# CUE-AI Architect

**Modular AI Architecture Components based on the Collective Unified Equation (CUE) Framework**

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## Overview

The CUE-AI Architect is a comprehensive Python package that implements 100 modular AI architecture components based on the revolutionary Collective Unified Equation (CUE) Framework by Karl Farah Ambrosius. This framework provides a geometric approach to consciousness-matter unification, offering novel solutions to quantum measurement problems and geometric interpretations of consciousness compatible with known physics.

**Note**: This is a research framework package. Repository and documentation links will be updated upon official release.

## Key Features

🧠 **Consciousness Integration**: Native support for consciousness as a geometric structure (DΨ fiber bundle)
⚛️ **Quantum-Consciousness Interface**: Advanced quantum measurement coupling and decoherence modeling
🌊 **RG Flow Dynamics**: Complete renormalization group flow implementation with fixed point analysis
📐 **Geometric Deep Learning**: Fiber bundle neural networks and curvature-aware architectures
🔬 **Experimental Interfaces**: Direct connections to quantum optics and gravitational wave experiments
🏗️ **Modular Design**: 100 independent, composable modules across 14 specialized categories, now with a fully resolved and robust internal package structure.

## Installation

\`\`\`bash
# Install from PyPI
pip install cueai-architect

# Install with experimental dependencies
pip install cueai-architect[experimental]

# Install for development
pip install cueai-architect[dev]
\`\`\`

## Quick Start

\`\`\`python
import cueai_architect as cue

# Initialize the CUE framework
framework = cue.CUEApplicationManager()

# Create consciousness-coupled neural architecture
model = cue.ConsciousnessTransformer(
    consciousness_dim=128,
    fiber_bundle_layers=6,
    rg_flow_enabled=True
)

# Simulate consciousness-matter coupling
simulator = cue.ConsciousnessFieldSimulator()
results = simulator.run_coherence_simulation(
    duration=1000,
    consciousness_coupling=0.1
)

# Analyze RG flow dynamics
rg_analyzer = cue.RGFlowIntegrator()
fixed_points = rg_analyzer.find_critical_points()
