Academic Diagram Generation

Generate NeurIPS-quality methodology diagrams or statistical plots from paper text.

Available Tools

Tool	Purpose
plan_diagram	Primary tool. Retrieves reference examples, loads their images, generates a detailed description via VLM with visual in-context learning, then refines it with style guidelines. Returns an optimized description.
generate_image	Render a diagram or plot from a description
critique_image	Evaluate and get revision feedback on a generated image
search_references	(Optional) Browse reference diagrams independently

Workflow

Follow these steps in order:

Step 1 — Plan (Retrieval + Description + Styling)

Call plan_diagram with the paper's methodology text, figure caption, and diagram type. This single tool call:

1. Retrieves the top-10 most relevant reference examples from the curated set
2. Loads their images and passes them to the VLM for visual in-context learning
3. Generates a comprehensive textual description using a dedicated planner prompt
4. Refines the description with NeurIPS-quality aesthetic guidelines via a stylist prompt

You receive back an optimized, publication-ready description.

Do NOT attempt to write the diagram description yourself. The plan_diagram tool produces significantly better descriptions because it uses reference images and dedicated prompts.

Step 2 — Generate

Call generate_image with the description returned by plan_diagram.

• For methodology diagrams: diagram_type = "methodology" (default)
• For statistical plots: diagram_type = "statistical_plot" and include raw_data

Step 3 — Critique & Refine (max 3 rounds)

Call critique_image with the generated image, the description, source text, and caption.

• If needs_revision is true: use the revised_description from the critique, then go back to Step 2.
• If needs_revision is false: the image is publication-ready. Done.

Repeat at most 3 total iterations.

Example Interaction

User: "Generate a methodology diagram for this paper: [text]"

Agent steps:
  1. plan_diagram(source_context=..., caption=..., diagram_type="methodology")
     → receives optimized description
  2. generate_image(description=<optimized_description>, diagram_type="methodology")
     → receives image path
  3. critique_image(image_path=..., description=..., source_context=..., caption=...)
     → if needs_revision: update description → generate_image again
  4. Reply with the final image path

Important Notes

• Always call plan_diagram first — it handles retrieval, planning, and styling in one step with visual in-context learning from real reference diagrams.
• Never use hex codes, pixel dimensions, or CSS values in descriptions — they render as garbled text in generated images.
• Never fall back to matplotlib or LaTeX for methodology diagrams — always use the image generation model via generate_image.
• For statistical plots, generate_image will automatically generate and execute matplotlib code.