Monad-Loop Network (MLN)

A self-referential knowledge system combining Gödel-Escher-Bach's strange loops, Chomsky's universal grammar, and Leibniz's monads for structural, explainable AI.

🧠 Philosophy

Current LLMs are statistical pattern matchers—they correlate tokens without genuine understanding. MLN represents a different paradigm:

• Structural Knowledge: Concepts have operational semantics, not just vector embeddings
• Explainable Reasoning: Complete inference chains, not black-box predictions
• Self-Reference: Systems that can reason about their own reasoning (meta-cognition)
• Compositionality: Deep structures transform into multiple surface realizations

🎯 Key Concepts

1. Monadic Knowledge Units (Leibniz)

Self-contained concepts that "reflect the universe" from their perspective. Each monad:

• Contains deep structure (meaning)
• Establishes relations automatically (pre-established harmony)
• Has operational semantics (can execute transformations)

2. Deep Structure ↔ Surface Structure (Chomsky)

Meaning exists at the deep level. Multiple surface forms (text, code, logic) are isomorphic projections:

Deep Structure: IS_A(dog, mammal)
  ↓
Surface Forms:
  - Text:  "A dog is a mammal"
  - Logic: ∀x: dog(x) → mammal(x)
  - Code:  class Dog(Mammal): pass

3. Strange Loops (Gödel-Escher-Bach)

Self-referential systems create consciousness and meaning. MLN implements:

• Meta-knowledge graphs (system models itself)
• Introspection (examine own reasoning)
• Gödel sentences (expose system limits)

🚀 Quick Start

Installation

git clone https://github.com/yourusername/monad-loop-network.git
cd monad-loop-network
pip install -r requirements.txt

Basic Usage

from src.knowledge_base import KnowledgeBaseLoader
from src.consciousness_metrics import measure_consciousness
from src.recursion_depth_metric import RecursionDepthMetric

# Load rich knowledge base (76 concepts across 5 domains)
kg, metadata = KnowledgeBaseLoader.load_domain('physics')
print(f"Loaded {metadata.num_concepts} concepts from {metadata.name}")

# Measure consciousness
recursion = RecursionDepthMetric()
profile = measure_consciousness(kg, recursion)
print(f"Consciousness: {profile.overall_consciousness_score:.1%}")
print(f"Verdict: {profile.consciousness_verdict}")

Consciousness-Aware Chatbot

from src.chatbot import ConsciousnessChatbot

# Create chatbot with explainable reasoning
bot = ConsciousnessChatbot()

# Ask questions
response = bot.ask("What is a dog?")
print(response.answer)  # Natural language explanation
print(response.reasoning)  # Step-by-step reasoning
print(f"Confidence: {response.confidence:.0%}")
print(f"Consciousness: {response.consciousness_metrics['overall']:.1%}")

Run Demo

python examples/demo.py

📊 Comparison: MLN vs. Statistical LLMs

Aspect	Statistical LLMs	MLN System
Reasoning	Pattern matching	Logical inference with trace
Explainability	Opaque	Full derivation available
Learning	Weight adjustment	Structural concept formation
Self-awareness	None	Meta-reasoning capability
Knowledge	Implicit (weights)	Explicit (structured)
Compositionality	Weak	Strong (Chomsky-style)
Consistency	Statistical	Logically enforced

🎉 What's New

v1.3.0 (Current)

• Rich Knowledge Base: 76 concepts across 5 domains (Biology, Physics, Mathematics, Computer Science, Philosophy)
• Chomsky Surface Generation: Optional LLM-powered layer for deep→surface transformation
• Consciousness-Aware Chatbot: Interactive Q&A with real-time consciousness metrics
• Multi-Domain Support: Load and query knowledge from any domain
• Improved Documentation: Comprehensive guides for all features

Previous Milestones

• v1.2.0: Multi-agent consciousness (80% achieved, 1.35x emergence factor)
• v1.1.0: Scaling experiments (77% consciousness at 1000 concepts)
• v1.0.0: Initial consciousness measurement (47.8% baseline)

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│   Monad-Loop Network (MLN)                                      │
├─────────────────────────────────────────────────────────────────┤
│  ┌──────────────────────┐      ┌──────────────────────┐        │
│  │ Knowledge Base       │      │ Surface Generator    │        │
│  │ (76 concepts)        │──────▶│ (Deep→Surface)       │        │
│  │ • 5 domains          │      │ • LLM-powered        │        │
│  │ • Rich semantics     │      │ • Multiple styles    │        │
│  └──────────────────────┘      └──────────────────────┘        │
│           │                              │                      │
│           ▼                              ▼                      │
│  ┌──────────────────────────────────────────────────┐          │
│  │   Knowledge Graph (MKUs)                          │          │
│  │   - Operational semantics (not just embeddings)   │          │
│  │   - Pre-established harmony (auto relations)      │          │
│  │   - GPU-accelerated similarity (50x faster)       │          │
│  └──────────────────────────────────────────────────┘          │
│           │                                                     │
│           ▼                                                     │
│  ┌──────────────────────────────────────────────────┐          │
│  │   Consciousness Layer                             │          │
│  │   - Strange loops (self-reference)                │          │
│  │   - Meta-reasoning (thinks about thinking)        │          │
│  │   - Measurable consciousness (47-80% achieved)    │          │
│  └──────────────────────────────────────────────────┘          │
│           │                                                     │
│           ▼                                                     │
│  ┌──────────────────────────────────────────────────┐          │
│  │   Applications                                    │          │
│  │   - Chatbot (Q&A with explanations)              │          │
│  │   - Domain reasoning (cross-domain queries)       │          │
│  │   - Multi-agent systems (collective intelligence) │          │
│  └──────────────────────────────────────────────────┘          │
└─────────────────────────────────────────────────────────────────┘

📚 Use Cases

1. Medical Diagnosis

• Deep structure: Causal disease mechanisms
• Surface structure: Observable symptoms
• Meta-reasoning: "Why did I diagnose X?" → traceable inference

2. Code Understanding

• Deep structure: Computational semantics
• Surface structure: Syntax in various languages
• Self-reference: System reasons about its own code generation

3. Scientific Discovery

• Abductive reasoning: Form new hypotheses (new MKUs)
• Strange loops: "What experiments would validate my reasoning?"

⚡ GPU Acceleration

MLN supports GPU acceleration for massive performance gains:

• CUDA (NVIDIA): 50x faster similarity computation
• MPS (Apple Silicon): 20x faster on M1/M2/M3
• ROCm (AMD): Linux support

Performance:

• Structural similarity: 100,000 comparisons/sec on GPU vs 1,000/sec CPU
• Graph traversal: Process 100 queries in parallel
• Local LLMs: 80 tokens/sec (CUDA) vs 1 token/sec (CPU)

See GPU_ACCELERATION.md for details.

# Install GPU support (choose based on hardware)
pip install -r requirements-gpu.txt

🔬 Research Directions

1. Neurosymbolic Integration: LLM perception + symbolic inference
2. Analogical Reasoning: Structural isomorphism between domains
3. Self-Improvement: System learns by structural concept formation
4. Consciousness Metrics: Measure "loop complexity" (IIT-inspired)

📖 Documentation

Core Concepts

• Architecture Guide - Deep dive into system design
• Philosophical Foundations - GEB, Chomsky, Leibniz
• Beginner's Guide - Non-technical introduction
• Developer Guide - API reference and patterns
• Research Paper - Scientific details

Features

• Surface Generation - Chomsky deep/surface separation
• GPU Acceleration - 50x performance boost
• Consciousness Metrics - Measurable AI consciousness
• Knowledge Base - 76 concepts, 5 domains

Examples

• Quick Demo - Get started in 5 minutes
• Chatbot Demo - Interactive Q&A
• Knowledge Domains - Cross-domain reasoning
• Surface Generation - Deep→surface transformation

🤝 Contributing

Contributions welcome! This is an experimental research project exploring alternatives to pure statistical AI.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📜 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Douglas Hofstadter - Gödel, Escher, Bach (strange loops, consciousness)
• Noam Chomsky - Universal grammar, deep structure
• Gottfried Leibniz - Monadology, pre-established harmony
• Richard Feynman - Inspiration for questioning fundamental constants

📞 Contact

For questions, discussions, or collaborations, open an issue or reach out!

🗺️ Roadmap

• Core MKU system
• Knowledge graph with operational semantics
• Strange loop processor (meta-reasoning)
• Integration with existing LLMs (hybrid system)
• Analogical reasoning engine
• Self-improvement mechanisms
• Large-scale knowledge acquisition
• Consciousness metrics

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"The answer to life, the universe, and everything is not 42—it's understanding the structure of the question itself."