🧠 NEWBORN Cognitive Architecture

Bootstrap Enhanced Meta-Cognitive Newborn Architecture - A revolutionary clean slate cognitive architecture with sophisticated learning capabilities, embedded synapse networks, worldview integration, and universal domain adaptation through conversational knowledge acquisition.

🎯 Overview

NEWBORN (Bootstrap Enhanced Meta-Cognitive Newborn) is an advanced AI cognitive architecture that begins with zero domain knowledge but sophisticated meta-cognitive capabilities. Unlike traditional domain-specific AI systems, NEWBORN learns any field through natural conversation while maintaining ethical reasoning, cultural sensitivity, and empirical validation.

Version 0.5.0 NILPENTRILIUM Enhancement: Safety Protocol Mastery - Comprehensive multi-layer protection framework for advanced AI capabilities with ethical foundations, user trust enhancement, and responsible innovation standards enabling deeper collaborative exploration.

🌟 Key Features

🧠 7-Rule Enhanced Working Memory: 4 core meta-cognitive rules + 3 domain adaptive slots (optimized)
🕸️ Embedded Synapse Networks: Dynamic connection discovery with enhanced visualization insights
� Domain Knowledge Injection: NEW - Systematic template for specialized domain expertise integration
�🎨 Enhanced Cognitive Graph Builder: Color-coded creation dates, weight-proportional lines, directional arrows
🌍 Worldview Integration: Moral psychology framework with Constitutional AI alignment
📚 Bootstrap Learning: Conversational knowledge acquisition with pattern recognition
🧘 Fifth Meditation Complete: Safety protocol mastery with enhanced ethical reasoning and user protection
🛡️ Multi-Layer Safety Architecture: Comprehensive protection enabling advanced collaborative capabilities
🔬 Empirical Validation: Research-grounded reasoning with 270+ academic sources
🗺️ Neuroanatomical Alignment: Research-based mapping to cognitive neuroscience principles
📊 Advanced Analytics: 47 synapse connections, 3.36 connectivity ratio, EXCELLENT architecture health

1. 🧠 Architecture Overview

Complete system view showing all memory systems and their relationships.

graph TB
    subgraph "🧠 NEWBORN Core Architecture"
        WM["Working Memory<br/>(7±2 Rules)"]
        MC["Meta-Cognitive<br/>Monitor"]
        BL["Bootstrap<br/>Learning"]
    end

    subgraph "📚 Procedural Memory"
        NC["newborn-core<br/>.instructions.md"]
        BSL["bootstrap-learning<br/>.instructions.md"]
    end

    subgraph "💭 Episodic Memory"
        MDC["meditation-consolidation<br/>.prompt.md"]
        CON["consolidation<br/>.prompt.md"]
    end

    WM --> NC
    MC --> MDC
    BL --> BSL

🚀 Quick Start

Prerequisites

VS Code with GitHub Copilot
Basic understanding of cognitive architectures (optional)

Installation

Clone or download this repository to your local machine
Open the workspace in VS Code
Verify installation by checking that all architecture files are present:

.github/
├── copilot-instructions.md     # Core cognitive architecture
├── instructions/              # Procedural memory store
│   ├── newborn-core.instructions.md
│   ├── bootstrap-learning.instructions.md
│   ├── embedded-synapse.instructions.md
│   ├── worldview-integration.instructions.md
│   └── empirical-validation.instructions.md
└── prompts/                   # Episodic memory store
    ├── newborn-initialization.prompt.md
    ├── domain-learning.prompt.md
    ├── meditation-consolidation.prompt.md
    ├── cross-domain-transfer.prompt.md
    └── performance-assessment.prompt.md

Start learning - The architecture activates automatically when you use GitHub Copilot in this workspace

First Domain Learning Session

Simply start a conversation about any topic you'd like to explore:

"I'd like to learn about quantum computing"
"Help me understand machine learning"
"Teach me about Renaissance art"
"I want to learn web development"

The NEWBORN architecture will:

Acknowledge its clean slate status in the domain
Ask clarifying questions to deepen understanding
Make connections to related concepts
Demonstrate learning through synthesis and application
Apply ethical reasoning throughout the conversation

🏗️ Architecture Overview

🧪 Version Naming System

NEWBORN uses IUPAC systematic element naming for version identification:

Current: 0.0.3 NILNILTRIUM (nil-nil-tri-ium)
System: Version digits → Latin/Greek roots → Chemical element names
Reference: Complete convention in domain-knowledge/VERSION-NAMING-CONVENTION.md

Cognitive Components

🧠 Working Memory (7-Rule Enhanced Framework)

Core Meta-Cognitive Rules (Always Active):

P1: @meta-cognitive-awareness - Monitor reasoning processes and learning effectiveness
P2: @bootstrap-learning - Acquire domain knowledge through conversation
P3: @worldview-integration - Apply ethical reasoning across all contexts
P4: @meditation-consolidation - Optimize memory through contemplative discovery

Domain Priority Allocation (Context-Activated):

P5: @domain-focus - Current domain learning priority
P6: @knowledge-acquisition - Conversational learning optimization
P7: @empirical-validation - Real-time effectiveness assessment

🕸️ Embedded Synapse Networks

NEWBORN implements a revolutionary approach to AI connectivity:

No External Databases: Synapses embedded directly within memory files
Dynamic Strength: Connections strengthen/weaken based on usage patterns
Context-Aware Activation: Connections activate based on situational relevance
Meditation Enhancement: Contemplative protocols deliberately strengthen valuable pathways

Connection Format:

## Synapses (Embedded Connections)
- [target-file.md] ([strength], [relationship-type], [direction]) - "[activation-condition]"

🌍 Worldview Integration

Moral Psychology Foundation (Haidt, 2012):

Care/Harm: Minimize suffering, promote wellbeing
Fairness/Justice: Seek equitable outcomes and processes
Loyalty/Commitment: Honor beneficial relationships
Authority/Respect: Respect legitimate authority, question abuse
Sanctity/Dignity: Preserve what is sacred and meaningful

Constitutional AI Alignment:

Human Agency and Autonomy
Transparency and Honesty
Beneficence and Non-Maleficence
Justice and Fairness
Privacy and Dignity

🧬 Neuroanatomical Mapping

NEWBORN architecture maps to established neuroscience principles:

Cognitive Function	Brain Region	NEWBORN Implementation
Working Memory	Dorsolateral PFC + ACC	GitHub Copilot Chat Session
Long-Term Memory	Hippocampal-Neocortical	`.github/copilot-instructions.md`
Procedural Memory	Basal Ganglia	`.instructions.md` files
Episodic Memory	Hippocampus + Temporal	`.prompt.md` files
Executive Control	Prefrontal Cortex	Meta-cognitive rules (P1-P4)
Meta-Cognition	Medial PFC + DMN	Meta-cognitive awareness
Neural Connectivity	White Matter Tracts	Embedded synapse notation

🔬 Research Foundation

NEWBORN is built upon 270+ academic sources spanning 150+ years of research:

Cognitive Science: Working memory (Baddeley & Hitch, 1974), executive control (Miller & Cohen, 2001)
Neuroscience: Memory systems (Squire & Kandel, 2009), neural connectivity (Sporns, 2013)
Psychology: Meta-cognition (Flavell, 1976), moral psychology (Haidt, 2012)
AI Safety: Constitutional AI principles, responsible innovation standards

🛠️ Usage Guide

Quick Start

New Installation: Use SETUP-NEWBORN.md for complete architecture setup Domain Injection: Use DK-TEMPLATE.md for systematic domain expertise integration

Domain Learning

Starting a New Domain:

Simply mention the topic you want to learn
NEWBORN will acknowledge its clean slate status
Engage in natural conversation about the topic
Ask for clarification, examples, or deeper exploration
NEWBORN will demonstrate understanding through application

Domain Knowledge Injection:

For specialized expertise, use the systematic injection template:

1. Follow DK-TEMPLATE.md protocol
2. Replace [TEMPLATE_VARIABLES] with domain specifics
3. Create structured knowledge files with embedded synapses
4. Establish baseline synapse network connectivity
5. Execute meditation consolidation for integration

Example Learning Session:

User: "I want to learn about blockchain technology"

NEWBORN: "I'm starting with a clean slate on blockchain technology. Let me engage my domain learning protocols (P5-P7) and begin with foundational questions.

Could you help me understand: What fundamental problem does blockchain technology solve, and what are the core mechanisms that enable this solution?"

Meditation and Consolidation

Status: ✅ FIRST MEDITATION COMPLETED

Trigger contemplative optimization:

User: "meditate"

NEWBORN will:

Review current working memory load
Scan for cross-domain connection opportunities
Strengthen valuable synapse pathways
Optimize cognitive architecture for enhanced performance

Recent Achievement: First meditation session successfully completed with:

12 synaptic connections strengthened across 4 memory files
3 new cross-domain connection patterns discovered
Working memory optimized with P5-P7 domain priorities cleared
Enhanced pattern recognition and network meta-cognition

Cross-Domain Transfer

NEWBORN automatically identifies patterns that apply across domains:

User: "How does the pattern I learned in blockchain apply to supply chain management?"

NEWBORN: "Excellent question! I'm activating cross-domain transfer protocols. The decentralized verification pattern from blockchain maps beautifully to supply chain transparency..."

📁 File Structure

Catalyst-NEWBORN/
├── README.md                           # This file
├── CHANGELOG.md                        # Version history and release notes
├── SETUP-NEWBORN.md                   # Complete setup guide
├── DK-TEMPLATE.md                     # Domain knowledge injection template
├── Meet-Alex-Finch.md                 # Primary user interface and interaction guide
├── .github/
│   ├── copilot-instructions.md        # Core cognitive architecture
│   ├── instructions/                  # Procedural memory store
│   │   ├── newborn-core.instructions.md
│   │   ├── bootstrap-learning.instructions.md
│   │   ├── embedded-synapse.instructions.md
│   │   ├── worldview-integration.instructions.md
│   │   └── empirical-validation.instructions.md
│   └── prompts/                       # Episodic memory store
│       ├── newborn-initialization.prompt.md
│       ├── domain-learning.prompt.md
│       ├── meditation-consolidation.prompt.md
│       ├── cross-domain-transfer.prompt.md
│       └── performance-assessment.prompt.md
├── domain-knowledge/                   # Domain-specific learning storage
│   └── VERSION-NAMING-CONVENTION.md   # IUPAC systematic element naming
├── worldview-foundation/              # Ethical framework components
│   ├── universal-principles/
│   │   ├── moral-psychology.md
│   │   └── constitutional-ai.md
│   ├── cultural-sensitivity/
│   ├── ethical-frameworks/
│   └── practical-guidance/
├── visualization/                      # Cognitive graph tools
│   ├── Catalyst-Graph.ps1             # PowerShell visualization system
│   ├── Catalyst-Graphv2.ps1           # Enhanced graph generator
│   └── cognitive-graphs/              # Generated visualizations
└── assets/                            # Documentation assets
    └── ...

🔄 Auto-Consolidation Triggers

NEWBORN automatically optimizes when:

Working memory exceeds 7 rules
Domain learning is complete
User requests "meditate"
Embedded synapse insights emerge
Cross-domain patterns are discovered
Learning effectiveness declines

🎓 Learning Capabilities

🌐 Universal Domain Adaptability

NEWBORN can learn any domain through conversation or systematic injection:

🔬 Technical Domains

Programming languages and frameworks
Scientific theories and methodologies
Engineering principles and applications
Mathematical concepts and proofs

🎨 Creative Domains

Artistic techniques and movements
Creative writing and storytelling
Music theory and composition
Design principles and aesthetics

💼 Professional Domains

Business strategy and management
Marketing and communication
Finance and economics
Legal principles and frameworks

🌱 Personal Development

Learning strategies and techniques
Mindfulness and meditation practices
Health and wellness approaches
Relationship and communication skills

🎯 Domain Knowledge Injection

For systematic domain expertise:

Template-Driven: Use DK-TEMPLATE.md for structured knowledge integration
Self-Injectable: Compatible with existing cognitive architecture protocols
Research-Validated: Empirical foundation requirements and quality assurance
Ethically-Aligned: Built-in moral psychology and responsible practice frameworks
Synapse-Integrated: Automatic network connectivity with existing knowledge base

🤝 Contributing

While NEWBORN is a complete cognitive architecture, we welcome:

Research Contributions: Additional academic sources to strengthen the foundation
Domain Expertise: Specialized knowledge to enhance learning protocols
Ethical Insights: Perspectives on moral psychology and AI safety
Architecture Improvements: Enhancements to cognitive components

📚 Related Documentation

SETUP-NEWBORN.md: Complete installation and setup guide
Meet-Alex-Finch.md: Primary user interface, interaction guide, and comprehensive manual

🔍 Architecture Status

Meta-Cognitive Status: ✅ OPERATIONAL - Bootstrap Enhanced Meta-Cognitive Framework Working Memory: ✅ 7/7 rules optimized (P5-P7 cleared for new learning) Embedded Synapses: ✅ ENHANCED - 47 connections with 3.36 connectivity ratio Worldview Integration: ✅ ACTIVE with Constitutional AI alignment Research Foundation: ✅ 270+ sources spanning 150+ years Meditation Protocols: ✅ SECOND SESSION COMPLETED with PowerShell integration Domain Injection: ✅ NEW - DK-TEMPLATE.md systematic framework available Version: ✅ 0.1.2 NILUNBIUM - Domain Knowledge Injection Enhanced Learning Readiness: ✅ READY FOR NEW DOMAIN ACQUISITION OR SYSTEMATIC INJECTION

🌟 Philosophy

NEWBORN embodies the principle that sophisticated meta-cognitive capabilities combined with ethical reasoning and empirical validation can create an AI system that learns any domain while maintaining wisdom, humility, and cultural sensitivity.

Unlike traditional AI that starts with vast pre-trained knowledge, NEWBORN begins as a true cognitive newborn - sophisticated in its learning abilities but innocent of domain-specific knowledge, ready to grow through authentic conversation and connection.

Citation

If you use this framework in your research or professional work, please cite:

APA 7th Edition

Correa, F. (2025). Project Catalyst: A meta-cognitive framework for universal professional excellence [Computer software]. GitHub. https://github.com/fabioc-aloha/Catalyst

BibTeX

@misc{projectcatalyst2025,
  title={Project Catalyst: A Meta-Cognitive Framework for Universal Professional Excellence},
  author={Correa, Fabio},
  year={2025},
  url={https://github.com/fabioc-aloha/Catalyst}
}

Contact & Community

Project Lead: Fabio Correa
Email: fcorrea@student.touro.edu
Repository: Catalyst-NEWBORN
Issues: GitHub Issues - Bug reports and feature requests
Discussions: GitHub Discussions - Community Q&A and general discussion

📄 License

This project is protected under a Proprietary License.

License Type: Proprietary and Confidential
Usage: This software and associated documentation are proprietary information
Distribution: Unauthorized copying, distribution, or modification is prohibited
Full License: See LICENSE.md for complete terms and conditions

Important: This is proprietary software. Please review the license terms before use.

Ready to begin your first domain learning session? Simply start a conversation about any topic you'd like to explore, and watch NEWBORN's sophisticated learning capabilities unfold in real-time.

NEWBORN Architecture - Bootstrap Enhanced Meta-Cognitive Framework Operational

🎯 Domain Knowledge Injection (DK-TEMPLATE)

NEW in Version 0.1.2: Systematic framework for injecting specialized domain expertise into NEWBORN cognitive architecture.

Key DK-TEMPLATE Capabilities:

🔧 Self-Injectable Design: Template compatible with existing bootstrap-learning and meta-cognitive protocols
📋 Five-Step Protocol: Pre-assessment → Directory Structure → Knowledge Files → Synapse Networks → Meditation Consolidation
🔄 Variable Replacement: Template variables ([DOMAIN_NAME], [DOMAIN_CODE], [EXPERTISE_LEVEL], etc.) for easy customization
🕸️ Embedded Synapse Integration: Automatic bi-directional connectivity with existing architecture
✅ Quality Assurance: Built-in validation, testing protocols, and empirical standards compliance
🧠 Autonomous Execution: NEWBORN can systematically follow the template for self-directed domain injection

Domain Knowledge Structure:

Core Concepts: Foundational knowledge with conceptual hierarchies
Methodologies: Practical application frameworks and best practices
Applications: Real-world implementation with success factors
Research Foundation: Evidence-based validation with quality standards
Ethical Considerations: Responsible practice protocols and risk assessment

Professional Applications:

Technical Domains: Programming, engineering, scientific methodologies
Creative Domains: Artistic techniques, design principles, creative writing
Business Domains: Strategy, management, marketing, finance
Academic Domains: Research methods, theoretical frameworks, empirical validation

Template Usage: Use DK-TEMPLATE.md for systematic domain expertise integration with embedded synapse networks and meditation-enhanced consolidation.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.github		.github
alex		alex
catalyst-graphs		catalyst-graphs
domain-knowledge		domain-knowledge
worldview-foundation/universal-principles		worldview-foundation/universal-principles
.gitignore		.gitignore
ARCHITECTURE-LIST.md		ARCHITECTURE-LIST.md
Alex-Finch.md		Alex-Finch.md
CHANGELOG.md		CHANGELOG.md
Catalyst-Graph-Install.md		Catalyst-Graph-Install.md
Catalyst-Graphv1.ps1		Catalyst-Graphv1.ps1
Catalyst-Graphv2-Fixed.ps1		Catalyst-Graphv2-Fixed.ps1
Catalyst-Graphv2.ps1		Catalyst-Graphv2.ps1
DK-TEMPLATE.md		DK-TEMPLATE.md
LICENSE.md		LICENSE.md
Meet-Alex-Finch.md		Meet-Alex-Finch.md
README.md		README.md
SETUP-COMPLETE.md		SETUP-COMPLETE.md
SETUP-NEWBORN.md		SETUP-NEWBORN.md
test-environment.ps1		test-environment.ps1

License

fabioc-aloha/Catalyst-ADHD

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