AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations [ICLR 2026]

From Text to Publication-Ready Diagrams
AutoFigure is an intelligent system that leverages Large Language Models (LLMs) with iterative refinement to generate high-quality scientific figures from text descriptions or research papers.

Quick Start • Web Interface • Configuration • API Reference

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✨ Features

Feature	Description
📝 Text-to-Figure	Generate figures directly from natural language descriptions.
📄 Paper-to-Figure	Extract methodology from PDFs and create visual diagrams automatically.
🔄 Iterative Refinement	Dual-agent system (Generation + Evaluation) for continuous quality optimization.
🎨 Multiple Formats	Output as SVG or mxGraph XML (fully compatible with draw.io).
💅 Image Enhancement	Optional AI-powered post-processing for aesthetic beautification.
🖥️ Web Interface	Interactive Next.js frontend for easy generation and editing.

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🚀 How It Works

AutoFigure employs a Review-Refine loop to ensure high accuracy and aesthetic quality.

Process:

1. Generate: The agent creates initial SVG/XML based on description & references.
2. Evaluate: The critic scores quality (0-10) and provides specific feedback.
3. Refine: The loop continues until the figure meets publication standards.

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🌟 Generated Examples

Here are examples of figures generated by AutoFigure across different domains, showcasing its versatility in handling various levels of complexity.

Category & Visualization
📄 Paper Case
📊 Survey Case
📝 Blog Case
📘 Textbook Case

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⚡ Quick Start

Option 1: Python SDK (Recommended)

Installation

You can install from source or via pip options:

# Basic installation
pip install autofigure

# With PDF support (for Paper-to-Figure)
pip install autofigure[pdf]

# With image enhancement
pip install autofigure[enhancement]

# Full installation (Recommended)
pip install autofigure[full]

Or clone the repository:

git clone https://github.com/ResearAI/AutoFigure.git
cd AutoFigure
pip install -e .
playwright install chromium  # Required for rendering

1. Basic Usage (Text-to-Figure)

from autofigure import AutoFigureAgent, Config

# 1. Configure
config = Config(
    generation_api_key="your-api-key",
    generation_provider="openrouter",  # options: 'openrouter', 'gemini', 'bianxie'
    generation_model="google/gemini-2.5-pro",
)

# 2. Generate
agent = AutoFigureAgent(config)
result = agent.generate(
    description="A flowchart showing transformer training pipeline",
    max_iterations=5,
    output_format="svg",
    topic="paper" # 'paper', 'survey', 'blog', 'textbook'
)

print(f"✅ Generated: {result.svg_path} (Score: {result.final_score}/10)")

2. Generate from Paper (PDF/Markdown)

Extract methodology from a paper and generate a figure automatically.

# Generate figure from paper (PDF or Markdown)
result = agent.generate_from_paper(
    paper_path="./paper.pdf",
    max_iterations=5,
    output_format="svg",
    enable_enhancement=True, # Enhance the result
)

if result.success:
    print(f"Extracted methodology: {result.methodology_text[:200]}...")
    print(f"Generated figure: {result.svg_path}")

3. With Image Enhancement

Generate multiple enhanced aesthetic variants of the figure.

result = agent.generate(
    description="Neural network architecture diagram",
    enable_enhancement=True,
    enhancement_count=3,     # Generate 3 variants
    art_style="Modern scientific illustration with clean lines",
    enhancement_input_type="code2prompt" # Best quality mode
)

if result.success:
    print(f"Original Preview: {result.preview_path}")
    print(f"Enhanced variants: {result.enhanced_paths}")

Option 2: Web Interface

Ideally suited for visual interaction and editing.

./start.sh
# Then open http://localhost:6002 in your browser

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📊 FigureBench Dataset

We introduce FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form text.

Dataset Overview

Category	Samples	Avg. Tokens	Text Density	Complexity
📄 Paper	3,200	12,732	42.1%	High
📝 Blog	20	4,047	46.0%	Med
📊 Survey	40	2,179	43.8%	High
📘 Textbook	40	352	25.0%	Low
Total	3,300	10k+	41.2%	~5.3 Components

Download

from datasets import load_dataset
dataset = load_dataset("WestlakeNLP/FigureBench")

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⚙️ Configuration

AutoFigure is highly configurable. You can set these in Config() or via environment variables.

Supported LLM Providers

Provider	Base URL	Recommended Models
OpenRouter	openrouter.ai/api/v1	gemini-2.5-pro
Bianxie	api.bianxie.ai/v1	gemini-2.5-pro
Google	generativelanguage...	gemini-2.5-pro

Generation Settings

Option	Description	Default
generation_api_key	API key for figure generation	Required
generation_base_url	Base URL for API	Provider default
generation_model	Model name	Provider default
generation_provider	Provider: 'openrouter', 'bianxie', 'gemini'	'openrouter'

Methodology Extraction Settings

Option	Description	Default
methodology_api_key	API key for methodology extraction	Same as generation
methodology_model	Model for methodology extraction	Same as generation
methodology_provider	Provider for methodology extraction	Same as generation

Enhancement Settings

Option	Description	Default
enhancement_api_key	API key for image enhancement	None
enhancement_provider	Enhancement provider	'openrouter'
enhancement_model	Model for image enhancement	Provider default
enhancement_input_type	Input type: 'none', 'code', 'code2prompt'	'code2prompt'
enhancement_count	Number of enhanced variants to generate	1
art_style	Art style description for enhancement	''

Pipeline Settings

Option	Description	Default
max_iterations	Maximum refinement iterations	5
quality_threshold	Quality threshold (0-10)	9.0
output_dir	Output directory	'./autofigure_output'
custom_references	Custom reference figure paths	None

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📚 API Reference

generate() Parameters

Parameter	Description
description	Text description of the figure to generate
max_iterations	Maximum iterations (overrides config)
output_format	'svg' or 'mxgraphxml'
quality_threshold	Quality threshold (overrides config)
enable_enhancement	Whether to enhance the final image
art_style	Art style for enhancement (overrides config)
enhancement_input_type	'none', 'code', or 'code2prompt' (overrides config)
enhancement_count	Number of enhanced variants (overrides config)
topic	Content type: 'paper', 'survey', 'blog', 'textbook'
custom_references	Custom reference figure paths

generate_from_paper() Parameters

Accepts all parameters from generate() plus:

Parameter	Description
paper_path	Path to paper file (PDF or Markdown)
methodology_api_key	API key for extraction (overrides config)
methodology_provider	Provider for extraction (overrides config)

Result Object (GenerationResult)

Attribute	Description
success	Whether generation was successful
svg_path	Path to generated SVG file
mxgraph_path	Path to generated mxGraph XML file
preview_path	Path to PNG preview image
enhanced_paths	List of all enhanced image paths
final_score	Final quality score (0-10)
methodology_text	Extracted methodology (from paper)
error	Error message if failed

Enhancement Modes

Mode	Description
none	Direct beautification without code reference
code	Use generated code (SVG/XML) as reference
code2prompt	Use LLM to analyze code and generate detailed prompt (recommended)

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📁 Project Structure

Click to expand directory tree

AutoFigure/
├── autofigure/              # 📦 Python SDK
│   ├── agent.py             # Main Agent
│   ├── generator.py         # Generation Pipeline
│   ├── enhancer.py          # Image Enhancement
│   └── extractor.py         # PDF Method Extraction
├── frontend/                # 🖥️ Next.js Web UI
├── backend/                 # 🔌 Flask API Server
├── scripts/                 # 🛠️ Utility Scripts
└── pyproject.toml           # Config

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🤝 Community & Support

WeChat Discussion Group
Scan the QR code to join our community. If the code is expired, please add WeChat ID nauhcutnil or contact tuchuan@mail.hfut.edu.cn.

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📜 Citation & License

If you use AutoFigure or FigureBench in your research, please cite:

@inproceedings{
zhu2026autofigure,
title={AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations},
author={Minjun Zhu and Zhen Lin and Yixuan Weng and Panzhong Lu and Qiujie Xie and Yifan Wei and Sifan Liu and Qiyao Sun and Yue Zhang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=5N3z9JQJKq}
}