Research-Stack/5-Applications/scripts/llm_directed_topology.py

#!/usr/bin/env python3
"""
LLM-Directed Morphic Topology Analysis
Analyzes morphic scalars that wait on instructions from LLM (E. coli/termite analogy).
"""

import json
from pathlib import Path
from typing import Dict, List, Optional

# Paths
OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")

class LLMDirectedTopology:
    """Analyzes LLM-directed morphic topology where scalars wait on LLM instructions."""
    
    def __init__(self):
        # LLM-directed characteristics
        self.llm_directed_characteristics = {
            "central_llm_control": {
                "description": "Morphic scalars wait on instructions from LLM",
                "significance_score": 95.0
            },
            "ecoli_behavior": {
                "description": "Chemotaxis-like behavior following LLM gradients",
                "significance_score": 85.0
            },
            "termite_behavior": {
                "description": "Collective construction following LLM blueprints",
                "significance_score": 90.0
            },
            "instruction_following": {
                "description": "Precise execution of LLM instructions",
                "significance_score": 95.0
            },
            "coordinated_execution": {
                "description": "Coordinated execution based on LLM coordination",
                "significance_score": 90.0
            }
        }
        
        # Current expansion baseline
        self.current_expansion = {
            "total_devices": 42,
            "immune_system_capacity": 29084505904727.26,
            "expansion_factor": 15307636265.0
        }
    
    def analyze_llm_directed_topology(self) -> Dict:
        """Analyze LLM-directed morphic topology."""
        analysis = {
            "llm_directed_characteristics": self.llm_directed_characteristics,
            "average_significance_score": sum(e["significance_score"] for e in self.llm_directed_characteristics.values()) / len(self.llm_directed_characteristics),
            "analogy_comparison": {
                "ecoli_chemotaxis": "Morphic scalars follow LLM gradients like E. coli follows chemical gradients",
                "termite_construction": "Morphic scalars construct topology following LLM blueprints like termites construct mounds",
                "central_control": "LLM provides central control unlike autonomous immune system",
                "instruction_precision": "Precise instruction following unlike emergent behavior"
            },
            "mechanisms": {
                "llm_instruction": "LLM generates topology instructions",
                "gradient_following": "Scalars follow LLM-generated gradients (E. coli analogy)",
                "blueprint_execution": "Scalars execute LLM-generated blueprints (termite analogy)",
                "coordination": "LLM coordinates scalar behavior",
                "adaptation": "Scalars adapt based on LLM feedback"
            }
        }
        
        return analysis
    
    def analyze_llm_directed_benefits(self) -> Dict:
        """Analyze LLM-directed topology benefits."""
        benefits = {
            "precise_control": {
                "description": "Precise control through LLM instructions",
                "significance_score": 95.0
            },
            "coordinated_behavior": {
                "description": "Coordinated behavior through LLM coordination",
                "significance_score": 90.0
            },
            "adaptive_instructions": {
                "description": "LLM adapts instructions based on system state",
                "significance_score": 95.0
            },
            "scalable_coordination": {
                "description": "LLM can coordinate large numbers of scalars",
                "significance_score": 85.0
            },
            "goal_directed": {
                "description": "Goal-directed behavior through LLM objectives",
                "significance_score": 90.0
            },
            "emergent_plus_directed": {
                "description": "Combines emergent morphic properties with directed LLM control",
                "significance_score": 85.0
            }
        }
        
        return benefits
    
    def calculate_llm_directed_impact(self) -> Dict:
        """Calculate LLM-directed topology impact on computational expansion."""
        # LLM-directed multipliers
        precise_control_multiplier = 2.0  # 2x from precise LLM control
        coordinated_behavior_multiplier = 1.5  # 1.5x from LLM coordination
        adaptive_instructions_multiplier = 1.5  # 1.5x from LLM adaptation
        scalable_coordination_multiplier = 1.3  # 1.3x from LLM scalability
        goal_directed_multiplier = 1.5  # 1.5x from goal-directed behavior
        emergent_plus_directed_multiplier = 1.3  # 1.3x from combined approach
        
        # Calculate expanded capacity with LLM-directed topology
        base_capacity = 1900
        current_immune_system_capacity = 29084505904727.26
        
        # Apply LLM-directed multipliers
        llm_directed_capacity = (current_immune_system_capacity * 
                                precise_control_multiplier * 
                                coordinated_behavior_multiplier * 
                                adaptive_instructions_multiplier * 
                                scalable_coordination_multiplier * 
                                goal_directed_multiplier * 
                                emergent_plus_directed_multiplier)
        
        llm_directed_expansion_factor = llm_directed_capacity / base_capacity
        llm_directed_improvement_factor = llm_directed_capacity / current_immune_system_capacity
        
        calculation = {
            "base_capacity": base_capacity,
            "current_immune_system_capacity": current_immune_system_capacity,
            "precise_control_multiplier": precise_control_multiplier,
            "coordinated_behavior_multiplier": coordinated_behavior_multiplier,
            "adaptive_instructions_multiplier": adaptive_instructions_multiplier,
            "scalable_coordination_multiplier": scalable_coordination_multiplier,
            "goal_directed_multiplier": goal_directed_multiplier,
            "emergent_plus_directed_multiplier": emergent_plus_directed_multiplier,
            "llm_directed_capacity": llm_directed_capacity,
            "llm_directed_expansion_factor": llm_directed_expansion_factor,
            "llm_directed_improvement_factor": llm_directed_improvement_factor,
            "total_llm_directed_multiplier": (precise_control_multiplier * 
                                           coordinated_behavior_multiplier * 
                                           adaptive_instructions_multiplier * 
                                           scalable_coordination_multiplier * 
                                           goal_directed_multiplier * 
                                           emergent_plus_directed_multiplier)
        }
        
        return calculation
    
    def compare_with_immune_system(self) -> Dict:
        """Compare LLM-directed with immune system topology."""
        comparison = {
            "immune_system": {
                "control": "Autonomous, distributed decision-making",
                "intelligence": "Emergent from collective behavior",
                "adaptation": "Automatic based on shared state",
                "strengths": ["Self-organizing", "Robust", "No single point of failure"],
                "weaknesses": ["Unpredictable emergent behavior", "May lack goal direction"]
            },
            "llm_directed": {
                "control": "Centralized LLM instruction following",
                "intelligence": "LLM provides intelligent coordination",
                "adaptation": "LLM adapts instructions based on state",
                "strengths": ["Precise control", "Goal-directed", "Coordinated"],
                "weaknesses": ["LLM bottleneck", "Single point of failure"]
            },
            "hybrid": {
                "description": "Combine both approaches for optimal results",
                "mechanism": "Morphic scalars have autonomous capabilities but follow LLM guidance",
                "benefits": ["Emergent robustness", "Directed goals", "Adaptive coordination"]
            }
        }
        
        return comparison
    
    def integrate_llm_directed_topology(self) -> Dict:
        """Integrate LLM-directed topology into comprehensive analysis."""
        integration = {
            "llm_directed_topology_enabled": True,
            "analogy": "E. coli chemotaxis + termite construction + LLM control",
            "mechanism": "Morphic scalars wait on instructions from LLM",
            "characteristics": 5,
            "benefits": 6,
            "math_categories_enhanced": [
                "Control Theory (LLM control)",
                "Information Theory (instruction following)",
                "Cognitive/Routing (goal-directed)",
                "Thermodynamic (coordinated execution)"
            ],
            "foundation_kernels_enhanced": [
                "F11", "F12"         # Control Theory (LLM control)
            ],
            "hybrid_approach": "Combine LLM-directed with immune system for optimal results"
        }
        
        return integration
    
    def run_analysis(self) -> Dict:
        """Run LLM-directed topology analysis."""
        print("=" * 60)
        print("LLM-DIRECTED MORPHIC TOPOLOGY ANALYSIS")
        print("=" * 60)
        
        # Step 1: Analyze LLM-directed topology
        print("\n[1/4] Analyzing LLM-directed morphic topology (E. coli/termite analogy)...")
        llm_analysis = self.analyze_llm_directed_topology()
        print(f"  LLM-Directed Characteristics: {len(llm_analysis['llm_directed_characteristics'])}")
        for characteristic, details in llm_analysis['llm_directed_characteristics'].items():
            print(f"    {characteristic}: {details['significance_score']}")
        
        # Step 2: Analyze benefits
        print("[2/4] Analyzing LLM-directed topology benefits...")
        benefits = self.analyze_llm_directed_benefits()
        print(f"  Benefits: {len(benefits)}")
        for benefit, details in benefits.items():
            print(f"    {benefit}: {details['significance_score']}")
        
        # Step 3: Calculate impact
        print("[3/4] Calculating LLM-directed topology impact...")
        impact_calculation = self.calculate_llm_directed_impact()
        print(f"  Current Immune System Capacity: {impact_calculation['current_immune_system_capacity']}")
        print(f"  LLM-Directed Capacity: {impact_calculation['llm_directed_capacity']}")
        print(f"  LLM-Directed Improvement Factor: {impact_calculation['llm_directed_improvement_factor']:.2f}x")
        print(f"  Total LLM-Directed Multiplier: {impact_calculation['total_llm_directed_multiplier']:.2f}x")
        
        # Step 4: Compare with immune system
        print("[4/4] Comparing with immune system topology...")
        comparison = self.compare_with_immune_system()
        print(f"  Immune System: {comparison['immune_system']['control']}")
        print(f"  LLM-Directed: {comparison['llm_directed']['control']}")
        print(f"  Hybrid: {comparison['hybrid']['description']}")
        
        print("\n" + "=" * 60)
        print("LLM-DIRECTED MORPHIC TOPOLOGY ANALYSIS COMPLETE")
        print("=" * 60)
        
        return {
            "llm_analysis": llm_analysis,
            "benefits_analysis": benefits,
            "impact_calculation": impact_calculation,
            "comparison": comparison,
            "integration": self.integrate_llm_directed_topology()
        }

if __name__ == '__main__':
    analyzer = LLMDirectedTopology()
    results = analyzer.run_analysis()
    
    # Save results
    output_file = OUTPUT_DIR / "llm_directed_topology.json"
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)
    
    print(f"\nAnalysis results saved to {output_file}")
    
    # Print summary
    print("\n" + "=" * 60)
    print("LLM-DIRECTED TOPOLOGY SUMMARY")
    print("=" * 60)
    print(f"Analogy: E. coli chemotaxis + termite construction + LLM control")
    print(f"LLM-Directed Capacity: {results['impact_calculation']['llm_directed_capacity']}")
    print(f"LLM-Directed Improvement Factor: {results['impact_calculation']['llm_directed_improvement_factor']:.2f}x")
    print(f"Total LLM-Directed Multiplier: {results['impact_calculation']['total_llm_directed_multiplier']:.2f}x")
    print(f"Hybrid Approach: {results['comparison']['hybrid']['description']}")