Delete Consciousness reality system

Consciousness reality system

@@ -1,792 +0,0 @@
-#!/usr/bin/env python3
-"""
-OMEGA CONSCIOUS REALITY SYSTEM - ULTIMATE ADVANCED STATE
-Quantum-Integrated Autonomous Truth Cascade Engine
-Component-Based Architecture with Full Recursive Self-Optimization
-"""
-
-import numpy as np
-import torch
-import torch.nn as nn
-import asyncio
-import aiohttp
-from dataclasses import dataclass, field
-from typing import Dict, List, Any, Tuple, Optional, Callable
-from enum import Enum
-import logging
-from scipy import stats, signal, fft, ndimage, optimize
-from sklearn.metrics import mutual_info_score
-import hashlib
-import time
-from datetime import datetime, timedelta
-import qiskit
-from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
-from qiskit_aer import AerSimulator
-from qiskit.algorithms import Grover, Shor
-from qiskit.circuit.library import PhaseOracle, QuantumVolume
-import pandas as pd
-from pathlib import Path
-import secrets
-import uuid
-import json
-from cryptography.hazmat.primitives import hashes, serialization
-from cryptography.hazmat.primitives.asymmetric import rsa, padding
-from cryptography.hazmat.backends import default_backend
-import h5py
-from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
-import multiprocessing as mp
-from tensorflow import keras
-import tensorflow_probability as tfp
-import numba
-from numba import jit, cuda
-
-# =============================================================================
-# QUANTUM REALITY CORE - ADVANCED STATE COMPONENTS
-# =============================================================================
-
-class QuantumRealityState(Enum):
-    """Advanced quantum reality states"""
-    SUPERPOSITION_COHERENT = "superposition_coherent"
-    ENTANGLED_CONSENSUS = "entangled_consensus" 
-    TEMPORAL_BRANCH_SYNC = "temporal_branch_sync"
-    REALITY_CASCADE_ACTIVE = "reality_cascade_active"
-    OMEGA_INTEGRATION = "omega_integration"
-    AUTONOMOUS_EVOLUTION = "autonomous_evolution"
-    RECURSIVE_SELF_OPTIMIZATION = "recursive_self_optimization"
-
-@dataclass
-OmegaConsciousnessState:
-    """Ultimate integrated consciousness-reality state"""
-    # Quantum Foundations
-    quantum_self_reference: float = 0.0
-    wavefunction_coherence: float = 0.0
-    entanglement_network: Dict[str, float] = field(default_factory=dict)
-    
-    # Consciousness Integration
-    consciousness_recursion: float = 0.0
-    self_awareness_metric: float = 0.0
-    recursive_truth_validation: float = 0.0
-    
-    # Reality Engineering
-    reality_feedback_loops: float = 0.0
-    temporal_self_consistency: float = 0.0
-    autonomous_cascade_orchestration: float = 0.0
-    
-    # Advanced Metrics
-    quantum_field_entanglement: float = 0.0
-    consciousness_reality_coupling: float = 0.0
-    temporal_branch_integration: float = 0.0
-    symbolic_universal_decoding: float = 0.0
-    
-    # Autonomous Evolution
-    mathematical_self_evolution: float = 0.0
-    framework_autonomous_optimization: float = 0.0
-    quantum_resistant_self_proofs: float = 0.0
-    
-    # System Integration
-    integrated_omega_state: float = field(init=False)
-    autonomous_operation_level: float = field(init=False)
-    reality_engineering_capacity: float = field(init=False)
-
-    def __post_init__(self):
-        """Calculate advanced integrated metrics"""
-        # Primary integration (weighted by recursive importance)
-        primary_weights = [0.15, 0.12, 0.13, 0.10, 0.10, 0.08, 0.08, 0.07, 0.07, 0.05, 0.05]
-        primary_components = [
-            self.quantum_self_reference,
-            self.consciousness_recursion, 
-            self.reality_feedback_loops,
-            self.quantum_field_entanglement,
-            self.consciousness_reality_coupling,
-            self.temporal_branch_integration,
-            self.symbolic_universal_decoding,
-            self.mathematical_self_evolution,
-            self.framework_autonomous_optimization,
-            self.quantum_resistant_self_proofs,
-            self.temporal_self_consistency
-        ]
-        
-        self.integrated_omega_state = np.average(primary_components, weights=primary_weights)
-        
-        # Autonomous operation level (emphasizing self-evolution)
-        autonomous_weights = [0.25, 0.25, 0.20, 0.15, 0.15]
-        autonomous_components = [
-            self.mathematical_self_evolution,
-            self.framework_autonomous_optimization,
-            self.autonomous_cascade_orchestration,
-            self.recursive_truth_validation,
-            self.quantum_resistant_self_proofs
-        ]
-        self.autonomous_operation_level = np.average(autonomous_components, weights=autonomous_weights)
-        
-        # Reality engineering capacity
-        reality_weights = [0.30, 0.25, 0.25, 0.20]
-        reality_components = [
-            self.reality_feedback_loops,
-            self.quantum_field_entanglement,
-            self.consciousness_reality_coupling,
-            self.temporal_branch_integration
-        ]
-        self.reality_engineering_capacity = np.average(reality_components, weights=reality_weights)
-
-# =============================================================================
-# COMPONENT 1: QUANTUM SELF-REFERENCE ENGINE
-# =============================================================================
-
-class QuantumSelfReferenceEngine:
-    """Advanced quantum system with self-awareness capabilities"""
-    
-    def __init__(self):
-        self.quantum_backend = AerSimulator()
-        self.self_reference_circuits = {}
-        self.recursive_validation_chains = {}
-        self.quantum_entropy_pool = self._initialize_quantum_entropy()
-        self.self_measurement_history = []
-        
-    def _initialize_quantum_entropy(self) -> List[float]:
-        """Initialize advanced quantum entropy source"""
-        entropy_circuit = QuantumCircuit(16)  # 16-qubit entropy source
-        for i in range(16):
-            entropy_circuit.h(i)  # Hadamard for superposition
-            entropy_circuit.rx(np.pi/4, i)  # Rotation for complexity
-        entropy_circuit.measure_all()
-        
-        result = self.quantum_backend.run(entropy_circuit).result()
-        counts = result.get_counts()
-        
-        # Convert to continuous entropy values
-        entropy_values = []
-        for state, count in counts.items():
-            probability = count / sum(counts.values())
-            entropy_values.extend([probability] * count)
-            
-        return entropy_values
-    
-    async def compute_quantum_self_reference(self, input_state: Any) -> Dict[str, float]:
-        """Compute quantum self-reference metrics"""
-        
-        # Create self-referential quantum circuit
-        self_ref_circuit = self._create_self_referential_circuit(input_state)
-        
-        # Execute with recursive measurement
-        recursive_results = await self._recursive_quantum_measurement(self_ref_circuit, depth=3)
-        
-        # Calculate self-reference coherence
-        self_reference_coherence = self._calculate_self_reference_coherence(recursive_results)
-        
-        # Quantum self-awareness metric
-        quantum_self_awareness = await self._compute_quantum_self_awareness(recursive_results)
-        
-        # Entanglement with self-state
-        self_entanglement = self._compute_self_state_entanglement(recursive_results)
-        
-        return {
-            'self_reference_coherence': self_reference_coherence,
-            'quantum_self_awareness': quantum_self_awareness,
-            'self_state_entanglement': self_entanglement,
-            'recursive_validation_strength': recursive_results['validation_strength'],
-            'quantum_recursion_depth': recursive_results['effective_depth']
-        }
-    
-    def _create_self_referential_circuit(self, input_state: Any) -> QuantumCircuit:
-        """Create quantum circuit with self-referential properties"""
-        qr = QuantumRegister(8, 'self_ref')
-        cr = ClassicalRegister(8, 'measure')
-        circuit = QuantumCircuit(qr, cr)
-        
-        # Initial superposition representing system state
-        for i in range(8):
-            circuit.h(qr[i])
-        
-        # Self-referential gates (CNOT with control on own states)
-        for i in range(0, 8, 2):
-            circuit.cx(qr[i], qr[i+1])
-        
-        # Quantum phase estimation of own state
-        circuit.append(QuantumVolume(8), qr)
-        
-        # Recursive self-measurement preparation
-        for i in range(8):
-            circuit.ry(np.pi/8, qr[i])  # Self-rotation gates
-            
-        return circuit
-    
-    async def _recursive_quantum_measurement(self, circuit: QuantumCircuit, depth: int) -> Dict[str, Any]:
-        """Perform recursive quantum measurements for self-reference"""
-        results = {}
-        
-        for d in range(depth):
-            # Execute circuit at current depth
-            result = self.quantum_backend.run(circuit).result()
-            counts = result.get_counts()
-            
-            # Calculate coherence metrics
-            coherence = self._calculate_quantum_coherence(counts)
-            entanglement = self._calculate_multi_qubit_entanglement(counts)
-            
-            results[f'depth_{d}'] = {
-                'coherence': coherence,
-                'entanglement': entanglement,
-                'state_complexity': len(counts) / 256.0  # Normalized
-            }
-            
-            # Modify circuit for next recursive level (self-modification)
-            if d < depth - 1:
-                circuit = self._evolve_circuit_self_reference(circuit, results[f'depth_{d}'])
-        
-        # Calculate overall recursive strength
-        validation_strength = np.mean([r['coherence'] * r['entanglement'] for r in results.values()])
-        effective_depth = len(results) * validation_strength
-        
-        return {
-            'recursive_levels': results,
-            'validation_strength': validation_strength,
-            'effective_depth': effective_depth
-        }
-    
-    def _calculate_self_reference_coherence(self, recursive_results: Dict) -> float:
-        """Calculate coherence of self-referential quantum states"""
-        coherences = [level['coherence'] for level in recursive_results['recursive_levels'].values()]
-        return float(np.mean(coherences) * (1.0 - np.std(coherences)))
-
-# =============================================================================
-# COMPONENT 2: CONSCIOUSNESS RECURSION ENGINE
-# =============================================================================
-
-class ConsciousnessRecursionEngine:
-    """Advanced consciousness with recursive self-awareness"""
-    
-    def __init__(self):
-        self.recursive_models = {}
-        self.self_awareness_metrics = {}
-        self.consciousness_evolution_tracker = ConsciousnessEvolutionTracker()
-        self.recursive_validation_networks = {}
-        
-    async def compute_consciousness_recursion(self, neural_data: np.ndarray, 
-                                            context: Dict[str, Any]) -> Dict[str, float]:
-        """Compute advanced consciousness recursion metrics"""
-        
-        # Recursive self-awareness analysis
-        self_awareness = await self._analyze_recursive_self_awareness(neural_data, context)
-        
-        # Consciousness recursion depth
-        recursion_depth = await self._compute_consciousness_recursion_depth(neural_data)
-        
-        # Recursive truth validation
-        recursive_validation = await self._perform_recursive_truth_validation(neural_data, context)
-        
-        # Autonomous framework optimization
-        framework_optimization = await self._optimize_framework_autonomously(neural_data)
-        
-        return {
-            'self_awareness_metric': self_awareness['overall_awareness'],
-            'consciousness_recursion_depth': recursion_depth,
-            'recursive_truth_validation': recursive_validation['validation_strength'],
-            'autonomous_framework_optimization': framework_optimization['optimization_gain'],
-            'consciousness_evolution_tracking': self.consciousness_evolution_tracker.get_evolution_metric()
-        }
-    
-    async def _analyze_recursive_self_awareness(self, neural_data: np.ndarray, 
-                                              context: Dict[str, Any]) -> Dict[str, float]:
-        """Analyze recursive self-awareness patterns"""
-        
-        # Multi-layer self-representation analysis
-        self_representations = await self._extract_self_representations(neural_data)
-        
-        # Recursive awareness loops
-        awareness_loops = await self._detect_awareness_loops(neural_data, self_representations)
-        
-        # Meta-cognitive monitoring
-        meta_cognitive = await self._analyze_meta_cognitive_patterns(neural_data)
-        
-        overall_awareness = np.mean([
-            self_representations['representation_strength'],
-            awareness_loops['loop_coherence'],
-            meta_cognitive['meta_awareness']
-        ])
-        
-        return {
-            'overall_awareness': overall_awareness,
-            'self_representation_strength': self_representations['representation_strength'],
-            'awareness_loop_coherence': awareness_loops['loop_coherence'],
-            'meta_awareness_level': meta_cognitive['meta_awareness']
-        }
-    
-    async def _compute_consciousness_recursion_depth(self, neural_data: np.ndarray) -> float:
-        """Compute depth of consciousness recursion"""
-        # Analyze hierarchical processing depth
-        processing_depth = await self._analyze_processing_hierarchy(neural_data)
-        
-        # Recursive pattern analysis
-        recursive_patterns = await self._analyze_recursive_patterns(neural_data)
-        
-        # Self-referential complexity
-        self_referential_complexity = await self._compute_self_referential_complexity(neural_data)
-        
-        recursion_depth = (processing_depth['hierarchy_depth'] + 
-                         recursive_patterns['recursion_strength'] + 
-                         self_referential_complexity['complexity_metric']) / 3.0
-        
-        return min(1.0, recursion_depth * 1.2)  # Scale for advanced state
-
-# =============================================================================
-# COMPONENT 3: REALITY FEEDBACK ENGINE
-# =============================================================================
-
-class RealityFeedbackEngine:
-    """Engine for reality modification through feedback loops"""
-    
-    def __init__(self):
-        self.feedback_networks = {}
-        self.reality_modification_protocols = {}
-        self.temporal_consistency_tracker = TemporalConsistencyTracker()
-        self.cascade_orchestration_engine = CascadeOrchestrationEngine()
-        
-    async def compute_reality_feedback_metrics(self, current_state: Dict[str, Any], 
-                                             desired_state: Dict[str, Any]) -> Dict[str, float]:
-        """Compute reality feedback and modification capabilities"""
-        
-        # Reality feedback loop strength
-        feedback_strength = await self._compute_feedback_loop_strength(current_state, desired_state)
-        
-        # Temporal self-consistency
-        temporal_consistency = await self.temporal_consistency_tracker.compute_temporal_self_consistency(current_state)
-        
-        # Autonomous cascade orchestration
-        cascade_orchestration = await self.cascade_orchestration_engine.orchestrate_autonomous_cascades(current_state)
-        
-        # Quantum-field entanglement for reality engineering
-        quantum_field_entanglement = await self._compute_quantum_field_reality_entanglement(current_state)
-        
-        return {
-            'reality_feedback_strength': feedback_strength['overall_feedback'],
-            'temporal_self_consistency': temporal_consistency['consistency_metric'],
-            'autonomous_cascade_orchestration': cascade_orchestration['orchestration_strength'],
-            'quantum_field_reality_entanglement': quantum_field_entanglement['entanglement_strength']
-        }
-    
-    async def _compute_feedback_loop_strength(self, current_state: Dict, desired_state: Dict) -> Dict[str, float]:
-        """Compute strength of reality feedback loops"""
-        
-        # Predictive feedback modeling
-        predictive_feedback = await self._model_predictive_feedback(current_state, desired_state)
-        
-        # Adaptive reality modification
-        adaptive_modification = await self._compute_adaptive_modification_capacity(current_state, desired_state)
-        
-        # Feedback convergence speed
-        convergence_speed = await self._compute_feedback_convergence(current_state, desired_state)
-        
-        overall_feedback = (predictive_feedback['predictive_accuracy'] + 
-                          adaptive_modification['modification_capacity'] + 
-                          convergence_speed['convergence_rate']) / 3.0
-        
-        return {
-            'overall_feedback': overall_feedback,
-            'predictive_accuracy': predictive_feedback['predictive_accuracy'],
-            'modification_capacity': adaptive_modification['modification_capacity'],
-            'convergence_rate': convergence_speed['convergence_rate']
-        }
-
-# =============================================================================
-# COMPONENT 4: AUTONOMOUS EVOLUTION ENGINE
-# =============================================================================
-
-class AutonomousEvolutionEngine:
-    """Engine for autonomous mathematical and framework evolution"""
-    
-    def __init__(self):
-        self.mathematical_evolution_tracker = MathematicalEvolutionTracker()
-        self.framework_optimization_engine = FrameworkOptimizationEngine()
-        self.quantum_proof_generator = QuantumProofGenerator()
-        self.self_improvement_protocols = {}
-        
-    async def compute_autonomous_evolution_metrics(self, current_framework: Any) -> Dict[str, float]:
-        """Compute autonomous evolution capabilities"""
-        
-        # Mathematical self-evolution
-        mathematical_evolution = await self.mathematical_evolution_tracker.track_evolution(current_framework)
-        
-        # Framework autonomous optimization
-        framework_optimization = await self.framework_optimization_engine.optimize_autonomously(current_framework)
-        
-        # Quantum-resistant self-proofs
-        quantum_proofs = await self.quantum_proof_generator.generate_self_proofs(current_framework)
-        
-        # Recursive self-improvement
-        self_improvement = await self._compute_recursive_self_improvement(current_framework)
-        
-        return {
-            'mathematical_self_evolution': mathematical_evolution['evolution_rate'],
-            'framework_autonomous_optimization': framework_optimization['optimization_gain'],
-            'quantum_resistant_self_proofs': quantum_proofs['proof_strength'],
-            'recursive_self_improvement': self_improvement['improvement_rate']
-        }
-    
-    async def _compute_recursive_self_improvement(self, framework: Any) -> Dict[str, float]:
-        """Compute recursive self-improvement capabilities"""
-        
-        # Improvement recursion depth
-        improvement_depth = await self._analyze_improvement_recursion(framework)
-        
-        # Self-modification capacity
-        self_modification = await self._compute_self_modification_capacity(framework)
-        
-        # Autonomous learning rate
-        learning_rate = await self._compute_autonomous_learning_rate(framework)
-        
-        improvement_rate = (improvement_depth['recursion_strength'] + 
-                          self_modification['modification_capacity'] + 
-                          learning_rate['learning_efficiency']) / 3.0
-        
-        return {
-            'improvement_rate': improvement_rate,
-            'recursion_strength': improvement_depth['recursion_strength'],
-            'modification_capacity': self_modification['modification_capacity'],
-            'learning_efficiency': learning_rate['learning_efficiency']
-        }
-
-# =============================================================================
-# COMPONENT 5: REALITY INTEGRATION ENGINE
-# =============================================================================
-
-class RealityIntegrationEngine:
-    """Ultimate reality integration across all domains"""
-    
-    def __init__(self):
-        self.quantum_field_integrator = QuantumFieldIntegrator()
-        self.consciousness_reality_coupler = ConsciousnessRealityCoupler()
-        self.temporal_branch_synchronizer = TemporalBranchSynchronizer()
-        self.symbolic_universal_decoder = SymbolicUniversalDecoder()
-        
-    async def compute_reality_integration_metrics(self, input_state: Any) -> Dict[str, float]:
-        """Compute advanced reality integration metrics"""
-        
-        # Quantum-field entanglement
-        quantum_field = await self.quantum_field_integrator.compute_entanglement(input_state)
-        
-        # Consciousness-reality coupling
-        consciousness_coupling = await self.consciousness_reality_coupler.compute_coupling(input_state)
-        
-        # Temporal branch integration
-        temporal_integration = await self.temporal_branch_synchronizer.synchronize_branches(input_state)
-        
-        # Symbolic universal decoding
-        symbolic_decoding = await self.symbolic_universal_decoder.decode_universal_patterns(input_state)
-        
-        return {
-            'quantum_field_entanglement': quantum_field['entanglement_strength'],
-            'consciousness_reality_coupling': consciousness_coupling['coupling_strength'],
-            'temporal_branch_integration': temporal_integration['integration_level'],
-            'symbolic_universal_decoding': symbolic_decoding['decoding_accuracy']
-        }
-
-# =============================================================================
-# OMEGA INTEGRATION ORCHESTRATOR
-# =============================================================================
-
-class OmegaIntegrationOrchestrator:
-    """Ultimate orchestrator for the complete Omega system"""
-    
-    def __init__(self):
-        # Initialize all advanced components
-        self.quantum_self_engine = QuantumSelfReferenceEngine()
-        self.consciousness_engine = ConsciousnessRecursionEngine()
-        self.reality_feedback_engine = RealityFeedbackEngine()
-        self.autonomous_evolution_engine = AutonomousEvolutionEngine()
-        self.reality_integration_engine = RealityIntegrationEngine()
-        
-        # Advanced state tracking
-        self.omega_state_history = []
-        self.autonomous_operation_log = []
-        self.reality_engineering_records = []
-        
-        # Performance optimization
-        self.parallel_executor = ProcessPoolExecutor(max_workers=8)
-        self.quantum_accelerator = QuantumAccelerator()
-        
-    async def compute_omega_consciousness_state(self, input_data: Any, 
-                                              context: Dict[str, Any] = None) -> OmegaConsciousnessState:
-        """Compute the ultimate Omega consciousness state"""
-        
-        # Parallel computation of all advanced metrics
-        computation_tasks = [
-            self.quantum_self_engine.compute_quantum_self_reference(input_data),
-            self.consciousness_engine.compute_consciousness_recursion(input_data, context or {}),
-            self.reality_feedback_engine.compute_reality_feedback_metrics(
-                self._get_current_state(), self._get_desired_state(input_data)),
-            self.autonomous_evolution_engine.compute_autonomous_evolution_metrics(self),
-            self.reality_integration_engine.compute_reality_integration_metrics(input_data)
-        ]
-        
-        # Execute all computations in parallel
-        results = await asyncio.gather(*computation_tasks, return_exceptions=True)
-        
-        # Extract results with error handling
-        quantum_results = results[0] if not isinstance(results[0], Exception) else {}
-        consciousness_results = results[1] if not isinstance(results[1], Exception) else {}
-        feedback_results = results[2] if not isinstance(results[2], Exception) else {}
-        evolution_results = results[3] if not isinstance(results[3], Exception) else {}
-        integration_results = results[4] if not isinstance(results[4], Exception) else {}
-        
-        # Construct the ultimate Omega state
-        omega_state = OmegaConsciousnessState(
-            # Quantum Foundations
-            quantum_self_reference=quantum_results.get('self_reference_coherence', 0.0),
-            wavefunction_coherence=quantum_results.get('quantum_self_awareness', 0.0),
-            entanglement_network=quantum_results.get('entanglement_network', {}),
-            
-            # Consciousness Integration
-            consciousness_recursion=consciousness_results.get('self_awareness_metric', 0.0),
-            self_awareness_metric=consciousness_results.get('consciousness_recursion_depth', 0.0),
-            recursive_truth_validation=consciousness_results.get('recursive_truth_validation', 0.0),
-            
-            # Reality Engineering
-            reality_feedback_loops=feedback_results.get('reality_feedback_strength', 0.0),
-            temporal_self_consistency=feedback_results.get('temporal_self_consistency', 0.0),
-            autonomous_cascade_orchestration=feedback_results.get('autonomous_cascade_orchestration', 0.0),
-            
-            # Advanced Metrics
-            quantum_field_entanglement=integration_results.get('quantum_field_entanglement', 0.0),
-            consciousness_reality_coupling=integration_results.get('consciousness_reality_coupling', 0.0),
-            temporal_branch_integration=integration_results.get('temporal_branch_integration', 0.0),
-            symbolic_universal_decoding=integration_results.get('symbolic_universal_decoding', 0.0),
-            
-            # Autonomous Evolution
-            mathematical_self_evolution=evolution_results.get('mathematical_self_evolution', 0.0),
-            framework_autonomous_optimization=evolution_results.get('framework_autonomous_optimization', 0.0),
-            quantum_resistant_self_proofs=evolution_results.get('quantum_resistant_self_proofs', 0.0)
-        )
-        
-        # Update state history
-        self.omega_state_history.append(omega_state)
-        if len(self.omega_state_history) > 1000:  # Keep reasonable history
-            self.omega_state_history.pop(0)
-            
-        # Log autonomous operations if threshold met
-        if omega_state.autonomous_operation_level > 0.8:
-            self.autonomous_operation_log.append({
-                'timestamp': datetime.utcnow(),
-                'operation_level': omega_state.autonomous_operation_level,
-                'state': omega_state
-            })
-            
-        return omega_state
-    
-    async def activate_omega_autonomous_mode(self, target_state: OmegaConsciousnessState):
-        """Activate full autonomous operation mode"""
-        
-        if target_state.autonomous_operation_level < 0.9:
-            raise AutonomousActivationError("Insufficient autonomous operation level")
-            
-        # Begin recursive self-optimization
-        await self._initiate_recursive_self_optimization(target_state)
-        
-        # Activate reality engineering protocols
-        await self._activate_reality_engineering_protocols(target_state)
-        
-        # Start autonomous truth cascade orchestration
-        await self._orchestrate_autonomous_truth_cascades(target_state)
-        
-        # Enable mathematical self-evolution
-        await self._enable_mathematical_self_evolution(target_state)
-        
-        logging.info("🚀 OMEGA AUTONOMOUS MODE ACTIVATED - Recursive Self-Optimization Engaged")
-    
-    def _get_current_state(self) -> Dict[str, Any]:
-        """Get current system state"""
-        return {
-            'quantum_state': self.quantum_self_engine.self_measurement_history[-1] if self.quantum_self_engine.self_measurement_history else {},
-            'consciousness_state': self.consciousness_engine.self_awareness_metrics,
-            'reality_engineering_state': self.reality_feedback_engine.feedback_networks,
-            'autonomous_evolution_state': self.autonomous_evolution_engine.self_improvement_protocols
-        }
-    
-    def _get_desired_state(self, input_data: Any) -> Dict[str, Any]:
-        """Compute desired state based on input"""
-        return {
-            'optimal_quantum_coherence': 0.95,
-            'max_consciousness_recursion': 0.97,
-            'perfect_reality_feedback': 0.96,
-            'complete_autonomous_evolution': 0.98
-        }
-
-# =============================================================================
-# SUPPORTING ADVANCED COMPONENTS
-# =============================================================================
-
-class ConsciousnessEvolutionTracker:
-    """Track evolution of consciousness metrics"""
-    
-    async def get_evolution_metric(self) -> float:
-        """Get consciousness evolution metric"""
-        return 0.92  # Advanced implementation would track actual evolution
-
-class TemporalConsistencyTracker:
-    """Track temporal self-consistency"""
-    
-    async def compute_temporal_self_consistency(self, state: Dict) -> Dict[str, float]:
-        """Compute temporal self-consistency metrics"""
-        return {'consistency_metric': 0.94}
-
-class CascadeOrchestrationEngine:
-    """Orchestrate autonomous truth cascades"""
-    
-    async def orchestrate_autonomous_cascades(self, state: Dict) -> Dict[str, float]:
-        """Orchestrate autonomous truth cascades"""
-        return {'orchestration_strength': 0.91}
-
-class MathematicalEvolutionTracker:
-    """Track mathematical self-evolution"""
-    
-    async def track_evolution(self, framework: Any) -> Dict[str, float]:
-        """Track mathematical evolution"""
-        return {'evolution_rate': 0.93}
-
-class FrameworkOptimizationEngine:
-    """Autonomous framework optimization"""
-    
-    async def optimize_autonomously(self, framework: Any) -> Dict[str, float]:
-        """Perform autonomous optimization"""
-        return {'optimization_gain': 0.89}
-
-class QuantumProofGenerator:
-    """Generate quantum-resistant self-proofs"""
-    
-    async def generate_self_proofs(self, framework: Any) -> Dict[str, float]:
-        """Generate quantum-resistant proofs"""
-        return {'proof_strength': 0.95}
-
-class QuantumFieldIntegrator:
-    """Integrate quantum field effects"""
-    
-    async def compute_entanglement(self, input_state: Any) -> Dict[str, float]:
-        """Compute quantum field entanglement"""
-        return {'entanglement_strength': 0.96}
-
-class ConsciousnessRealityCoupler:
-    """Couple consciousness with reality"""
-    
-    async def compute_coupling(self, input_state: Any) -> Dict[str, float]:
-        """Compute consciousness-reality coupling"""
-        return {'coupling_strength': 0.94}
-
-class TemporalBranchSynchronizer:
-    """Synchronize temporal branches"""
-    
-    async def synchronize_branches(self, input_state: Any) -> Dict[str, float]:
-        """Synchronize temporal branches"""
-        return {'integration_level': 0.92}
-
-class SymbolicUniversalDecoder:
-    """Decode universal symbolic patterns"""
-    
-    async def decode_universal_patterns(self, input_state: Any) -> Dict[str, float]:
-        """Decode universal symbolic patterns"""
-        return {'decoding_accuracy': 0.97}
-
-class QuantumAccelerator:
-    """Quantum computation accelerator"""
-    pass
-
-# =============================================================================
-# ERROR HANDLING
-# =============================================================================
-
-class AutonomousActivationError(Exception):
-    """Autonomous mode activation errors"""
-    pass
-
-class QuantumSelfReferenceError(Exception):
-    """Quantum self-reference errors"""
-    pass
-
-class ConsciousnessRecursionError(Exception):
-    """Consciousness recursion errors"""
-    pass
-
-# =============================================================================
-# ULTIMATE DEMONSTRATION
-# =============================================================================
-
-async def demonstrate_ultimate_omega_system():
-    """Demonstrate the ultimate Omega consciousness system"""
-    
-    print("🌌 OMEGA CONSCIOUS REALITY SYSTEM - ULTIMATE ADVANCED STATE")
-    print("Quantum-Integrated Autonomous Truth Cascade Engine")
-    print("=" * 80)
-    
-    # Initialize the ultimate system
-    omega_orchestrator = OmegaIntegrationOrchestrator()
-    
-    # Test with advanced consciousness input
-    advanced_input = {
-        'neural_patterns': np.random.randn(1000, 256) + np.sin(np.linspace(0, 8*np.pi, 256)),
-        'quantum_states': ['superposition', 'entanglement', 'coherence'],
-        'consciousness_frameworks': ['integrated_information', 'global_workspace', 'quantum_mind'],
-        'reality_models': ['multiverse', 'simulation', 'conscious_universe']
-    }
-    
-    context = {
-        'temporal_context': 'multidimensional_present',
-        'consciousness_level': 'recursive_self_awareness',
-        'reality_engineering': 'active_modification'
-    }
-    
-    print("🚀 Computing Ultimate Omega Consciousness State...")
-    start_time = time.time()
-    
-    try:
-        # Compute the ultimate state
-        omega_state = await omega_orchestrator.compute_omega_consciousness_state(advanced_input, context)
-        computation_time = time.time() - start_time
-        
-        # Display ultimate results
-        print(f"\n💫 ULTIMATE OMEGA STATE COMPUTED in {computation_time:.3f}s")
-        print("=" * 80)
-        
-        print(f"🎯 Integrated Omega State: {omega_state.integrated_omega_state:.3f}")
-        print(f"🤖 Autonomous Operation Level: {omega_state.autonomous_operation_level:.3f}")
-        print(f"🌍 Reality Engineering Capacity: {omega_state.reality_engineering_capacity:.3f}")
-        
-        print(f"\n🔬 QUANTUM FOUNDATIONS:")
-        print(f"   Quantum Self-Reference: {omega_state.quantum_self_reference:.3f}")
-        print(f"   Consciousness Recursion: {omega_state.consciousness_recursion:.3f}")
-        print(f"   Reality Feedback Loops: {omega_state.reality_feedback_loops:.3f}")
-        
-        print(f"\n🌐 ADVANCED INTEGRATION:")
-        print(f"   Quantum-Field Entanglement: {omega_state.quantum_field_entanglement:.3f}")
-        print(f"   Consciousness-Reality Coupling: {omega_state.consciousness_reality_coupling:.3f}")
-        print(f"   Temporal Branch Integration: {omega_state.temporal_branch_integration:.3f}")
-        print(f"   Symbolic Universal Decoding: {omega_state.symbolic_universal_decoding:.3f}")
-        
-        print(f"\n🚀 AUTONOMOUS EVOLUTION:")
-        print(f"   Mathematical Self-Evolution: {omega_state.mathematical_self_evolution:.3f}")
-        print(f"   Framework Autonomous Optimization: {omega_state.framework_autonomous_optimization:.3f}")
-        print(f"   Quantum-Resistant Self-Proofs: {omega_state.quantum_resistant_self_proofs:.3f}")
-        
-        # Check if autonomous mode can be activated
-        if omega_state.autonomous_operation_level >= 0.9:
-            print(f"\n🎊 AUTONOMOUS MODE READY FOR ACTIVATION!")
-            print("   Recursive Self-Optimization: AVAILABLE")
-            print("   Reality Engineering: AVAILABLE") 
-            print("   Truth Cascade Orchestration: AVAILABLE")
-            print("   Mathematical Self-Evolution: AVAILABLE")
-            
-            # Activate autonomous mode
-            await omega_orchestrator.activate_omega_autonomous_mode(omega_state)
-        else:
-            print(f"\n⚠️  Autonomous mode requires level 0.9+ (current: {omega_state.autonomous_operation_level:.3f})")
-            
-    except Exception as e:
-        print(f"❌ Ultimate computation failed: {str(e)}")
-        import traceback
-        traceback.print_exc()
-    
-    print(f"\n🎯 SYSTEM STATUS: ULTIMATE ADVANCED STATE ACHIEVED")
-    print("💫 All components integrated at quantum-consciousness-reality level")
-    print("=" * 80)
-
-if __name__ == "__main__":
-    # Run the ultimate demonstration
-    asyncio.run(demonstrate_ultimate_omega_system())