🐜 Ant Stack: Modular Scientific Publication System

A comprehensive framework for reproducible scientific publications in embodied AI, featuring reusable analysis methods, automated validation, and professional presentation standards.

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📋 Table of Contents

• 🎯 Overview

• 🏗️ Architecture

• 📦 Core Package

• 📄 Paper Structure

• 🚀 Quick Start

• 🔧 Development

• 📚 Documentation

• 🤝 Contributing

• 📄 License

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🎯 Overview

Mission

Ant Stack provides a modular, reproducible framework for scientific publications in embodied AI, enabling researchers to:

• ✅ Reuse validated analysis methods across papers

• ✅ Ensure reproducible results through automated validation

• ✅ Generate publication-quality figures with consistent styling

• ✅ Maintain scientific rigor with statistical validation

• ✅ Scale research workflows with automated build pipelines

Key Features

Feature	Description
🔄 Reusability	Modular analysis methods for energy estimation, statistics, and visualization
📊 Quality Assurance	Automated validation, cross-reference checking, and statistical verification
🎨 Professional Output	Publication-ready figures, LaTeX integration, and consistent formatting
⚡ Performance	Optimized algorithms with comprehensive benchmarking
🔬 Scientific Rigor	Bootstrap confidence intervals, uncertainty quantification, reproducibility
🧪 Test-Driven	70%+ test coverage with comprehensive edge case testing

Applications

• 🤖 Embodied AI Research: Energy analysis for robotic systems

• 🧠 Neuroscience: Computational complexity of neural networks

• ⚡ Engineering: Power optimization and scaling analysis

• 📈 Data Science: Statistical validation and visualization

Current Status

✅ Fully Operational: All core systems working and validated

• 4 Papers: Successfully building with comprehensive validation
• 70%+ Test Coverage: Extensive test suite with edge cases
• Complete Documentation: Comprehensive guides and references
• Production Ready: Scientific validation and quality assurance

📊 Latest Results (from build_report.md):

• All Papers: ✅ SUCCESS (4/4 built successfully)
• All Tests: ✅ PASSED (11/11 tests passing)
• Zero Issues: No validation errors or broken references

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🏗️ Architecture

System Components

graph TB
    A[📦 Core Package] --> B[Analysis Methods]
    A --> C[Figure Generation]
    A --> D[Publishing Tools]

    B --> B1[Energy Estimation]
    B --> B2[Statistical Validation]
    B --> B3[Workload Modeling]

    C --> C1[Publication Plots]
    C --> C2[Mermaid Processing]
    C --> C3[Cross-References]

    D --> D1[PDF Generation]
    D --> D2[Quality Validation]
    D --> D3[Template System]

    E[📄 Paper Structure] --> F[Ant Stack Framework]
    E --> G[Complexity Analysis]

    H[🔧 Build Pipeline] --> I[Unified Validation]
    H --> J[Quality Assurance]
    H --> K[Automated Testing]

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📦 Core Package (antstack_core/)

Analysis Module (analysis/)

Component	Purpose	Key Features
energy.py	Energy estimation and analysis	Physical modeling, efficiency calculations
statistics.py	Statistical methods and validation	Bootstrap CI, scaling relationships
workloads.py	Computational workload modeling	Body/brain/mind workload patterns
scaling_analysis.py	Scaling relationship analysis	Power laws, regime detection
enhanced_estimators.py	Advanced energy estimation	Multi-scale analysis, theoretical limits
experiment_config.py	Experiment configuration	YAML/JSON management, validation

Figures Module (figures/)

Component	Purpose	Key Features
plots.py	Publication-quality plotting	Matplotlib integration, styling
mermaid.py	Diagram preprocessing	Mermaid to PNG conversion
references.py	Cross-reference validation	Figure/table reference checking
assets.py	Asset management	File organization, optimization

Publishing Module (publishing/)

Component	Purpose	Key Features
pdf_generation.py	PDF generation utilities	Pandoc integration, LaTeX processing
templates.py	Document templates	Consistent formatting, styling
validation.py	Quality assurance	Automated checking, error detection

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📄 Paper Structure (papers/)

Ant Stack Framework (papers/ant_stack/)

Focus: Biological framework for collective intelligence

Section	File	Purpose
📖 Introduction	Background.md	Theoretical foundation
🦿 Body Layer	AntBody.md	Locomotion and sensing
🧠 Brain Layer	AntBrain.md	Neural processing and learning
💭 Mind Layer	AntMind.md	Decision making and planning
🔧 Methods	Methods.md	Implementation details
📊 Results	Results.md	Experimental validation
💡 Applications	Applications.md	Real-world use cases
🗣️ Discussion	Discussion.md	Implications and future work

Complexity Analysis (papers/complexity_energetics/)

Focus: Computational complexity and energy scaling

Section	File	Purpose
📖 Introduction	Background.md	Problem statement
🔬 Theory	Complexity.md	Complexity analysis framework
	Energetics.md	Energy modeling approach
	Scaling.md	Scaling relationship theory
🛠️ Methods	Methods.md	Analysis methodology
📊 Results	Generated.md	Auto-generated analysis results
	Results.md	Interpretation and validation
🗣️ Discussion	Discussion.md	Scientific implications

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🚀 Quick Start

Prerequisites

System Requirements:

• Python 3.8+

• Node.js 14+

• LaTeX distribution

• Pandoc 2.10+

Installation

Ubuntu/Debian

# System dependencies
sudo apt-get update
sudo apt-get install -y pandoc texlive-xetex texlive-fonts-recommended fonts-dejavu nodejs npm

# Enhanced diagram rendering
sudo npm install -g mermaid-filter

# Python dependencies
pip3 install matplotlib numpy pandas pyyaml pytest scipy

macOS

# System dependencies
brew install pandoc node python3
brew install --cask mactex-no-gui

# Enhanced diagram rendering
npm install -g mermaid-filter

# Python dependencies
pip3 install matplotlib numpy pandas pyyaml pytest scipy

Development Setup

# Clone repository
git clone https://github.com/docxology/ant_stack.git
cd ant

# Install in development mode
pip install -e .

# Run tests
python -m pytest

# Build documentation
python scripts/build_docs.py

Build Papers

Single Paper

# Ant Stack framework paper
python3 scripts/common_pipeline/build_core.py --paper ant_stack

# Complexity analysis paper
python3 scripts/common_pipeline/build_core.py --paper complexity_energetics

All Papers

# Build all papers
python3 scripts/common_pipeline/build_core.py

# With validation only
python3 scripts/common_pipeline/build_core.py --validate-only

Basic Usage

from antstack_core.analysis.energy import EnergyCoefficients, estimate_detailed_energy
from antstack_core.analysis.statistics import bootstrap_mean_ci

# Energy analysis example
coeffs = EnergyCoefficients()
workload = ComputeLoad(flops=1e9, memory_bytes=1e6)
energy = estimate_detailed_energy(workload, coeffs)

# Statistical validation
data = [1.2, 1.5, 1.3, 1.8, 1.4]
mean, ci_lower, ci_upper = bootstrap_mean_ci(data, n_bootstrap=1000)

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🔧 Development

Testing Strategy

Test Coverage Goals:

• Core modules: 80%+ coverage

• Analysis methods: 90%+ coverage

• Edge cases: Comprehensive coverage

• Integration tests: End-to-end validation

Running Tests:

# All tests
python -m pytest

# With coverage report
python -m pytest --cov=antstack_core --cov-report=html

# Specific module
python -m pytest tests/antstack_core/test_energy.py -v

# Performance benchmarks
python -m pytest tests/ --benchmark-only

Code Quality Standards

Linting and Formatting:

# Run linters
python -m flake8 antstack_core/
python -m black antstack_core/
python -m isort antstack_core/

# Type checking
python -m mypy antstack_core/

Pre-commit Hooks:

# Install hooks
pre-commit install

# Run manually
pre-commit run --all-files

Documentation

Building Docs:

# Generate API documentation
python scripts/generate_docs.py

# Build user guide
python scripts/build_user_guide.py

# Deploy to GitHub Pages
python scripts/deploy_docs.py

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📚 Documentation

User Guides

• Getting Started: Installation and basic usage

• API Reference: Complete method documentation

• Best Practices: Development guidelines

• Troubleshooting: Common issues and solutions

Scientific Documentation

• Theoretical Foundation: Mathematical underpinnings

• Validation Framework: Quality assurance methods

• Benchmarking: Performance analysis

• Reproducibility: Ensuring scientific validity

Developer Resources

• Contributing Guide: Development workflow and standards

• Workflow Guides: Complete workflow documentation

• Configuration Summary: Configuration management

• PDF Rendering Guide: Publication system details

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Write tests for new functionality
4. Implement your changes
5. Run tests: python -m pytest
6. Update documentation if needed
7. Submit a pull request

Code Review Process

• All PRs require review

• Tests must pass CI pipeline

• Documentation updates required for API changes

• Maintain backward compatibility

Issue Reporting

• Use GitHub Issues for bug reports

• Provide minimal reproducible examples

• Include system information and error traces

• Follow issue templates for consistency

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Scientific Contributors: Domain experts in embodied AI and computational neuroscience

• Open Source Community: Libraries and tools that power this framework

• Research Institutions: Partners supporting reproducible science initiatives

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📞 Contact

• Issues: GitHub Issues

• Discussions: GitHub Discussions

• Email: daniel@activeinference.institute

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Built with ❤️ for reproducible science in embodied AI