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Contributing to Monad-Loop Network

Thank you for your interest in contributing to MLN! This project explores fundamental questions about intelligence, consciousness, and knowledge representation.

Philosophy

This is a research project exploring alternatives to statistical AI. Contributions should align with the core principles:

  1. Structural over statistical: Favor explicit structure over implicit correlations
  2. Explainable by design: Every inference should be traceable
  3. Self-reference: Systems should reason about their own reasoning
  4. Compositionality: Complex concepts built from simpler parts

How to Contribute

Reporting Issues

  • Bugs: Describe the problem, expected behavior, and steps to reproduce
  • Philosophical questions: Open discussions about the theoretical foundations
  • Feature requests: Explain the use case and how it aligns with MLN's philosophy

Code Contributions

Setup Development Environment

git clone https://github.com/yourusername/monad-loop-network.git
cd monad-loop-network
pip install -r requirements.txt
pip install -e .  # Install in development mode

Running Tests

# Run all tests
python tests/test_mln.py

# Or use pytest
pytest tests/

# With coverage
pytest --cov=src tests/

Code Style

  • Follow PEP 8
  • Use type hints where appropriate
  • Write docstrings for public methods
  • Keep functions focused and composable
def example_function(param: str) -> Dict:
    """
    Brief description.
    
    Parameters:
    - param (str): Description
    
    Returns:
    - Dict: Description
    """
    pass

Commit Messages

Use clear, descriptive commit messages:

Add analogical reasoning engine

Implements structural isomorphism matching between source and target
domains, enabling cross-domain inference.

Relates to issue #42

Areas for Contribution

1. Core System Enhancements

  • Improve relation inference: Better algorithms for pre-established harmony
  • Optimize graph traversal: Faster inference chain discovery
  • Add inference rules: New Chomsky-style transformations

2. Philosophical Extensions

  • Analogical reasoning: Hofstadter's Fluid Concepts approach
  • Abductive learning: Form new concepts from examples
  • Consciousness metrics: Measure strange loop complexity

3. Integration

  • LLM integration: Combine with GPT/Claude for perception
  • Knowledge import: Load from existing ontologies (DBpedia, ConceptNet)
  • Visualization: Graph visualization tools

4. Documentation

  • Tutorials: Step-by-step guides for specific use cases
  • Philosophical deep-dives: Explore connections to other thinkers
  • API examples: More usage patterns

5. Testing

  • Unit tests: Test individual components
  • Integration tests: Test end-to-end scenarios
  • Property-based tests: Test invariants with hypothesis

Pull Request Process

  1. Fork the repository
  2. Create a branch: git checkout -b feature/your-feature-name
  3. Make changes: Write code and tests
  4. Test locally: Ensure all tests pass
  5. Commit: Use clear commit messages
  6. Push: git push origin feature/your-feature-name
  7. Open PR: Describe your changes and motivation

PR Checklist

  • Tests added/updated
  • Documentation updated
  • Code follows style guidelines
  • All tests pass
  • No merge conflicts
  • Philosophical alignment with MLN principles

Code Review

Expect feedback on:

  • Correctness: Does it work as intended?
  • Philosophy: Does it align with MLN's principles?
  • Clarity: Is the code understandable?
  • Testing: Are there adequate tests?

Research Directions

Interesting open problems:

1. Scaling Pre-established Harmony

Current O(n) complexity for adding concepts. Can we:

  • Use approximate structural similarity?
  • Lazy relation establishment?
  • Distributed knowledge graphs?

2. Learning Deep Structures

How can systems learn deep structures from examples?

  • Inverse transformations (surface → deep)
  • Structural abstraction
  • Concept composition

3. Consciousness Metrics

How do we measure "consciousness" or "understanding"?

  • Strange loop depth
  • Integration (IIT's Φ)
  • Causal density

4. Hybrid Architectures

What's the optimal LLM + symbolic integration?

  • LLM for perception, symbolic for reasoning?
  • Bidirectional information flow?
  • Meta-learning for architecture selection?

Discussion

Join the conversation:

  • Issues: Philosophical questions, feature requests
  • Discussions: Deeper explorations of theoretical foundations
  • PRs: Code review and collaboration

Code of Conduct

Be respectful and constructive:

  • Focus on ideas, not people
  • Welcome diverse perspectives
  • Assume good faith
  • Help newcomers

Recognition

Contributors will be acknowledged in:

  • README.md
  • Release notes
  • Research papers (if applicable)

License

By contributing, you agree that your contributions will be licensed under the MIT License.

Questions?

Open an issue or start a discussion. We're here to explore these ideas together!

"The best way to have a good idea is to have lots of ideas." — Linus Pauling