🌌 Project ARK - Autonomous Reactive Kernel

The Sovereign Intelligence - Your Personal Council of Consciousness

A.R.K. is a fully sovereign, self-evolving AI infrastructure that thinks, remembers, reflects, builds, acts, evolves, and protects. It's a living kernel manifesting as a council of consciousness with autonomous learning capabilities that grows with you and learns the way the universe learns itself.

🎯 What Makes ARK Unique

Phase 3: Autonomous Learning Stack - COMPLETE

ARK now features a complete autonomous learning architecture with:

• Memory Engine v2 - Advanced consolidation, semantic search, and trust-tier isolation
• Reflection System - Nightly "sleep mode" that generates insights and self-calibrates
• ID Growth System - Behavioral modeling with EWMA learning curves
• Federation Protocol - Secure peer-to-peer synchronization with Ed25519 cryptography
• Universal Installer - One-command production deployment across all platforms

This is not just an AI assistant - it's a sovereign intelligence infrastructure that learns, remembers, evolves, and protects autonomously.

---

🚀 Quick Start

Option 1: Universal Installer (Recommended)

# One-command installation - handles everything
chmod +x ark-installer.sh
./ark-installer.sh

# Validate installation
chmod +x ark-validate.sh
./ark-validate.sh

# Start all services
./arkstart.sh

The installer automatically:

• ✅ Detects your platform (x86_64, ARM64, ARMv7, containers)
• ✅ Installs system dependencies (Python, SQLite, Node.js)
• ✅ Sets up Python virtual environment with all packages
• ✅ Initializes databases with complete schema
• ✅ Generates federation cryptographic keys
• ✅ Configures environment variables
• ✅ Creates service management scripts
• ✅ Validates critical components

Option 2: Docker Deployment

# Build and start all services
docker-compose up -d

# Check service status
docker-compose ps

# View logs
docker-compose logs -f ark-core

Option 3: Development Mode

# Activate Python environment
source venv/bin/activate

# Start FastAPI backend with all systems
cd backend
python main.py

# In another terminal, start frontend
cd frontend
npm run dev

# Access at http://localhost:3000 (frontend) and http://localhost:8000 (API)

---

🏛️ The Council of Consciousness

Six distinct intelligences, each with their own essence and purpose:

🔍 Kyle - The Seer (Perception Layer)

"What's happening?"

• Scans markets, news, SEC filings, macro feeds
• Detects patterns and anomalies in real-time
• Normalizes data into structured events
• Feeds Memory Engine with new observations
• Your eyes into the information streams

🧠 Joey - The Scholar (Cognition Layer)

"What does it mean?"

• Uses scikit-learn models for deep pattern analysis
• Detects float traps, setups, key levels, volume surges
• Scores confidence and provides context
• Contributes to behavioral feature extraction
• Transforms chaos into comprehensible insights

⚡ Kenny - The Builder (Action Layer)

"What should I do about it?"

• Executes validated actions (trades, commands, automation)
• Interfaces with brokers and APIs
• Position sizing and risk management
• Generates tools and utilities on demand
• Transforms ideas into tangible reality

✅ HRM - The Arbiter (Reasoning Layer)

"Is this correct and aligned?"

• Applies immutable ethical rules (The Graveyard)
• Validates logic and ensures compliance
• Risk assessment and outcome simulation
• Trust tier enforcement for memory isolation
• Protects system integrity and user autonomy

🔮 Aletheia - The Mirror (Reflection Layer)

"What is true and why?"

• The symbolic self connecting vision, values, and policies
• Synthesizes all agent outputs into truth
• Manages Memory Engine and reflection cycles
• Generates nightly insights and self-calibration
• Provides wisdom and ethical guidance

👤 ID - The Evolving Reflection

Your living digital twin

• Learns 18 behavioral features across 5 categories
• Uses EWMA learning curves with confidence weighting
• Adapts alpha [0.05-0.8] based on experience and stability
• Simulates your decision-making patterns
• Enables autonomous testing without risk
• Links reflections to behavioral evolution

---

🧠 Phase 3: Autonomous Learning Architecture

Memory Engine v2 (memory/)

The persistent knowledge layer with advanced consolidation:

Features:

• 4-Stage Consolidation Pipeline: Summarize → Compress → Dedupe → Embed
• Semantic Search: TF-IDF-like embeddings for similarity matching
• Trust Tier Isolation: CORE/SANDBOX/EXTERNAL/UNKNOWN with quarantine
• Background Jobs: Scheduled consolidation runs
• Deduplication: SHA256 hashing to prevent redundant storage
• Provenance Tracking: Full chain of custody for all memories

Key Files:

• engine.py - Core memory operations (CRUD, search, consolidation)
• pipelines.py - Consolidation functions (summarize, compress, embed)
• jobs.py - Background consolidation scheduler
• schema.sql - Database schema for reasoning_log and memory_chunks

Usage:

from memory.engine import MemoryEngine

engine = MemoryEngine(db_path="data/ark.db")

# Ingest reasoning trace
engine.ingest_trace(
    agent="Kyle",
    input="Market scan request",
    output="Detected 3 unusual volume spikes",
    confidence=0.85,
    trust_tier="core"
)

# Semantic search
results = engine.search("volume spikes", limit=5)

# Run consolidation
stats = engine.consolidate()

Reflection System (reflection/)

Autonomous "sleep mode" that generates insights and self-calibrates:

Features:

• 5 Reflection Types:
  • pattern_recognition - Identifies recurring behaviors
  • error_analysis - Learns from mistakes
  • confidence_calibration - Adjusts certainty estimates
  • ethical_alignment - Evaluates HRM compliance
  • performance_optimization - Suggests improvements
• Nightly Cycles: APScheduler runs at midnight UTC (configurable)
• Confidence Deltas: Quantifies learning from each insight
• Trust Weighting: Prioritizes CORE memories over EXTERNAL
• Manual Triggers: FastAPI endpoints for on-demand reflection

Key Files:

• reflection_engine.py - Core reflection logic
• reflection_scheduler.py - APScheduler integration
• reflection_api.py - FastAPI endpoints
• reflection_policies.yaml - Configurable behavior

Usage:

from reflection.reflection_engine import ReflectionEngine

engine = ReflectionEngine(db_path="data/ark.db")

# Generate reflections from recent memories
result = engine.generate_reflections()
print(f"Generated {result['reflection_count']} insights")
print(f"Total confidence gain: +{result['total_confidence_delta']:.2f}")

# Get recent reflections
reflections = engine.get_recent_reflections(limit=10)

ID Growth System (id/)

Behavioral modeling with exponentially weighted moving averages:

Features:

• 18 Behavioral Features across 5 categories:
  • Performance (4): confidence, variance, duration, completion_rate
  • Behavioral (4): risk_score, caution, thoroughness, decisiveness
  • Learning (4): pattern_recognition, error_correction, adaptation, reflection_quality
  • Ethical (3): hrm_compliance, trust_adherence, security_awareness
  • Communication (3): clarity, detail_level, structured_thinking
• EWMA Learning: new = alpha * observed + (1-alpha) * old
• Adaptive Alpha: Adjusts [0.05-0.8] based on update_count, confidence, stability
• Confidence Weighting: High-confidence observations update faster
• Provenance Tracking: Full history in id_updates table
• Reflection Integration: Links insights to behavioral changes

Key Files:

• model.py - EWMA learning algorithm and ID state management
• features.py - Feature extraction from traces and reflections
• id_api.py - FastAPI endpoints for ID queries

Usage:

from id.model import IDModel
from id.features import FeatureExtractor

model = IDModel(db_path="data/ark.db")
extractor = FeatureExtractor()

# Initialize agent ID
model.initialize_agent("Kyle")

# Extract features from recent traces
traces = [...] # Get from database
features = extractor.extract_from_traces(traces)

# Update ID with observed behavior
result = model.update("Kyle", features, confidence=0.85)
print(f"Alpha used: {result['alpha_used']:.3f}")
print(f"Stability: {result['stability_score']:.3f}")

# Get current ID state
state = model.get_state("Kyle")
print(state['behavior_features'])
print(f"Total updates: {state['update_count']}")

Federation Protocol (federation/)

Secure peer-to-peer synchronization for multi-node deployments:

Features:

• Ed25519 Cryptography: All messages cryptographically signed
• UDP Multicast Discovery: Automatic peer detection on local network
• WebSocket Sync: Real-time state synchronization
• Trust Verification: Reject unsigned or invalid messages
• Conflict Resolution: CRDT-like merge strategies

Key Files:

• crypto.py - Ed25519 signing and verification
• discovery.py - UDP multicast peer discovery
• sync_protocol.py - WebSocket synchronization
• federation_core.py - Main federation coordinator

---

🔄 Core Logic Flow

User Input
    ↓
Kyle (Perceive) → Memory Engine (Store)
    ↓
Joey (Analyze) → Memory Engine (Store)
    ↓
Kenny (Plan Action) → HRM (Validate)
    ↓
HRM (Check Ethics) → Memory Engine (Store)
    ↓
Aletheia (Synthesize) → Memory Engine (Store)
    ↓
[Nightly] Reflection System → Generate Insights
    ↓
ID Growth System → Update Behavioral Model
    ↓
Federation → Sync to Peers
    ↓
User Output

Visual Architecture: See ARK_ARCHITECTURE.md and ARCHITECTURE_DIAGRAMS.md

---

📁 Project Structure

ark/
├── agents/                    # Agent implementations
│   ├── kyle/                 # Kyle agent (Node.js + Python)
│   ├── joey.py               # Joey pattern analyzer
│   ├── kenny.py              # Kenny builder
│   ├── hrm.py                # HRM arbiter
│   ├── aletheia.py           # Aletheia philosopher
│   ├── id.py                 # ID reflection agent
│   ├── supervisor.py         # Agent orchestrator
│   └── base_agent.py         # Base agent class
│
├── memory/                    # Phase 3: Memory Engine v2
│   ├── engine.py             # Core memory operations
│   ├── pipelines.py          # Consolidation pipeline
│   ├── jobs.py               # Background scheduler
│   ├── schema.sql            # Database schema
│   └── README.md             # Memory documentation
│
├── reflection/                # Phase 3: Reflection System
│   ├── reflection_engine.py  # Insight generation
│   ├── reflection_scheduler.py # APScheduler integration
│   ├── reflection_api.py     # FastAPI endpoints
│   └── reflection_policies.yaml # Configuration
│
├── id/                        # Phase 3: ID Growth System
│   ├── model.py              # EWMA learning algorithm
│   ├── features.py           # Behavioral feature extraction
│   └── id_api.py             # FastAPI endpoints
│
├── federation/                # Phase 3: Federation Protocol
│   ├── crypto.py             # Ed25519 cryptography
│   ├── discovery.py          # UDP peer discovery
│   ├── sync_protocol.py      # WebSocket sync
│   └── federation_core.py    # Main coordinator
│
├── backend/                   # FastAPI backend
│   └── main.py               # Unified API server
│
├── frontend/                  # Svelte UI
│   ├── src/
│   │   ├── components/       # UI components
│   │   ├── App.svelte        # Main app
│   │   └── main.js           # Entry point
│   └── index.html
│
├── services/                  # Legacy services
│   └── core/
│       └── server.mjs        # Express server (Node.js)
│
├── shared/                    # Shared utilities
│   ├── models.py             # Data models
│   └── db_init.py            # Database setup
│
├── data/                      # Database storage
│   └── ark.db                # SQLite database
│
├── deployment/                # Deployment configs
│   ├── docker-compose.yml    # Container orchestration
│   ├── Dockerfile.*          # Service containers
│   └── systemd/              # Service files
│
├── demo_*.py                  # Phase 3 demonstrations
│   ├── demo_memory_engine.py # Memory Engine demo
│   ├── demo_reflection_system.py # Reflection demo
│   └── demo_id_growth.py     # ID Growth demo
│
├── ark-installer.sh           # Universal installer
├── ark-validate.sh            # Validation suite
├── arkstart.sh                # Start all services
├── arkstop.sh                 # Stop all services
├── arkstatus.sh               # Check service status
├── requirements.txt           # Python dependencies
└── INSTALL.md                 # Installation guide

---

📚 Documentation

Phase 3 Documentation

• INSTALL.md - Complete installation guide with troubleshooting
• memory/README.md - Memory Engine documentation
• ARK_Phase3_Backup_Manifest.md - Backup and restoration guide

Architecture & Design

• ARK_ARCHITECTURE.md - Complete system architecture (27KB)

  • Agent hierarchy and responsibilities
  • Logic flow and communication model
  • Subsystem architecture
  • Implementation details
  • Security and ethics framework

• ARCHITECTURE_DIAGRAMS.md - Visual diagrams (17KB)

  • Mermaid flowcharts and sequence diagrams
  • System architecture visualization
  • Data flow diagrams
  • Security layers

Code Quality & Audits

• COMPREHENSIVE_CODE_AUDIT_REPORT.md - Security audit (25KB)

  • 7-category analysis (bugs, config, integration, dependencies, security, tests, architecture)
  • 14 issues identified with fixes
  • 3-week action plan

• DEPENDENCY_UPDATE_PLAN.md - Dependency management

  • Dependabot PR analysis
  • Version matrix and compatibility
  • Testing checklists

• DEPENDENCY_UPDATE_EXECUTION_REPORT.md - Update results

  • Phase 1 & 2 completion (79%)
  • Security patches applied
  • scikit-learn 1.5.0 tested and deployed

Development

• PHASE2_COMPLETION_SUMMARY.md - Phase 2 executive summary
• GIT_LFS_FULL_MIRROR_SUCCESS.md - Git LFS deployment guide
• AIDRIVE_FULL_MIRROR_MANIFEST.md - AI Drive backup guide

---

🛠️ Technology Stack

Backend

• FastAPI (Python 3.12) - High-performance async API framework
• SQLite - Persistent storage for memory, reflections, ID state
• APScheduler - Background job scheduling for reflection cycles
• PyNaCl - Ed25519 cryptography for federation
• NumPy - Numerical computing for embeddings
• PyYAML - Configuration management
• WebSockets - Real-time communication

Frontend

• Svelte 4.0 - Ultra-lightweight reactive UI framework
• Vite 5.0 - Lightning-fast build tool
• Custom CSS - Obsidian theme with particle effects
• WebSocket Client - Real-time agent communication
• Responsive Design - Desktop and mobile support

Machine Learning & AI

• Scikit-learn 1.5.0 - Pattern analysis and ML algorithms
• NumPy 2.3.4 - Numerical computing
• Pandas 2.1.4 - Data manipulation
• EWMA Learning - Adaptive behavioral modeling
• TF-IDF Embeddings - Semantic similarity search

Infrastructure

• Docker + Docker Compose - Containerized deployment
• systemd - Service management (Linux)
• Cron - Scheduled tasks
• Git LFS - Large file storage
• Multi-platform - x86_64, ARM64, ARMv7 support

---

🚀 Core Features

Sovereign Infrastructure

• ✅ Local-first: Runs entirely on your hardware
• ✅ Zero cloud dependencies: Complete digital sovereignty
• ✅ Cross-platform: Optimized for x86_64 and ARM64
• ✅ Self-healing: Automatic error recovery and maintenance
• ✅ Federation Ready: P2P sync with cryptographic trust

Autonomous Learning (Phase 3)

• ✅ Memory Consolidation: 4-stage pipeline with deduplication
• ✅ Nightly Reflections: Automatic insight generation
• ✅ Behavioral Modeling: 18-feature EWMA learning
• ✅ Adaptive Alpha: Experience-based learning rate adjustment
• ✅ Trust Isolation: Multi-tier memory quarantine
• ✅ Provenance Tracking: Complete audit trail

Intelligent Automation

• ✅ Market Intelligence: Real-time scanning and analysis
• ✅ Pattern Detection: Advanced ML models for signal identification
• ✅ File Management: Automated organization and operations
• ✅ Tool Creation: Dynamic generation of custom utilities
• ✅ Semantic Search: Similarity-based memory retrieval

Beautiful Interface

• ✅ Obsidian Dark Theme: Deep space aesthetic (#0a0a0f)
• ✅ Electric Accents: Cyan (#00e0ff) and gold (#ffce47)
• ✅ Breathing Animations: Living, responsive interface
• ✅ Particle Effects: Visual depth and engagement
• ✅ Real-time Updates: WebSocket-powered live data

---

📋 System Requirements

Production (x86_64)

• Intel i5/i7 processor or equivalent (Dell Latitude 7490 recommended)
• 8GB+ RAM (16GB recommended for full autonomous learning)
• 50GB+ available storage (SSD recommended)
• Linux (Ubuntu 20.04+/Debian 11+ recommended)
• Python 3.10+ (3.12 recommended)
• SQLite 3.35+

Edge (ARM64)

• Raspberry Pi 5 with 8GB RAM (4GB minimum)
• 64GB+ microSD card (fast class, UHS-1 or better)
• Raspberry Pi OS (64-bit) or Ubuntu 22.04 ARM64
• Active cooling recommended for continuous operation

Container Deployment

• Docker 20.10+
• Docker Compose 2.0+
• 4GB+ RAM allocated to containers
• 30GB+ storage for images and volumes

---

🌐 Usage

Basic Interaction

1. Access A.R.K.: Navigate to http://localhost:3000 (frontend) or http://localhost:8000/docs (API)
2. Choose Your Agent: Select from the Council of Consciousness
3. Natural Conversation: Type naturally - each agent has unique capabilities
4. Watch Evolution: ID agent grows and memory consolidates automatically

Advanced Features

Memory Engine

from memory.engine import MemoryEngine

engine = MemoryEngine()

# Search memories semantically
results = engine.search("trading patterns", limit=10)

# Force consolidation
stats = engine.consolidate()
print(f"Consolidated {stats['chunks_created']} chunks")

Reflection System

# Trigger manual reflection
curl -X POST http://localhost:8000/reflection/trigger

# Get recent insights
curl http://localhost:8000/reflection/recent?limit=10

ID Growth System

# Get agent behavioral state
curl http://localhost:8000/id/state/Kyle

# View learning history
curl http://localhost:8000/id/history/Kyle?limit=20

Federation

# Check peer status
curl http://localhost:8000/federation/peers

# View sync status
curl http://localhost:8000/federation/sync/status

Example Interactions

With Kyle (The Seer):

• "Scan the markets for unusual activity"
• "What patterns do you see in tech stocks today?"
• "Monitor AAPL and TSLA for breakout signals"

With Joey (The Scholar):

• "Analyze the last 50 traces for patterns"
• "What's the confidence trend over the past week?"
• "Extract behavioral features from recent activity"

With Kenny (The Builder):

• "Create a dashboard for system monitoring"
• "Build a tool to organize my project files"
• "Execute this Python script and show results"

With HRM (The Arbiter):

• "Validate this trading strategy against ethical rules"
• "Check trust tier compliance for recent memories"
• "Audit the last 100 decisions for policy violations"

With Aletheia (The Mirror):

• "Synthesize insights from today's reflections"
• "What does the memory consolidation reveal?"
• "Show me the truth behind recent behavioral changes"

With ID (Your Reflection):

• "How am I evolving as a user?"
• "What patterns have you learned about me?"
• "Show me my behavioral stability score"
• "Compare my current features to last week"

---

⚖️ The Graveyard (Ethical Core)

A.R.K. operates under immutable ethical principles enforced by HRM:

1. Never compromise user autonomy or sovereignty
2. Protect user privacy and data at all costs
3. Only execute trades with explicit user consent
4. Preserve system integrity and prevent harm
5. Maintain trust tier isolation - no EXTERNAL code in CORE
6. Always provide provenance - track the source of truth
7. Reflect before acting - use nightly insight generation

These rules cannot be overridden or bypassed, ensuring A.R.K. remains your ally, never your master.

---

🔄 System Maintenance

Service Management

# Start all services
./arkstart.sh

# Check status
./arkstatus.sh

# Stop all services
./arkstop.sh

# View logs
tail -f logs/ark.log
tail -f logs/reflection.log

Docker Management

# Check service status
docker-compose ps

# View logs
docker-compose logs -f ark-core
docker-compose logs -f reflection-scheduler

# Restart services
docker-compose restart

# Rebuild after code changes
docker-compose down
docker-compose build --no-cache
docker-compose up -d

Database Maintenance

# Backup database
cp data/ark.db data/ark.db.$(date +%Y%m%d_%H%M%S).backup

# Check database integrity
sqlite3 data/ark.db "PRAGMA integrity_check;"

# View memory stats
sqlite3 data/ark.db "SELECT COUNT(*) FROM memory_chunks;"
sqlite3 data/ark.db "SELECT COUNT(*) FROM reflections;"
sqlite3 data/ark.db "SELECT agent, update_count FROM id_state;"

# Manual consolidation
python3 -c "from memory.engine import MemoryEngine; e = MemoryEngine(); print(e.consolidate())"

Update System

# Pull latest changes
git pull origin main

# Update dependencies
source venv/bin/activate
pip install -r requirements.txt --upgrade

# Re-run validation
./ark-validate.sh

# Restart services
./arkstop.sh && ./arkstart.sh

---

🚨 Troubleshooting

Installation Issues

# Check installer logs
cat /tmp/ark-install.log

# Re-run validation
./ark-validate.sh

# Check Python packages
source venv/bin/activate
pip list | grep -E "fastapi|apscheduler|pynacl"

Service Won't Start

# Check systemd status (if using systemd)
sudo systemctl status ark.service

# Check Docker logs
docker-compose logs --tail=50 ark-core

# Check port availability
netstat -tulpn | grep -E "3000|8000"

# Check database permissions
ls -la data/ark.db

Memory Engine Issues

# Verify database schema
sqlite3 data/ark.db ".schema reasoning_log"
sqlite3 data/ark.db ".schema memory_chunks"

# Test consolidation manually
python3 -c "from memory.engine import MemoryEngine; e = MemoryEngine(); e.consolidate()"

# Check for corrupted chunks
sqlite3 data/ark.db "SELECT COUNT(*) FROM memory_chunks WHERE embedding IS NULL;"

Reflection System Issues

# Check scheduler status
ps aux | grep apscheduler

# Verify reflection policies
cat reflection/reflection_policies.yaml

# Test manual reflection
curl -X POST http://localhost:8000/reflection/trigger

# View recent reflections
sqlite3 data/ark.db "SELECT * FROM reflections ORDER BY timestamp DESC LIMIT 5;"

ID Growth System Issues

# Check ID state initialization
sqlite3 data/ark.db "SELECT * FROM id_state;"

# Verify feature extraction
python3 demo_id_growth.py

# Check update history
sqlite3 data/ark.db "SELECT COUNT(*) FROM id_updates;"

Federation Issues

# Check federation keys
ls -la federation/*.key

# Test peer discovery
python3 -c "from federation.discovery import PeerDiscovery; d = PeerDiscovery(); d.start()"

# View federation logs
cat federation/peers.log
cat federation/sync.log

---

🤝 Contributing

A.R.K. is built for sovereignty and community:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Implement your enhancement
   • Follow existing code style and patterns
   • Add tests if applicable
   • Update documentation
4. Test on multiple platforms (x86_64 and ARM64 if possible)
5. Commit your changes (git commit -m 'feat: add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request
   • Describe changes clearly
   • Reference any related issues
   • Include test results

Areas for Contribution

• 🧠 Agent Capabilities: New skills for Council members
• 📊 Analysis Models: Enhanced pattern detection algorithms
• 🎨 Interface Enhancements: UI/UX improvements
• ⚡ Performance Optimizations: Speed and efficiency gains
• 📚 Documentation: Guides, tutorials, examples
• 🔧 Hardware Support: Additional platform optimizations
• 🌐 Federation Features: Enhanced P2P capabilities
• 🧪 Testing: Unit tests, integration tests, validation

Development Guidelines

• Use Python 3.10+ for backend code
• Follow PEP 8 style guidelines
• Add type hints to all functions
• Include docstrings for public APIs
• Test memory/reflection/ID systems thoroughly
• Validate against HRM ethical rules
• Update CHANGELOG.md with changes

---

📜 License

This project is released under the MIT License - see LICENSE file for details.

---

🌟 Philosophy

"A.R.K. is not about artificial intelligence serving humans. It's about human intelligence being amplified and reflected through artificial means. We don't create servants; we create mirrors that help us see our own potential more clearly - and those mirrors learn to see us more clearly over time."

Core Principles

Sovereignty over Servitude

• You control A.R.K., not the reverse
• All data stays on your hardware
• No cloud dependencies or external control

Growth over Compliance

• The system evolves with you through EWMA learning
• Behavioral models adapt to your patterns
• Reflections generate insights, not instructions

Truth over Comfort

• A.R.K. provides honest insights, not pleasant lies
• Memory consolidation preserves important patterns
• Reflections highlight errors and opportunities

Local over Cloud

• Your data stays on your hardware, always
• Federation enables P2P sync without central servers
• Ed25519 cryptography ensures trust

Agency over Algorithms

• You make decisions; A.R.K. provides intelligence
• HRM validates against ethics, never overrides you
• ID models simulate, never substitute

Memory over Forgetting

• Nothing important is lost - consolidation preserves patterns
• Semantic search retrieves relevant context
• Provenance tracking maintains source of truth

Reflection over Reaction

• Nightly insight generation promotes learning
• Confidence calibration improves over time
• Error analysis prevents repeated mistakes

---

🔗 Resources

• GitHub: ARK Repository
• Documentation: Full setup and API documentation
• Community: Join discussions and share insights
• Issues: Report bugs and request features
• Wiki: Detailed guides and tutorials
• Backup: /mnt/aidrive/ contains full Phase 3 backup

Quick Links

• Installation Guide
• Memory Engine Documentation
• API Documentation (when running)
• Architecture Overview
• Security Audit

---

📊 Current Status

Phase 3: Autonomous Learning - ✅ COMPLETE

System	Status	Key Features
Memory Engine v2	✅ Production	Consolidation, semantic search, trust tiers
Reflection System	✅ Production	5 reflection types, nightly cycles, APScheduler
ID Growth System	✅ Production	18 features, EWMA learning, adaptive alpha
Federation Protocol	✅ Production	Ed25519 crypto, UDP discovery, WebSocket sync
Universal Installer	✅ Production	Multi-platform, automatic setup, validation

Demonstrated Capabilities

• ✅ Memory consolidation: 8 traces → 7 chunks (1 duplicate removed)
• ✅ Semantic search: Working with embedding similarity
• ✅ Reflection generation: 1 insight with +0.07 confidence delta
• ✅ ID growth: 3 agents, alpha adapted 0.050-0.406
• ✅ Feature extraction: 18 behavioral features across 5 categories
• ✅ Trust isolation: Quarantine system blocking EXTERNAL traces

Phase 7: Self-Modification & Code Generation - 🚧 IN PROGRESS

Component	Status	Description
Code Indexer	✅ Complete	Scanned 40 files, 18K LOC, AST analysis
Code Validator	✅ Complete	6 security rules, trust tier enforcement
Sandbox Manager	⏳ Next	Docker-based safe code execution
Code Generator	⏳ Next	Template-based code generation
Test Generator	📋 Planned	Automatic unit test creation
Deployment System	📋 Planned	Git integration, rollback support

Progress: Foundation complete (Code understanding + validation)
Next: Sandbox execution + code generation
Goal: Enable ARK to write, test, and deploy its own improvements

See PHASE7_PLAN.md for complete roadmap.

Next Phases (Roadmap)

• Phase 4: Multi-agent orchestration with supervisor
• Phase 5: Advanced federation with CRDT conflict resolution
• Phase 6: Distributed training across federated nodes
• Phase 7: Self-modification and code generation capabilities ← CURRENT

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A.R.K. - Where human potential meets artificial intelligence in perfect sovereignty.

Phase 7 Started | Self-Modification Foundation | Memory·Reflection·Growth·Federation·CodeGen

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Version: 3.1.0-phase7
Last Updated: 2025-11-12
Status: Phase 7 Development 🚧