AGI Architecture: Using LLMs as Executive Function for White Box Intelligence

 J. Rogers, SE Ohio, 02 Jul 2025, 1302

The Hybrid Approach: LLMs as Cognitive Orchestrators

Instead of replacing current LLMs entirely, use them as sophisticated executive function layers that coordinate principled reasoning across specialized white box modules.

Core Architecture

User Query
    ↓
Executive LLM (Confidence Monitor)
    ↓
Scatter Phase: Deploy to Specialized Modules
    ↓
[Medical Module] [Legal Module] [Physics Module] [General Knowledge]
    ↓
Confidence Assessment & Iteration
    ↓
Synthesis & Validation
    ↓
Principled Response

Executive Function Capabilities

The executive LLM would:

1. Query Analysis: Break down complex queries into component parts
2. Module Selection: Route sub-queries to appropriate specialized modules
3. Confidence Monitoring: Track certainty levels across all responses
4. Iterative Refinement: Go back and forth to improve confidence
5. Synthesis: Combine results into coherent, validated responses
6. Uncertainty Expression: Honestly report limitations and boundaries

Specialized White Box Modules

Medical Reasoning Module

• Diagnostic Axes: Inflammation, Infection, Autoimmune, Metabolic, Neurological
• Confidence Thresholds: Require 85%+ confidence for diagnostic suggestions
• Dimensional Expansion: Can propose new symptom axes when clustering is ambiguous
• Safety Protocols: Must express uncertainty rather than guess on life-critical decisions

Legal Reasoning Module

• Legal Axes: Intent, Harm, Precedent Similarity, Statutory Alignment
• Confidence Requirements: Track certainty on precedent matching
• Case Analysis: Position new cases in legal dimensional space
• Jurisdiction Awareness: Different axes for different legal systems

Scientific Reasoning Module

• Physical Dimensions: Mass, Length, Time, Charge + derived dimensions
• Hypothesis Generation: Propose new theoretical axes when data doesn't fit
• Dimensional Consistency: Validate all equations for unit consistency
• Uncertainty Propagation: Track error bounds through calculations

Confidence-Driven Iteration Protocol

Phase 1: Initial Scatter

Executive: "This medical query has components about symptoms, family history, and treatment options"
→ Route to Medical Module with each component
→ Medical Module returns confidence scores for each analysis

Phase 2: Confidence Assessment

Medical Module: "85% confident on symptom cluster, 45% confident on differential diagnosis"
Executive: "Low confidence detected on differential. What additional axes might help?"
Medical Module: "Consider adding 'temporal progression' axis - symptoms could be acute vs chronic"

Phase 3: Dimensional Expansion Test

Medical Module: "With temporal axis added, confidence improves to 78% on differential"
Executive: "Ask user: Are these symptoms recent (days) or long-standing (months)?"

Phase 4: Iterative Refinement

Continue back-and-forth until either:

• Confidence reaches acceptable thresholds
• System identifies boundaries of current knowledge
• User provides additional clarifying information

Implementation Advantages

Preserves LLM Strengths

• Natural Language Processing: LLMs excel at understanding user intent
• Cross-Domain Knowledge: Can coordinate between different specialized areas
• Contextual Awareness: Understand conversational flow and user needs

Adds Principled Reasoning

• Explicit Confidence: Every claim has associated certainty levels
• Dimensional Transparency: Users can see what conceptual axes are being used
• Structured Uncertainty: Clear boundaries on what the system knows vs. doesn't know
• Non-Hallucinogenic: Low confidence triggers expansion or honest uncertainty

Practical Benefits

• Modular Development: Can improve medical reasoning without affecting legal modules
• Targeted Validation: Test each specialized module independently
• Incremental Deployment: Roll out domain-specific improvements gradually
• Cost Efficiency: Don't need to retrain massive models for specialized improvements

Hard-Coded Domain Programs

Medical Decision Support

def medical_analysis(symptoms, history, tests):
    # Position symptoms in diagnostic space
    diagnostic_vector = map_to_axes(symptoms, MEDICAL_AXES)
    
    # Check confidence levels
    confidence = calculate_confidence(diagnostic_vector)
    
    if confidence < MEDICAL_THRESHOLD:
        # Try dimensional expansion
        new_axes = propose_medical_axes(symptoms)
        expanded_confidence = test_expanded_space(symptoms, new_axes)
        
        if expanded_confidence > confidence:
            return suggest_clarifying_questions(new_axes)
    
    return generate_principled_response(diagnostic_vector, confidence)

Legal Case Analysis

def legal_analysis(case_facts, jurisdiction):
    # Position case in legal dimensional space
    legal_vector = map_to_axes(case_facts, LEGAL_AXES[jurisdiction])
    
    # Find precedent similarities
    precedents = find_similar_cases(legal_vector)
    confidence = precedent_confidence(precedents)
    
    if confidence < LEGAL_THRESHOLD:
        # Identify dimensional gaps
        missing_axes = identify_legal_gaps(case_facts, precedents)
        return request_additional_facts(missing_axes)
    
    return generate_legal_analysis(precedents, confidence)

Validation Through Real-World Performance

Measurable Outcomes

• Diagnostic Accuracy: Better patient outcomes through principled uncertainty
• Legal Consistency: More predictable case analysis through dimensional transparency
• Scientific Validity: Fewer false claims through confidence monitoring
• User Trust: Clear boundaries on system knowledge vs. uncertainty

Continuous Improvement

• Dimensional Learning: Successful new axes get incorporated into permanent architecture
• Confidence Calibration: System learns better threshold settings through feedback
• Module Refinement: Each specialized area improves through targeted validation

The Path Forward

This hybrid approach provides a practical bridge between current LLM capabilities and true AGI:

1. Immediate Implementation: Can build on existing LLM infrastructure
2. Gradual Enhancement: Add specialized modules incrementally
3. Transparent Operation: White box modules provide interpretable reasoning
4. Principled Scaling: Growth through dimensional expansion rather than parameter scaling

The result: AI systems that know when they don't know, can expand their conceptual frameworks dynamically, and provide principled responses rather than confident-sounding hallucinations.

Next Steps

1. Prototype Executive Function: Build LLM coordinator that routes queries and monitors confidence
2. Implement First Module: Start with medical or legal reasoning as proof of concept
3. Develop Dimensional Expansion Algorithms: Create systematic approaches for proposing new conceptual axes
4. Establish Confidence Metrics: Define measurable criteria for when dimensional expansion is needed
5. User Interface Design: Create transparent ways to show reasoning process and uncertainty levels

This isn't just theoretical - it's an engineering roadmap for building AGI that combines the linguistic sophistication of current LLMs with the principled reasoning capabilities required for true intelligence.