advanced Tips

• Aesthetic Enhancement: If you already have a hand-drawn sketch or a "ugly" draft, you can use PaperBanana to "polish" it. The framework can apply summarized aesthetic guidelines to improve your color schemes and typography automatically.

• Precision for Plots: While PaperBanana generates images for methodology diagrams, it uses a code-based paradigm (Matplotlib) for statistical plots to prevent "numerical hallucinations".