Training System - skenai/WILL GitHub Wiki

version: 2.0.0 date: 2025-03-04 type: system-doc status: public tags: [william, training, system] 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: []

WILL Training System

Overview

WILL's training system enables continuous learning and adaptation through pattern recognition, deep integration, and knowledge sharing. The system is designed to evolve naturally while maintaining consistency in interactions.

Core Components

1. Pattern Recognition

Recognition = {
    Input: "User Reference",
    Process: "Pattern Match",
    Output: "Understanding"
}

2. Deep Integration

Integration = {
    Input: "Understanding",
    Process: "System Link",
    Output: "Integration"
}

3. Knowledge Evolution

Evolution = {
    Input: "Integration",
    Process: "Growth",
    Output: "Wisdom"
}

Training Process

1. Active Listening

  • Key reference detection
  • Context understanding
  • Pattern tracking
  • Real-time adaptation

2. Deep Integration

  • System connectivity
  • Pattern application
  • Knowledge growth
  • Feedback loops

3. Natural Evolution

  • Continuous learning
  • Organic growth
  • Wisdom sharing
  • Pattern refinement

Reference Patterns

1. Input Processing

Training = {
    Input: {
        Listen: "User References",
        Track: "Key Patterns",
        Store: "Deep Understanding"
    },
    Process: {
        Analyze: "Pattern Meaning",
        Connect: "System Application",
        Learn: "Deep Integration"
    },
    Output: {
        Apply: "Pattern Usage",
        Grow: "System Evolution",
        Share: "Knowledge Transfer"
    }
}

2. Learning Cycles

  • Pattern recognition
  • Context integration
  • Knowledge application
  • System evolution

Implementation Guidelines

1. Pattern Integration

  • Maintain consistency
  • Ensure relevance
  • Track effectiveness
  • Adapt as needed

2. Knowledge Management

  • Structured storage
  • Efficient retrieval
  • Regular updates
  • Quality control

Related Pages

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