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version: 2.0.0 date: 2025-03-04 type: system-doc status: public tags: [william, gen, evo] related: [] changelog:

• version: 2.0.0 date: 2025-03-04 changes:
  • "MAJOR: Switch to YAML frontmatter"
  • "MAJOR: Enhanced metadata structure" references: []
• version: 1.0.0 date: 2025-03-03 changes:
  • "MAJOR: Initial documentation" references: []

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GEN-EVO Overview

Overview

The GEN-EVO (Genesis Evolution) system implements WILLIAM's evolution and emergence patterns through a three-stage architecture.

Core Components

1. Stage 1 (SKENAI)

[Raw] → [Initial] → [Basic]
  ↑        ↕          ↓
[Log] ← [Process] ← [Check]

• Raw pattern intake
• Initial emergence
• Basic validation
• Pattern categorization
• Preliminary evolution

2. Stage 2 (SKENAI-Q)

[Deep] → [Quality] → [Validate]
  ↑         ↕           ↓
[Track] ← [Assess] ← [Review]

• Pattern validation
• Quality assessment
• Evolution protocols
• Technical review
• System feedback

3. Stage 3 (SKENAI-R)

[Final] → [Deploy] → [Release]
  ↑         ↕          ↓
[Monitor] ← [Secure] ← [Track]

• Final verification
• Production readiness
• Pattern deployment
• Access management
• System monitoring

Relationship with LEGEND

• LEGEND: Genesis framework (foundational structure)
• GEN-EVO: Evolution framework (emergent adaptation)
• Complementary operation
• No interference with LEGEND systems
• Separate validation mechanisms

AI-Human Collaboration

GEN-EVO emerged from the synthesis of AI and human capabilities:

1. AI Contribution

   • Pattern detection at network scale
   • Real-time adaptation strategies
   • Complex relationship mapping
   • Continuous system optimization

2. Human Contribution

   • Strategic vision and direction
   • Value definition and validation
   • Experience-based insights
   • Creative problem framing

Together, this creates a system where:

• AI identifies patterns and optimizes paths
• Humans guide evolution and validate meaning
• Both contribute to quality measurement
• Natural emergence meets purposeful direction

This collaboration enables GEN-EVO to be both highly efficient and deeply meaningful, combining machine precision with human wisdom.

Integration with NATURAL Framework

• Clean repository separation
• Natural pipeline flow
• Validator protection
• Interface standards

Pipeline API Integration

• /pipeline/submit - Entry point
• /pipeline/validate - Basic checks
• /pipeline/analyze - Efficiency (Q.1)
• /pipeline/patterns - Recognition (Q.2)
• /pipeline/status - State checks
• /pipeline/vote - Governance

Integration with Three-Graph Lattice

• Technical graph validation
• Economic resource optimization
• Quality metrics tracking