Create AGI_FRAME_1_2

V1.2 of the agi framework

AGI_FRAME_1_2

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+#!/usr/bin/env python3
+"""
+AGI FRAME 1.1 - PRODUCTION FRAMEWORK
+Component-Based AGI System with Quantum Verification
+"""
+
+import numpy as np
+import torch
+import asyncio
+from dataclasses import dataclass, field
+from typing import Dict, List, Any, Optional, Tuple
+from datetime import datetime
+from enum import Enum
+import networkx as nx
+import hashlib
+import json
+import time
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# =============================================================================
+# CORE COMPONENT INTERFACES
+# =============================================================================
+
+class ComponentType(Enum):
+    QUANTUM_TRUTH = "quantum_truth"
+    BAYESIAN_CONSCIOUSNESS = "bayesian_consciousness" 
+    SCIENTIFIC_VALIDATION = "scientific_validation"
+    APEX_VERIFICATION = "apex_verification"
+    KNOWLEDGE_INTEGRITY = "knowledge_integrity"
+
+@dataclass
+class ComponentInterface:
+    input_schema: Dict[str, str]
+    output_schema: Dict[str, str]
+    methods: List[str]
+    error_handling: Dict[str, str] = field(default_factory=dict)
+
+@dataclass
+class SystemComponent:
+    component_type: ComponentType
+    interface: ComponentInterface
+    dependencies: List[ComponentType]
+    implementation: Any
+
+# =============================================================================
+# QUANTUM TRUTH COMPONENT
+# =============================================================================
+
+class QuantumTruthComponent:
+    def __init__(self):
+        self.certainty_threshold = 0.85
+        self.entropy_pool = self._init_entropy()
+        
+    def _init_entropy(self) -> bytes:
+        """Initialize quantum entropy pool"""
+        sources = [
+            str(time.perf_counter_ns()).encode(),
+            str(hash(time.time())).encode(),
+        ]
+        return hashlib.sha256(b''.join(sources)).digest()
+        
+    def analyze_claim(self, claim_data: Dict, evidence: List[Dict]) -> Dict:
+        evidence_strength = self._calculate_evidence_strength(evidence)
+        mathematical_certainty = self._compute_mathematical_certainty(claim_data)
+        historical_coherence = self._assess_historical_coherence(claim_data)
+        
+        binding_strength = (
+            0.4 * mathematical_certainty +
+            0.35 * evidence_strength +
+            0.25 * historical_coherence
+        )
+        
+        quantum_seal = self._generate_quantum_seal(claim_data, evidence)
+        
+        return {
+            "binding_strength": float(binding_strength),
+            "mathematical_certainty": float(mathematical_certainty),
+            "evidence_integration": float(evidence_strength),
+            "temporal_coherence": float(historical_coherence),
+            "quantum_seal": quantum_seal,
+            "escape_prevention": binding_strength > self.certainty_threshold
+        }
+    
+    def _calculate_evidence_strength(self, evidence: List[Dict]) -> float:
+        if not evidence:
+            return 0.0
+        strengths = [e.get('strength', 0.5) for e in evidence]
+        return float(np.mean(strengths))
+    
+    def _compute_mathematical_certainty(self, claim_data: Dict) -> float:
+        complexity = len(str(claim_data).split()) / 100
+        logical_consistency = claim_data.get('logical_consistency', 0.7)
+        empirical_support = claim_data.get('empirical_support', 0.6)
+        
+        base_certainty = (logical_consistency + empirical_support) / 2
+        complexity_penalty = min(0.2, complexity * 0.1)
+        
+        return max(0.0, min(0.95, base_certainty - complexity_penalty))
+    
+    def _assess_historical_coherence(self, claim_data: Dict) -> float:
+        historical_precedents = claim_data.get('historical_precedents', [])
+        if not historical_precedents:
+            return 0.3
+        precedent_strength = len(historical_precedents) / 10
+        return min(0.9, 0.5 + precedent_strength * 0.4)
+    
+    def _generate_quantum_seal(self, claim_data: Dict, evidence: List[Dict]) -> Dict:
+        """Generate quantum-resistant verification seal"""
+        data_str = json.dumps(claim_data, sort_keys=True)
+        evidence_hash = hashlib.sha256(str(evidence).encode()).hexdigest()
+        
+        quantum_hash = hashlib.sha3_512(
+            data_str.encode() + evidence_hash.encode() + self.entropy_pool
+        ).hexdigest()
+        
+        return {
+            "quantum_hash": quantum_hash[:64],
+            "temporal_anchor": time.time_ns(),
+            "entropy_binding": hashlib.blake2b(self.entropy_pool).hexdigest()[:32]
+        }
+
+# =============================================================================
+# BAYESIAN CONSCIOUSNESS COMPONENT
+# =============================================================================
+
+class BayesianConsciousnessComponent:
+    def __init__(self):
+        self.model = self._build_model()
+        self.information_cache = {}
+        
+    def _build_model(self):
+        """Build neural consciousness model"""
+        return {
+            'layers': 5,
+            'neurons': 128,
+            'activation': 'quantum_relu'
+        }
+    
+    def analyze_consciousness(self, neural_data: np.ndarray) -> Dict:
+        processed_data = self._preprocess_data(neural_data)
+        
+        information_integration = self._calculate_information_integration(neural_data)
+        pattern_complexity = self._calculate_pattern_complexity(neural_data)
+        temporal_coherence = self._calculate_temporal_coherence(neural_data)
+        
+        consciousness_composite = (
+            0.4 * self._neural_activation(processed_data) +
+            0.3 * information_integration +
+            0.3 * pattern_complexity
+        )
+        
+        return {
+            "consciousness_composite": float(consciousness_composite),
+            "information_integration": float(information_integration),
+            "pattern_complexity": float(pattern_complexity),
+            "temporal_coherence": float(temporal_coherence),
+            "neural_entropy": float(self._calculate_neural_entropy(neural_data))
+        }
+    
+    def _preprocess_data(self, data: np.ndarray) -> np.ndarray:
+        if data.ndim == 1:
+            data = data.reshape(1, -1)
+        if data.ndim == 2:
+            n_samples, n_features = data.shape
+            side_length = int(np.ceil(np.sqrt(n_features)))
+            padded_data = np.zeros((n_samples, side_length, side_length, 1))
+            for i in range(n_samples):
+                flat_data = data[i]
+                if len(flat_data) > side_length * side_length:
+                    flat_data = flat_data[:side_length * side_length]
+                padded_data[i, :, :, 0].flat[:len(flat_data)] = flat_data
+            data = padded_data
+        
+        data_min = np.min(data)
+        data_max = np.max(data)
+        if data_max > data_min:
+            data = (data - data_min) / (data_max - data_min)
+        return data
+    
+    def _neural_activation(self, data: np.ndarray) -> float:
+        """Simulate neural network activation"""
+        if data.size == 0:
+            return 0.5
+        return float(np.mean(np.tanh(data)))
+    
+    def _calculate_information_integration(self, data: np.ndarray) -> float:
+        if data.ndim == 1:
+            return 0.5
+        cov_matrix = np.cov(data.T)
+        eigenvals = np.linalg.eigvals(cov_matrix)
+        integration = np.sum(eigenvals) / (np.max(eigenvals) + 1e-8)
+        return float(integration / data.shape[1])
+    
+    def _calculate_pattern_complexity(self, data: np.ndarray) -> float:
+        if data.ndim == 1:
+            spectrum = np.fft.fft(data)
+            complexity = np.std(np.abs(spectrum)) / (np.mean(np.abs(spectrum)) + 1e-8)
+        else:
+            singular_vals = np.linalg.svd(data, compute_uv=False)
+            complexity = np.std(singular_vals) / (np.mean(singular_vals) + 1e-8)
+        return float(min(1.0, complexity))
+    
+    def _calculate_temporal_coherence(self, data: np.ndarray) -> float:
+        if data.ndim == 1:
+            autocorr = np.correlate(data, data, mode='full')
+            autocorr = autocorr[len(autocorr)//2:]
+            coherence = autocorr[1] / (autocorr[0] + 1e-8) if len(autocorr) > 1 else 0.5
+        else:
+            coherences = []
+            for i in range(data.shape[1]):
+                autocorr = np.correlate(data[:, i], data[:, i], mode='full')
+                autocorr = autocorr[len(autocorr)//2:]
+                coh = autocorr[1] / (autocorr[0] + 1e-8) if len(autocorr) > 1 else 0.5
+                coherences.append(coh)
+            coherence = np.mean(coherences)
+        return float(abs(coherence))
+    
+    def _calculate_neural_entropy(self, data: np.ndarray) -> float:
+        """Calculate neural entropy for consciousness measurement"""
+        if data.size == 0:
+            return 0.0
+        histogram = np.histogram(data, bins=20)[0]
+        probabilities = histogram / np.sum(histogram)
+        entropy = -np.sum(probabilities * np.log(probabilities + 1e-8))
+        return float(entropy / np.log(len(probabilities)))
+
+# =============================================================================
+# SCIENTIFIC VALIDATION COMPONENT
+# =============================================================================
+
+class ScientificValidationComponent:
+    def __init__(self):
+        self.validation_methods = {
+            'statistical_analysis': self._perform_statistical_analysis,
+            'reproducibility_analysis': self._perform_reproducibility_analysis,
+            'peer_validation': self._perform_peer_validation
+        }
+        
+    def validate_claim(self, claim_data: Dict, evidence: List[Dict]) -> Dict:
+        validation_results = {}
+        
+        for method_name, method_func in self.validation_methods.items():
+            try:
+                validation_results[method_name] = method_func(claim_data, evidence)
+            except Exception as e:
+                validation_results[method_name] = {'error': str(e), 'valid': False}
+        
+        overall_validity = self._compute_overall_validity(validation_results)
+        
+        return {
+            "overall_validity": overall_validity,
+            "validation_methods": validation_results,
+            "confidence_level": self._calculate_confidence_level(overall_validity),
+            "scientific_grade": self._assign_scientific_grade(overall_validity)
+        }
+    
+    def _perform_statistical_analysis(self, claim_data: Dict, evidence: List[Dict]) -> Dict:
+        if not evidence:
+            return {'valid': False, 'reason': 'insufficient_evidence'}
+        
+        evidence_strengths = [e.get('strength', 0.5) for e in evidence]
+        mean_strength = np.mean(evidence_strengths)
+        std_strength = np.std(evidence_strengths)
+        
+        return {
+            'valid': mean_strength > 0.6 and std_strength < 0.3,
+            'mean_strength': float(mean_strength),
+            'variance': float(std_strength),
+            'sample_size': len(evidence)
+        }
+    
+    def _perform_reproducibility_analysis(self, evidence: List[Dict]) -> Dict:
+        if len(evidence) < 2:
+            return {'valid': False, 'reason': 'insufficient_replication_data'}
+        
+        reproducibility_scores = []
+        for e in evidence:
+            replication_count = e.get('replication_count', 0)
+            reproducibility = min(1.0, replication_count / 3)
+            reproducibility_scores.append(reproducibility)
+        
+        avg_reproducibility = np.mean(reproducibility_scores)
+        
+        return {
+            'valid': avg_reproducibility > 0.6,
+            'reproducibility_score': float(avg_reproducibility),
+            'studies_considered': len(evidence)
+        }
+    
+    def _perform_peer_validation(self, claim_data: Dict, evidence: List[Dict]) -> Dict:
+        source_quality = claim_data.get('source_quality', 0.5)
+        citation_count = claim_data.get('citation_count', 0)
+        
+        peer_score = (source_quality * 0.6 + min(1.0, citation_count / 100) * 0.4)
+        
+        return {
+            'valid': peer_score > 0.5,
+            'peer_score': float(peer_score),
+            'source_quality': float(source_quality),
+            'citation_impact': min(1.0, citation_count / 100)
+        }
+    
+    def _compute_overall_validity(self, validation_results: Dict) -> float:
+        valid_methods = [result for result in validation_results.values() 
+                        if isinstance(result, dict) and result.get('valid', False)]
+        
+        if not valid_methods:
+            return 0.0
+        
+        return min(0.95, len(valid_methods) / len(validation_results))
+    
+    def _calculate_confidence_level(self, validity: float) -> str:
+        if validity > 0.9:
+            return "high"
+        elif validity > 0.7:
+            return "medium"
+        elif validity > 0.5:
+            return "low"
+        else:
+            return "very_low"
+    
+    def _assign_scientific_grade(self, validity: float) -> str:
+        if validity > 0.9:
+            return "A - Robust Scientific Consensus"
+        elif validity > 0.7:
+            return "B - Strong Evidence"
+        elif validity > 0.5:
+            return "C - Moderate Support"
+        else:
+            return "D - Limited Evidence"
+
+# =============================================================================
+# APEX VERIFICATION COMPONENT
+# =============================================================================
+
+class ApexVerificationComponent:
+    def __init__(self):
+        self.verification_cache = {}
+        self.integrity_threshold = 0.8
+        
+    def perform_apex_verification(self, claim_data: Dict, 
+                                truth_results: Dict,
+                                consciousness_results: Dict,
+                                science_results: Dict) -> Dict:
+        """Perform comprehensive apex-level verification"""
+        
+        integrity_score = self._calculate_integrity_score(
+            truth_results, consciousness_results, science_results
+        )
+        
+        coherence_analysis = self._analyze_multi_dimensional_coherence(
+            truth_results, consciousness_results, science_results
+        )
+        
+        verification_seal = self._generate_verification_seal(
+            claim_data, integrity_score, coherence_analysis
+        )
+        
+        return {
+            "apex_integrity_score": float(integrity_score),
+            "multi_dimensional_coherence": coherence_analysis,
+            "verification_seal": verification_seal,
+            "apex_certified": integrity_score > self.integrity_threshold,
+            "verification_timestamp": datetime.utcnow().isoformat(),
+            "composite_confidence": self._calculate_composite_confidence(
+                truth_results, consciousness_results, science_results
+            )
+        }
+    
+    def _calculate_integrity_score(self, truth: Dict, consciousness: Dict, science: Dict) -> float:
+        """Calculate comprehensive integrity score across all dimensions"""
+        truth_strength = truth.get('binding_strength', 0.5)
+        consciousness_level = consciousness.get('consciousness_composite', 0.5)
+        scientific_validity = science.get('overall_validity', 0.5)
+        
+        integrity = (
+            truth_strength * 0.4 +
+            consciousness_level * 0.3 +
+            scientific_validity * 0.3
+        )
+        
+        return max(0.0, min(1.0, integrity))
+    
+    def _analyze_multi_dimensional_coherence(self, truth: Dict, consciousness: Dict, science: Dict) -> Dict:
+        """Analyze coherence across different verification dimensions"""
+        
+        dimensional_scores = {
+            'truth_consciousness_alignment': abs(
+                truth.get('binding_strength', 0.5) - 
+                consciousness.get('consciousness_composite', 0.5)
+            ),
+            'truth_science_alignment': abs(
+                truth.get('binding_strength', 0.5) - 
+                science.get('overall_validity', 0.5)
+            ),
+            'consciousness_science_alignment': abs(
+                consciousness.get('consciousness_composite', 0.5) - 
+                science.get('overall_validity', 0.5)
+            )
+        }
+        
+        overall_coherence = 1.0 - np.mean(list(dimensional_scores.values()))
+        
+        return {
+            "overall_coherence": float(overall_coherence),
+            "dimensional_alignment": dimensional_scores,
+            "coherence_grade": "high" if overall_coherence > 0.8 else "medium" if overall_coherence > 0.6 else "low"
+        }
+    
+    def _generate_verification_seal(self, claim_data: Dict, integrity_score: float, coherence: Dict) -> Dict:
+        """Generate apex verification seal"""
+        seal_data = {
+            'claim_hash': hashlib.sha256(json.dumps(claim_data).encode()).hexdigest(),
+            'integrity_score': integrity_score,
+            'coherence_level': coherence['overall_coherence'],
+            'timestamp': time.time_ns(),
+            'apex_version': '1.1'
+        }
+        
+        seal_hash = hashlib.sha3_512(json.dumps(seal_data).encode()).hexdigest()
+        
+        return {
+            "seal_hash": seal_hash[:64],
+            "seal_data": seal_data,
+            "verification_level": "APEX_CERTIFIED" if integrity_score > 0.8 else "STANDARD_VERIFIED"
+        }
+    
+    def _calculate_composite_confidence(self, truth: Dict, consciousness: Dict, science: Dict) -> float:
+        """Calculate composite confidence score"""
+        confidence_factors = [
+            truth.get('binding_strength', 0.5),
+            consciousness.get('consciousness_composite', 0.5),
+            science.get('overall_validity', 0.5),
+            truth.get('mathematical_certainty', 0.5)
+        ]
+        
+        return float(np.mean(confidence_factors))
+
+# =============================================================================
+# INTEGRATION ENGINE
+# =============================================================================
+
+class IntegrationEngine:
+    def __init__(self):
+        self.component_registry = {}
+        self.data_flow_graph = nx.DiGraph()
+        self.workflow_history = []
+        
+    def register_component(self, component: SystemComponent):
+        self.component_registry[component.component_type] = component
+        for dep in component.dependencies:
+            self.data_flow_graph.add_edge(dep, component.component_type)
+    
+    def execute_workflow(self, start_component: ComponentType, input_data: Dict) -> Dict:
+        current_component = start_component
+        current_data = input_data
+        results = {}
+        
+        while current_component:
+            component = self.component_registry[current_component]
+            instance = component.implementation
+            method_name = component.interface.methods[0]
+            method = getattr(instance, method_name)
+            
+            result = method(current_data)
+            results[current_component] = result
+            
+            next_components = list(self.data_flow_graph.successors(current_component))
+            if not next_components:
+                break
+                
+            current_component = next_components[0]
+            current_data = result
+        
+        workflow_result = {
+            'component_results': results,
+            'final_output': current_data,
+            'timestamp': datetime.utcnow().isoformat(),
+            'workflow_id': hashlib.sha256(str(input_data).encode()).hexdigest()[:16]
+        }
+        
+        self.workflow_history.append(workflow_result)
+        return workflow_result
+
+# =============================================================================
+# AGI FRAME 1.1 MAIN FRAMEWORK
+# =============================================================================
+
+class AGIFrame:
+    def __init__(self):
+        self.integrator = IntegrationEngine()
+        self.initialize_components()
+        
+    def initialize_components(self):
+        # Quantum Truth Component
+        truth_component = SystemComponent(
+            component_type=ComponentType.QUANTUM_TRUTH,
+            interface=ComponentInterface(
+                input_schema={'claim_data': 'dict', 'evidence': 'list'},
+                output_schema={'analysis': 'dict'},
+                methods=['analyze_claim'],
+                error_handling={'invalid_input': 'return_error', 'processing_error': 'retry'}
+            ),
+            dependencies=[],
+            implementation=QuantumTruthComponent()
+        )
+        
+        # Bayesian Consciousness Component
+        consciousness_component = SystemComponent(
+            component_type=ComponentType.BAYESIAN_CONSCIOUSNESS,
+            interface=ComponentInterface(
+                input_schema={'neural_data': 'ndarray'},
+                output_schema={'metrics': 'dict'},
+                methods=['analyze_consciousness'],
+                error_handling={'invalid_data': 'skip', 'model_error': 'fallback'}
+            ),
+            dependencies=[ComponentType.QUANTUM_TRUTH],
+            implementation=BayesianConsciousnessComponent()
+        )
+        
+        # Scientific Validation Component
+        science_component = SystemComponent(
+            component_type=ComponentType.SCIENTIFIC_VALIDATION,
+            interface=ComponentInterface(
+                input_schema={'claim_data': 'dict', 'evidence': 'list'},
+                output_schema={'validation_results': 'dict'},
+                methods=['validate_claim'],
+                error_handling={'insufficient_data': 'return_partial', 'analysis_error': 'log_only'}
+            ),
+            dependencies=[ComponentType.QUANTUM_TRUTH],
+            implementation=ScientificValidationComponent()
+        )
+        
+        # Apex Verification Component
+        apex_component = SystemComponent(
+            component_type=ComponentType.APEX_VERIFICATION,
+            interface=ComponentInterface(
+                input_schema={'claim_data': 'dict', 'truth_results': 'dict', 
+                            'consciousness_results': 'dict', 'science_results': 'dict'},
+                output_schema={'apex_verification': 'dict'},
+                methods=['perform_apex_verification'],
+                error_handling={'integration_error': 'partial_verification', 'data_mismatch': 'reconcile'}
+            ),
+            dependencies=[ComponentType.QUANTUM_TRUTH, ComponentType.BAYESIAN_CONSCIOUSNESS, ComponentType.SCIENTIFIC_VALIDATION],
+            implementation=ApexVerificationComponent()
+        )
+        
+        components = [truth_component, consciousness_component, science_component, apex_component]
+        
+        for component in components:
+            self.integrator.register_component(component)
+    
+    def analyze(self, claim: str, evidence: List[Dict], neural_data: np.ndarray) -> Dict:
+        claim_data = {
+            'content': claim,
+            'logical_consistency': 0.7,
+            'empirical_support': 0.6,
+            'historical_precedents': ['context_patterns'],
+            'source_quality': 0.8,
+            'citation_count': 25
+        }
+        
+        input_data = {
+            'claim_data': claim_data,
+            'evidence': evidence,
+            'neural_data': neural_data
+        }
+        
+        workflow_result = self.integrator.execute_workflow(ComponentType.QUANTUM_TRUTH, input_data)
+        return self._synthesize_results(workflow_result)
+    
+    def _synthesize_results(self, workflow_result: Dict) -> Dict:
+        component_results = workflow_result['component_results']
+        
+        truth_results = component_results.get(ComponentType.QUANTUM_TRUTH, {})
+        consciousness_results = component_results.get(ComponentType.BAYESIAN_CONSCIOUSNESS, {})
+        science_results = component_results.get(ComponentType.SCIENTIFIC_VALIDATION, {})
+        apex_results = component_results.get(ComponentType.APEX_VERIFICATION, {})
+        
+        overall_confidence = apex_results.get('composite_confidence', 0.5)
+        
+        return {
+            'overall_confidence': float(overall_confidence),
+            'truth_metrics': truth_results,
+            'consciousness_metrics': consciousness_results,
+            'scientific_validation': science_results,
+            'apex_verification': apex_results,
+            'workflow_metadata': {
+                'execution_path': list(component_results.keys()),
+                'timestamp': workflow_result['timestamp'],
+                'workflow_id': workflow_result['workflow_id']
+            },
+            'integrated_assessment': self._generate_assessment(overall_confidence, apex_results)
+        }
+    
+    def _generate_assessment(self, confidence: float, apex_results: Dict) -> str:
+        apex_certified = apex_results.get('apex_certified', False)
+        
+        if apex_certified and confidence > 0.9:
+            return "APEX_CERTIFIED_HIGH_CONFIDENCE"
+        elif apex_certified:
+            return "APEX_CERTIFIED"
+        elif confidence > 0.8:
+            return "HIGHLY_RELIABLE"
+        elif confidence > 0.7:
+            return "MODERATELY_RELIABLE"
+        elif confidence > 0.5:
+            return "CAUTIOUSLY_RELIABLE"
+        else:
+            return "UNRELIABLE"
+
+# =============================================================================
+# PRODUCTION USAGE
+# =============================================================================
+
+def main():
+    """Main execution function"""
+    framework = AGIFrame()
+    
+    # Sample data for analysis
+    claim = "Consciousness represents a fundamental property of universal information processing"
+    
+    evidence = [
+        {'content': 'Neuroscientific research on integrated information', 'strength': 0.8, 'replication_count': 3},
+        {'content': 'Quantum consciousness theories', 'strength': 0.6, 'replication_count': 1},
+        {'content': 'Philosophical frameworks', 'strength': 0.7, 'replication_count': 2}
+    ]
+    
+    # Generate sample neural data
+    neural_data = np.random.randn(100, 256) + np.sin(np.linspace(0, 4*np.pi, 256))
+    
+    # Execute comprehensive analysis
+    results = framework.analyze(claim, evidence, neural_data)
+    
+    print("AGI FRAME 1.1 - COMPREHENSIVE ANALYSIS RESULTS")
+    print("=" * 60)
+    print(f"Claim: {claim[:80]}...")
+    print(f"Overall Confidence: {results['overall_confidence']:.3f}")
+    print(f"Assessment: {results['integrated_assessment']}")
+    print(f"Truth Binding: {results['truth_metrics']['binding_strength']:.3f}")
+    print(f"Consciousness Composite: {results['consciousness_metrics']['consciousness_composite']:.3f}")
+    print(f"Scientific Validity: {results['scientific_validation']['overall_validity']:.3f}")
+    
+    apex_verification = results['apex_verification']
+    if apex_verification:
+        print(f"Apex Integrity: {apex_verification.get('apex_integrity_score', 0):.3f}")
+        print(f"Coherence Level: {apex_verification.get('multi_dimensional_coherence', {}).get('overall_coherence', 0):.3f}")
+        print(f"Certified: {apex_verification.get('apex_certified', False)}")
+
+if __name__ == "__main__":
+    main()