cueai-architect 0.1.3

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

CUE-AI Architect

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

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()