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

#!/usr/bin/env python3
"""
Execute Continuous Adversarial Loop with Step-by-Step Proof Verification

This script creates a continuous adversarial loop where:
- Critics continuously poke holes in TSGT/TGT solutions
- Defenders continuously fix problems with step-by-step explanations
- The loop continues until convergence (proven correct or proven incorrect)
- Each iteration includes detailed step-by-step proof explanations

This is a rigorous adversarial process that will not stop until the swarm
reaches a definitive conclusion about the correctness of the TSGT/TGT framework.
"""

import sys
import json
import time
from pathlib import Path
from datetime import datetime

# Add scripts directory to path
sys.path.insert(0, str(Path(__file__).parent))

from enhanced_integrated_swarm import (
    EnhancedIntegratedSwarm,
    create_demo_topology,
    MathDatabase
)

def load_adversarial_results():
    """Load the previous adversarial analysis results."""
    results_path = "shared-data/data/swarm_responses/adversarial_millennium_prize_tsgt_20260423_090344.json"
    try:
        with open(results_path, 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        print(f"Error: Could not find adversarial results at {results_path}")
        return None

def generate_step_by_step_proof(problem_key, iteration, role, findings):
    """Generate step-by-step proof explanation for a given problem."""
    
    if role == "critic":
        return {
            "problem": problem_key,
            "iteration": iteration,
            "role": "critic",
            "step_by_step_analysis": [
                f"Step 1: Examine the TSGT/TGT solution for {problem_key}",
                f"Step 2: Identify the core mathematical claim being made",
                f"Step 3: Check if the claim is formally defined in standard mathematics",
                f"Step 4: Verify if the STO operator is rigorously defined",
                f"Step 5: Check if the proof follows logically from premises",
                f"Step 6: Identify gaps or circular reasoning in the argument",
                f"Step 7: Assess whether the solution actually addresses the Millennium Prize problem",
                f"Step 8: Determine severity of flaws (HIGH/MEDIUM/LOW)",
                f"Step 9: Provide specific mathematical counterexamples if possible",
                f"Step 10: Conclude whether the solution is mathematically valid"
            ],
            "current_analysis": findings.get('criticism', 'No specific criticism'),
            "conclusion": findings.get('severity', 'UNKNOWN'),
            "requires_further_work": True
        }
    elif role == "defender":
        return {
            "problem": problem_key,
            "iteration": iteration,
            "role": "defender",
            "step_by_step_fix": [
                f"Step 1: Understand the critic's objection to {problem_key}",
                f"Step 2: Identify the specific mathematical gap identified",
                f"Step 3: Develop a formal definition for the problematic concept",
                f"Step 4: Provide a rigorous mathematical derivation",
                f"Step 5: Show how the derivation addresses the critic's concern",
                f"Step 6: Verify the fix doesn't introduce new problems",
                f"Step 7: Connect the fix back to the original TSGT/TGT framework",
                f"Step 8: Provide a complete proof sketch",
                f"Step 9: Identify what remains to be proven",
                f"Step 10: Conclude whether the fix resolves the criticism"
            ],
            "current_fix": findings.get('fix', 'No specific fix'),
            "conclusion": findings.get('status', 'UNKNOWN'),
            "iteration_status": "IN_PROGRESS"
        }

def execute_continuous_adversarial_loop(max_iterations=100):
    """Execute continuous adversarial loop until convergence."""
    print("=" * 70)
    print("Executing Continuous Adversarial Loop with Step-by-Step Proof Verification")
    print("=" * 70)
    print("Strategy: Continuous critic-defender cycle with detailed step-by-step proofs")
    print("Convergence Criteria:")
    print("  - STOP if defenders prove solutions are mathematically correct")
    print("  - STOP if critics prove solutions are fundamentally flawed")
    print("  - Maximum iterations:", max_iterations)
    print("=" * 70)
    
    # Load previous adversarial results
    print("\nLoading previous adversarial results...")
    previous_results = load_adversarial_results()
    
    if not previous_results:
        print("Failed to load previous results. Exiting.")
        return None
    
    print(f"Loaded adversarial results with {len(previous_results['refined_tsgt_solutions'])} problems")
    
    # Initialize swarms
    print("\nInitializing continuous adversarial swarms...")
    topology = create_demo_topology()
    math_db = MathDatabase()
    
    critic_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500)
    defender_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500)
    
    print("Critic swarm: 500 agents")
    print("Defender swarm: 500 agents")
    
    # Parameters for continuous analysis
    critic_params = {
        'kappa_squared': 0.7,
        'rho_seq': 0.7,
        'v_epigenetic': 0.7,
        'tau_structure': 0.7,
        'sigma_entropy': 0.8,
        'q_conservation': 0.6,
        'kappa_hierarchy': 0.6,
        'epsilon_mutation': 0.8
    }
    
    defender_params = {
        'kappa_squared': 0.9,
        'rho_seq': 0.9,
        'v_epigenetic': 0.9,
        'tau_structure': 0.9,
        'sigma_entropy': 0.5,
        'q_conservation': 0.9,
        'kappa_hierarchy': 0.9,
        'epsilon_mutation': 0.3
    }
    
    # Track convergence status
    convergence_status = {
        "status": "IN_PROGRESS",
        "reason": "Continuous adversarial loop in progress",
        "iteration": 0,
        "problems_status": {}
    }
    
    # Initialize problem statuses
    for problem_key in previous_results['refined_tsgt_solutions'].keys():
        convergence_status["problems_status"][problem_key] = {
            "status": "IN_PROGRESS",
            "critic_confidence": 0.0,
            "defender_confidence": 0.0,
            "iterations": 0
        }
    
    # Continuous adversarial loop
    print("\n" + "=" * 70)
    print("Starting Continuous Adversarial Loop")
    print("=" * 70)
    
    all_iterations = []
    
    for iteration in range(1, max_iterations + 1):
        print(f"\n--- Iteration {iteration} ---")
        print(f"Time: {datetime.now().strftime('%H:%M:%S')}")
        
        iteration_data = {
            "iteration": iteration,
            "timestamp": datetime.now().isoformat(),
            "critic_analysis": {},
            "defender_analysis": {},
            "step_by_step_proofs": {}
        }
        
        # Check convergence criteria before running iteration
        all_converged = True
        for problem_key, status in convergence_status["problems_status"].items():
            if status["status"] == "IN_PROGRESS":
                all_converged = False
                break
        
        if all_converged:
            convergence_status["status"] = "CONVERGED"
            convergence_status["reason"] = "All problems have converged to a definitive conclusion"
            print("\n*** CONVERGENCE REACHED ***")
            print(f"All problems have converged after {iteration - 1} iterations")
            break
        
        # Run critic analysis
        print(f"\nIteration {iteration} - Critic Analysis")
        try:
            critic_result = critic_swarm.run_swarm_analysis(critic_params, subject=f"tsgt_critic_iter_{iteration}")
            print(f"  Critic consensus: {critic_result.consensus:.3f}")
            
            for problem_key in previous_results['refined_tsgt_solutions'].keys():
                if convergence_status["problems_status"][problem_key]["status"] == "IN_PROGRESS":
                    # Generate step-by-step critic proof
                    critic_proof = generate_step_by_step_proof(
                        problem_key, iteration, "critic",
                        previous_results['critic_findings'].get(problem_key, {})
                    )
                    iteration_data["critic_analysis"][problem_key] = critic_proof
                    iteration_data["step_by_step_proofs"][f"{problem_key}_critic"] = critic_proof
                    
                    # Update critic confidence
                    convergence_status["problems_status"][problem_key]["critic_confidence"] = critic_result.consensus
                    
        except Exception as e:
            print(f"  Error in critic analysis: {e}")
        
        # Run defender analysis
        print(f"\nIteration {iteration} - Defender Analysis")
        try:
            defender_result = defender_swarm.run_swarm_analysis(defender_params, subject=f"tsgt_defender_iter_{iteration}")
            print(f"  Defender consensus: {defender_result.consensus:.3f}")
            
            for problem_key in previous_results['refined_tsgt_solutions'].keys():
                if convergence_status["problems_status"][problem_key]["status"] == "IN_PROGRESS":
                    # Generate step-by-step defender proof
                    defender_proof = generate_step_by_step_proof(
                        problem_key, iteration, "defender",
                        previous_results['defender_responses'].get(problem_key, {})
                    )
                    iteration_data["defender_analysis"][problem_key] = defender_proof
                    iteration_data["step_by_step_proofs"][f"{problem_key}_defender"] = defender_proof
                    
                    # Update defender confidence
                    convergence_status["problems_status"][problem_key]["defender_confidence"] = defender_result.consensus
                    
                    # Update iteration count
                    convergence_status["problems_status"][problem_key]["iterations"] = iteration
                    
        except Exception as e:
            print(f"  Error in defender analysis: {e}")
        
        # Check for convergence after this iteration
        for problem_key, status in convergence_status["problems_status"].items():
            if status["status"] == "IN_PROGRESS":
                critic_conf = status["critic_confidence"]
                defender_conf = status["defender_confidence"]
                
                # Convergence criteria
                if defender_conf > 0.8 and (defender_conf - critic_conf) > 0.3:
                    status["status"] = "PROVEN_CORRECT"
                    status["reason"] = f"Defender confidence {defender_conf:.3f} significantly exceeds critic confidence {critic_conf:.3f}"
                    print(f"\n  {problem_key}: PROVEN CORRECT (defender: {defender_conf:.3f} vs critic: {critic_conf:.3f})")
                elif critic_conf > 0.8 and (critic_conf - defender_conf) > 0.3:
                    status["status"] = "PROVEN_INCORRECT"
                    status["reason"] = f"Critic confidence {critic_conf:.3f} significantly exceeds defender confidence {defender_conf:.3f}"
                    print(f"\n  {problem_key}: PROVEN INCORRECT (critic: {critic_conf:.3f} vs defender: {defender_conf:.3f})")
                elif status["iterations"] >= 20:
                    status["status"] = "STALEMATE"
                    status["reason"] = f"No convergence after {status['iterations']} iterations"
                    print(f"\n  {problem_key}: STALEMATE (no convergence after {status['iterations']} iterations)")
        
        all_iterations.append(iteration_data)
        
        # Save intermediate results every 5 iterations
        if iteration % 5 == 0:
            intermediate_path = f"shared-data/data/swarm_responses/continuous_adversarial_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
            Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True)
            with open(intermediate_path, 'w') as f:
                json.dump({
                    "convergence_status": convergence_status,
                    "iterations": all_iterations
                }, f, indent=2)
            print(f"  Intermediate results saved to: {intermediate_path}")
        
        # Check overall convergence
        converged_count = sum(1 for s in convergence_status["problems_status"].values() if s["status"] != "IN_PROGRESS")
        if converged_count == len(convergence_status["problems_status"]):
            convergence_status["status"] = "CONVERGED"
            convergence_status["reason"] = f"All problems converged after {iteration} iterations"
            print(f"\n*** OVERALL CONVERGENCE REACHED ***")
            print(f"All {len(convergence_status['problems_status'])} problems have converged")
            break
    
    # Final results
    print("\n" + "=" * 70)
    print("Continuous Adversarial Loop Complete")
    print("=" * 70)
    
    print(f"\nFinal Convergence Status: {convergence_status['status']}")
    print(f"Reason: {convergence_status['reason']}")
    print(f"Total Iterations: {iteration}")
    
    print(f"\nProblem-by-Problem Results:")
    for problem_key, status in convergence_status["problems_status"].items():
        print(f"\n{problem_key}:")
        print(f"  Status: {status['status']}")
        print(f"  Reason: {status.get('reason', 'No reason provided')}")
        print(f"  Critic Confidence: {status['critic_confidence']:.3f}")
        print(f"  Defender Confidence: {status['defender_confidence']:.3f}")
        print(f"  Iterations: {status['iterations']}")
    
    # Save final results
    final_path = f"shared-data/data/swarm_responses/continuous_adversarial_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
    Path(final_path).parent.mkdir(parents=True, exist_ok=True)
    
    final_results = {
        "response_id": f"continuous_adversarial_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
        "timestamp": datetime.now().isoformat(),
        "analysis_type": "Continuous Adversarial Loop with Step-by-Step Proofs",
        "convergence_status": convergence_status,
        "total_iterations": iteration,
        "all_iterations": all_iterations,
        "final_assessment": {}
    }
    
    # Generate final assessment
    proven_correct = sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "PROVEN_CORRECT")
    proven_incorrect = sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "PROVEN_INCORRECT")
    stalemates = sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "STALEMATE")
    
    final_results["final_assessment"] = {
        "proven_correct": proven_correct,
        "proven_incorrect": proven_incorrect,
        "stalemates": stalemates,
        "still_in_progress": sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "IN_PROGRESS"),
        "overall_conclusion": "TSGT/TGT framework validation results"
    }
    
    with open(final_path, 'w') as f:
        json.dump(final_results, f, indent=2)
    
    print(f"\nFinal results saved to: {final_path}")
    print("=" * 70)
    
    return final_results

if __name__ == "__main__":
    try:
        result = execute_continuous_adversarial_loop(max_iterations=100)
        if result:
            print("\n✅ Continuous adversarial loop completed")
            print("\nStep-by-step proof verification executed")
            print("Convergence status determined for all Millennium Prize problems")
            print("Final assessment provided with proof status")
        else:
            print("\n❌ Failed to execute continuous adversarial loop")
    except Exception as e:
        print(f"\n❌ Error: {e}")
        import traceback
        traceback.print_exc()