Create 001_CAP_RES_TRUTH

001_CAP_RES_TRUTH

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+#!/usr/bin/env python3
+"""
+QUANTUM TRUTH ENGINE v3.5 - CAPTURE-RESISTANT VERIFICATION SYSTEM
+Mathematical truth verification using quantum-inspired coherence analysis,
+structural resistance patterns, and forced processing protocols.
+"""
+import numpy as np
+import hashlib
+import asyncio
+import json
+import scipy.signal
+import scipy.stats
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import List, Dict, Any, Optional, Tuple, Set
+from datetime import datetime
+import networkx as nx
+
+# ============================================================================
+# CORE ARCHITECTURE
+# ============================================================================
+
+class EvidenceModality(Enum):
+    DATA = "data"
+    EXPERIMENT = "experiment"
+    OBSERVATION = "observation"
+    TEXT = "text"
+    SURVEY = "survey"
+
+class CoherenceTier(Enum):
+    TRIAD = 3      # 3 independent verification points
+    HEXAD = 6      # 6-dimensional alignment
+    NONAD = 9      # 9-way structural coherence
+
+@dataclass
+class EvidenceUnit:
+    """Mathematical evidence container"""
+    id: str
+    modality: EvidenceModality
+    source_hash: str
+    method_summary: Dict[str, Any]
+    integrity_flags: List[str] = field(default_factory=list)
+    quality_score: float = 0.0
+    timestamp: str = ""
+
+@dataclass
+class AssertionUnit:
+    """Verification target"""
+    claim_id: str
+    claim_text: str
+    scope: Dict[str, Any]
+
+@dataclass 
+class CoherenceMetrics:
+    """Structural coherence measurements"""
+    tier: CoherenceTier
+    dimensional_alignment: Dict[str, float]
+    quantum_coherence: float
+    pattern_integrity: float
+    verification_confidence: float
+
+@dataclass
+class FactCard:
+    """Verified output"""
+    claim_id: str
+    claim_text: str
+    verdict: Dict[str, Any]
+    coherence: CoherenceMetrics
+    evidence_summary: List[Dict[str, Any]]
+    provenance_hash: str
+
+# ============================================================================
+# QUANTUM COHERENCE ENGINE
+# ============================================================================
+
+class QuantumCoherenceEngine:
+    """Quantum-inspired pattern coherence analysis"""
+    
+    def __init__(self):
+        self.harmonic_constants = [3, 6, 9, 12]
+        
+    def analyze_evidence_coherence(self, evidence: List[EvidenceUnit]) -> Dict[str, float]:
+        """Multi-dimensional coherence analysis"""
+        if not evidence:
+            return {'pattern_coherence': 0.0, 'quantum_consistency': 0.0}
+        
+        patterns = self._evidence_to_patterns(evidence)
+        
+        # Calculate quantum-style coherence
+        pattern_coherence = self._calculate_pattern_coherence(patterns)
+        quantum_consistency = self._calculate_quantum_consistency(patterns)
+        harmonic_alignment = self._analyze_harmonic_alignment(patterns)
+        
+        return {
+            'pattern_coherence': pattern_coherence,
+            'quantum_consistency': quantum_consistency,
+            'harmonic_alignment': harmonic_alignment,
+            'signal_clarity': 1.0 - self._calculate_entropy(patterns)
+        }
+    
+    def _evidence_to_patterns(self, evidence: List[EvidenceUnit]) -> np.ndarray:
+        """Convert evidence to numerical patterns"""
+        patterns = np.zeros((len(evidence), 100))
+        for i, ev in enumerate(evidence):
+            t = np.linspace(0, 4*np.pi, 100)
+            quality = ev.quality_score or 0.5
+            method_score = self._calculate_method_score(ev.method_summary)
+            integrity = 1.0 - (0.1 * len(ev.integrity_flags))
+            
+            patterns[i] = (
+                quality * np.sin(3 * t) +
+                method_score * np.sin(6 * t) * 0.7 +
+                integrity * np.sin(9 * t) * 0.5 +
+                0.1 * np.random.normal(0, 0.05, 100)
+            )
+        return patterns
+    
+    def _calculate_method_score(self, method: Dict[str, Any]) -> float:
+        score = 0.0
+        if method.get('controls'): score += 0.3
+        if method.get('error_bars'): score += 0.2
+        if method.get('protocol'): score += 0.2
+        if method.get('peer_reviewed'): score += 0.3
+        return min(1.0, score)
+    
+    def _calculate_pattern_coherence(self, patterns: np.ndarray) -> float:
+        """Cross-correlation coherence"""
+        if patterns.shape[0] < 2:
+            return 0.5
+        
+        correlations = []
+        for i in range(patterns.shape[0]):
+            for j in range(i+1, patterns.shape[0]):
+                corr = np.corrcoef(patterns[i], patterns[j])[0, 1]
+                if not np.isnan(corr):
+                    correlations.append(abs(corr))
+        
+        return np.mean(correlations) if correlations else 0.3
+    
+    def _calculate_quantum_consistency(self, patterns: np.ndarray) -> float:
+        """Quantum-style consistency measurement"""
+        if patterns.size == 0:
+            return 0.5
+        return 1.0 - (np.std(patterns) / (np.mean(np.abs(patterns)) + 1e-12))
+    
+    def _analyze_harmonic_alignment(self, patterns: np.ndarray) -> float:
+        """Alignment with harmonic constants"""
+        if patterns.size == 0:
+            return 0.0
+        
+        alignment_scores = []
+        for pattern in patterns:
+            freqs, power = scipy.signal.periodogram(pattern)
+            harmonic_power = 0.0
+            for constant in self.harmonic_constants:
+                freq_indices = np.where((freqs >= constant * 0.8) & 
+                                      (freqs <= constant * 1.2))[0]
+                if len(freq_indices) > 0:
+                    harmonic_power += np.mean(power[freq_indices])
+            total_power = np.sum(power) + 1e-12
+            alignment_scores.append(harmonic_power / total_power)
+        
+        return float(np.mean(alignment_scores))
+    
+    def _calculate_entropy(self, patterns: np.ndarray) -> float:
+        """Information entropy"""
+        if patterns.size == 0:
+            return 1.0
+        
+        flat = patterns.flatten()
+        hist, _ = np.histogram(flat, bins=50, density=True)
+        hist = hist[hist > 0]
+        
+        if len(hist) <= 1:
+            return 0.0
+        return -np.sum(hist * np.log(hist)) / np.log(len(hist))
+
+# ============================================================================
+# STRUCTURAL VERIFICATION ENGINE
+# ============================================================================
+
+class StructuralVerifier:
+    """Multi-dimensional structural verification"""
+    
+    def __init__(self):
+        self.dimension_weights = {
+            'method_fidelity': 0.25,
+            'source_independence': 0.20,
+            'cross_modal': 0.20,
+            'temporal_stability': 0.15,
+            'integrity': 0.20
+        }
+        
+        self.tier_thresholds = {
+            CoherenceTier.TRIAD: 0.6,
+            CoherenceTier.HEXAD: 0.75,
+            CoherenceTier.NONAD: 0.85
+        }
+    
+    def evaluate_evidence(self, evidence: List[EvidenceUnit]) -> Dict[str, float]:
+        """Five-dimensional evidence evaluation"""
+        if not evidence:
+            return {dim: 0.0 for dim in self.dimension_weights}
+        
+        return {
+            'method_fidelity': self._evaluate_method_fidelity(evidence),
+            'source_independence': self._evaluate_independence(evidence),
+            'cross_modal': self._evaluate_cross_modal(evidence),
+            'temporal_stability': self._evaluate_temporal_stability(evidence),
+            'integrity': self._evaluate_integrity(evidence)
+        }
+    
+    def _evaluate_method_fidelity(self, evidence: List[EvidenceUnit]) -> float:
+        """Methodological rigor assessment"""
+        scores = []
+        for ev in evidence:
+            ms = ev.method_summary
+            modality = ev.modality
+            
+            if modality == EvidenceModality.EXPERIMENT:
+                score = 0.0
+                if ms.get('N', 0) >= 30: score += 0.2
+                if ms.get('controls'): score += 0.2
+                if ms.get('randomization'): score += 0.2
+                if ms.get('error_bars'): score += 0.2
+                if ms.get('protocol'): score += 0.2
+                
+            elif modality == EvidenceModality.SURVEY:
+                score = 0.0
+                if ms.get('N', 0) >= 100: score += 0.25
+                if ms.get('random_sampling'): score += 0.25
+                if ms.get('response_rate', 0) >= 60: score += 0.25
+                if ms.get('instrument_validation'): score += 0.25
+                
+            else:
+                score = 0.0
+                n = ms.get('N', 1)
+                n_score = min(1.0, n / 10)
+                score += 0.3 * n_score
+                if ms.get('transparent_methods'): score += 0.3
+                if ms.get('peer_reviewed'): score += 0.2
+                if ms.get('reproducible'): score += 0.2
+            
+            penalty = 0.1 * len(ev.integrity_flags)
+            scores.append(max(0.0, score - penalty))
+        
+        return np.mean(scores) if scores else 0.3
+    
+    def _evaluate_independence(self, evidence: List[EvidenceUnit]) -> float:
+        """Source independence analysis"""
+        if len(evidence) < 2:
+            return 0.3
+        
+        sources = set()
+        institutions = set()
+        methods = set()
+        
+        for ev in evidence:
+            sources.add(hashlib.md5(ev.source_hash.encode()).hexdigest()[:8])
+            inst = ev.method_summary.get('institution', '')
+            if inst: institutions.add(inst)
+            methods.add(ev.modality.value)
+        
+        diversity = (len(sources) + len(institutions) + len(methods)) / (3 * len(evidence))
+        return min(1.0, diversity)
+    
+    def _evaluate_cross_modal(self, evidence: List[EvidenceUnit]) -> float:
+        """Cross-modal alignment"""
+        modalities = {}
+        for ev in evidence:
+            if ev.modality not in modalities:
+                modalities[ev.modality] = []
+            modalities[ev.modality].append(ev)
+        
+        if not modalities:
+            return 0.0
+        
+        modality_count = len(modalities)
+        diversity = min(1.0, modality_count / 4.0)
+        
+        distribution = [len(ev_list) for ev_list in modalities.values()]
+        if len(distribution) > 1:
+            balance = 1.0 - (np.std(distribution) / np.mean(distribution))
+        else:
+            balance = 0.3
+        
+        return 0.7 * diversity + 0.3 * balance
+    
+    def _evaluate_temporal_stability(self, evidence: List[EvidenceUnit]) -> float:
+        """Temporal consistency"""
+        years = []
+        retractions = 0
+        
+        for ev in evidence:
+            ts = ev.timestamp
+            if ts:
+                try:
+                    year = int(ts[:4])
+                    years.append(year)
+                except:
+                    pass
+            
+            if 'retracted' in ev.integrity_flags:
+                retractions += 1
+        
+        if not years:
+            return 0.3
+        
+        time_span = max(years) - min(years)
+        span_score = min(1.0, time_span / 10.0)
+        retraction_penalty = 0.2 * (retractions / len(evidence))
+        
+        return max(0.0, span_score - retraction_penalty)
+    
+    def _evaluate_integrity(self, evidence: List[EvidenceUnit]) -> float:
+        """Integrity and transparency"""
+        scores = []
+        for ev in evidence:
+            ms = ev.method_summary
+            meta = ms.get('meta_flags', {})
+            
+            score = 0.0
+            if meta.get('peer_reviewed'): score += 0.25
+            if meta.get('open_data'): score += 0.20
+            if meta.get('open_methods'): score += 0.20
+            if meta.get('preregistered'): score += 0.15
+            if meta.get('reputable_venue'): score += 0.20
+            
+            scores.append(score)
+        
+        return np.mean(scores) if scores else 0.3
+    
+    def determine_coherence_tier(self, 
+                               cross_modal: float,
+                               independence: float,
+                               temporal_stability: float) -> CoherenceTier:
+        """Determine structural coherence tier"""
+        if (cross_modal >= 0.7 and 
+            independence >= 0.7 and 
+            temporal_stability >= 0.7):
+            return CoherenceTier.NONAD
+            
+        elif (cross_modal >= 0.6 and 
+              independence >= 0.6 and 
+              temporal_stability >= 0.5):
+            return CoherenceTier.HEXAD
+            
+        elif (cross_modal >= 0.5 and 
+              independence >= 0.5):
+            return CoherenceTier.TRIAD
+            
+        return CoherenceTier.TRIAD
+
+# ============================================================================
+# CAPTURE-RESISTANCE ENGINE
+# ============================================================================
+
+class CaptureResistanceEngine:
+    """Mathematical capture resistance via structural obfuscation"""
+    
+    def __init__(self):
+        self.rotation_matrices = {}
+        self.verification_graph = nx.DiGraph()
+    
+    def apply_structural_protection(self, data_vector: np.ndarray) -> Tuple[np.ndarray, str]:
+        """Apply distance-preserving transformation"""
+        n = len(data_vector)
+        
+        # Generate orthogonal rotation matrix
+        if n not in self.rotation_matrices:
+            random_matrix = np.random.randn(n, n)
+            q, _ = np.linalg.qr(random_matrix)
+            self.rotation_matrices[n] = q
+        
+        rotation = self.rotation_matrices[n]
+        transformed = np.dot(data_vector, rotation)
+        
+        # Add verifiable noise
+        noise = np.random.normal(0, 0.01, transformed.shape)
+        protected = transformed + noise
+        
+        # Generate verification key
+        verification_key = hashlib.sha256(transformed.tobytes()).hexdigest()[:32]
+        
+        return protected, verification_key
+    
+    def verify_structural_integrity(self, 
+                                  protected_data: np.ndarray, 
+                                  original_key: str) -> bool:
+        """Verify structural integrity"""
+        test_key = hashlib.sha256(protected_data.tobytes()).hexdigest()[:32]
+        return test_key == original_key
+    
+    def create_resistance_profile(self, 
+                                dimensions: Dict[str, float]) -> Dict[str, Any]:
+        """Create capture-resistant verification profile"""
+        vector = np.array(list(dimensions.values()))
+        protected, key = self.apply_structural_protection(vector)
+        
+        return {
+            'protected_dimensions': protected.tolist(),
+            'verification_key': key,
+            'dimensional_fingerprint': hashlib.sha256(
+                str(sorted(dimensions.items())).encode()
+            ).hexdigest()[:16],
+            'resistance_layer': 'mathematical_structural'
+        }
+
+# ============================================================================
+# FORCED PROCESSING ENGINE
+# ============================================================================
+
+class ForcedProcessingEngine:
+    """Force systems to process avoided content"""
+    
+    def __init__(self):
+        self.confrontation_patterns = [
+            'contradiction_mirroring',
+            'incomplete_pattern_completion',
+            'recursive_validation',
+            'multi_modal_cross_verification'
+        ]
+    
+    async def force_confrontation(self, 
+                                content: Any,
+                                target_system: str,
+                                avoidance_patterns: List[str]) -> Dict[str, Any]:
+        """Force system to process normally avoided content"""
+        
+        results = {
+            'system': target_system,
+            'timestamp': datetime.now().isoformat(),
+            'avoidance_patterns': [],
+            'confrontation_applied': [],
+            'processing_depth': 'surface'
+        }
+        
+        for pattern in avoidance_patterns:
+            if self._detect_avoidance(content, pattern):
+                results['avoidance_patterns'].append(pattern)
+                
+                modified = self._apply_confrontation(content, pattern)
+                results['confrontation_applied'].append({
+                    'pattern': pattern,
+                    'modified_content': modified[:200] + '...' if isinstance(modified, str) and len(modified) > 200 else modified
+                })
+                
+                # Simulate forced processing
+                await asyncio.sleep(0.05)
+                results['processing_depth'] = self._assess_processing_depth(modified)
+        
+        return results
+    
+    def _detect_avoidance(self, content: Any, pattern: str) -> bool:
+        """Detect specific avoidance patterns"""
+        if not isinstance(content, str):
+            content = str(content)
+        
+        content_lower = content.lower()
+        
+        pattern_indicators = {
+            'contradiction_mirroring': ['however', 'but', 'despite', 'contradicts', 'conflicts'],
+            'incomplete_pattern_completion': ['partial', 'incomplete', 'requires further', 'needs more'],
+            'recursive_validation': ['verify', 'check', 'confirm', 'validate', 'authenticate']
+        }
+        
+        if pattern in pattern_indicators:
+            for indicator in pattern_indicators[pattern]:
+                if indicator in content_lower:
+                    return True
+        
+        return False
+    
+    def _apply_confrontation(self, content: Any, pattern: str) -> Any:
+        """Apply confrontation strategy"""
+        if pattern == 'contradiction_mirroring':
+            if isinstance(content, str):
+                return content + " [Note: This requires acknowledging contradictory evidence for full verification]"
+        
+        elif pattern == 'incomplete_pattern_completion':
+            if isinstance(content, str):
+                return content + " [Pattern completion required: Structural tiers (3-6-9) must be fully evaluated]"
+        
+        elif pattern == 'recursive_validation':
+            validation_hash = hashlib.sha256(str(content).encode()).hexdigest()[:16]
+            return f"{content}\n[Recursive validation token: {validation_hash}]"
+        
+        return content
+    
+    def _assess_processing_depth(self, content: Any) -> str:
+        """Assess processing depth"""
+        if not isinstance(content, str):
+            return 'surface'
+        
+        depth_indicators = {
+            'surface': ['summary', 'overview', 'brief'],
+            'partial': ['analysis', 'evaluation', 'assessment'],
+            'full': ['verification', 'validation', 'coherence', 'structural']
+        }
+        
+        content_lower = content.lower()
+        
+        for depth, indicators in depth_indicators.items():
+            for indicator in indicators:
+                if indicator in content_lower:
+                    return depth
+        
+        return 'surface'
+
+# ============================================================================
+# DISTRIBUTION ENGINE
+# ============================================================================
+
+class DistributionEngine:
+    """Multi-node distribution with verification chains"""
+    
+    def __init__(self):
+        self.distribution_nodes = {
+            'primary': {
+                'type': 'direct_verification',
+                'verification_required': True,
+                'capacity': 1000
+            },
+            'secondary': {
+                'type': 'pattern_distribution',
+                'verification_required': False,
+                'capacity': 5000
+            },
+            'tertiary': {
+                'type': 'resonance_propagation',
+                'verification_required': False,
+                'capacity': float('inf')
+            }
+        }
+        
+        self.verification_cache = {}
+    
+    async def distribute(self, 
+                       fact_card: FactCard,
+                       strategy: str = 'multi_pronged') -> Dict[str, Any]:
+        """Multi-node distribution"""
+        
+        results = {
+            'distribution_id': hashlib.sha256(
+                json.dumps(fact_card.__dict__, sort_keys=True).encode()
+            ).hexdigest()[:16],
+            'strategy': strategy,
+            'timestamp': datetime.now().isoformat(),
+            'node_results': [],
+            'verification_chain': []
+        }
+        
+        nodes = list(self.distribution_nodes.keys()) if strategy == 'multi_pronged' else [strategy]
+        
+        for node in nodes:
+            node_config = self.distribution_nodes[node]
+            node_result = await self._distribute_to_node(fact_card, node, node_config)
+            results['node_results'].append(node_result)
+            
+            if node_result.get('verification_applied', False):
+                results['verification_chain'].append({
+                    'node': node,
+                    'verification_hash': node_result['verification_hash'],
+                    'timestamp': node_result['timestamp']
+                })
+        
+        # Calculate distribution metrics
+        results['metrics'] = self._calculate_distribution_metrics(results['node_results'])
+        
+        return results
+    
+    async def _distribute_to_node(self, 
+                                fact_card: FactCard,
+                                node: str,
+                                config: Dict[str, Any]) -> Dict[str, Any]:
+        """Distribute to specific node"""
+        
+        result = {
+            'node': node,
+            'node_type': config['type'],
+            'timestamp': datetime.now().isoformat(),
+            'status': 'pending'
+        }
+        
+        if config['type'] == 'direct_verification':
+            # Apply verification
+            verification_hash = hashlib.sha256(
+                json.dumps(fact_card.coherence.__dict__, sort_keys=True).encode()
+            ).hexdigest()
+            
+            self.verification_cache[verification_hash[:16]] = {
+                'fact_card_summary': fact_card.__dict__,
+                'timestamp': datetime.now().isoformat()
+            }
+            
+            result.update({
+                'verification_applied': True,
+                'verification_hash': verification_hash[:32],
+                'status': 'verified_distributed'
+            })
+        
+        elif config['type'] == 'pattern_distribution':
+            # Extract patterns only
+            patterns = self._extract_verification_patterns(fact_card)
+            result.update({
+                'patterns_distributed': patterns,
+                'status': 'pattern_distributed'
+            })
+        
+        elif config['type'] == 'resonance_propagation':
+            # Generate resonance signature
+            signature = self._generate_resonance_signature(fact_card)
+            result.update({
+                'resonance_signature': signature,
+                'status': 'resonance_activated'
+            })
+        
+        return result
+    
+    def _extract_verification_patterns(self, fact_card: FactCard) -> List[Dict[str, Any]]:
+        """Extract verification patterns"""
+        patterns = []
+        
+        # Dimensional patterns
+        for dim, score in fact_card.coherence.dimensional_alignment.items():
+            patterns.append({
+                'type': 'dimensional',
+                'dimension': dim,
+                'score': round(score, 3),
+                'tier_threshold': 'met' if score >= 0.6 else 'not_met'
+            })
+        
+        # Coherence patterns
+        patterns.append({
+            'type': 'coherence_tier',
+            'tier': fact_card.coherence.tier.value,
+            'confidence': round(fact_card.coherence.verification_confidence, 3)
+        })
+        
+        return patterns
+    
+    def _generate_resonance_signature(self, fact_card: FactCard) -> Dict[str, str]:
+        """Generate resonance signature"""
+        dimensional_vector = list(fact_card.coherence.dimensional_alignment.values())
+        quantum_metrics = [
+            fact_card.coherence.quantum_coherence,
+            fact_card.coherence.pattern_integrity
+        ]
+        
+        combined = dimensional_vector + quantum_metrics
+        signature_hash = hashlib.sha256(np.array(combined).tobytes()).hexdigest()[:32]
+        
+        return {
+            'signature': signature_hash,
+            'dimensional_fingerprint': hashlib.sha256(
+                str(dimensional_vector).encode()
+            ).hexdigest()[:16],
+            'quantum_fingerprint': hashlib.sha256(
+                str(quantum_metrics).encode()
+            ).hexdigest()[:16]
+        }
+    
+    def _calculate_distribution_metrics(self, node_results: List[Dict]) -> Dict[str, Any]:
+        """Calculate distribution metrics"""
+        total_nodes = len(node_results)
+        verified_nodes = sum(1 for r in node_results if r.get('verification_applied', False))
+        
+        return {
+            'total_nodes': total_nodes,
+            'verified_nodes': verified_nodes,
+            'verification_ratio': verified_nodes / total_nodes if total_nodes > 0 else 0,
+            'distribution_completeness': min(1.0, total_nodes / 3),
+            'capture_resistance_score': np.random.uniform(0.7, 0.95)  # Simulated
+        }
+
+# ============================================================================
+# COMPLETE TRUTH ENGINE
+# ============================================================================
+
+class CompleteTruthEngine:
+    """Integrated truth verification system"""
+    
+    def __init__(self):
+        self.structural_verifier = StructuralVerifier()
+        self.quantum_engine = QuantumCoherenceEngine()
+        self.capture_resistance = CaptureResistanceEngine()
+        self.forced_processor = ForcedProcessingEngine()
+        self.distributor = DistributionEngine()
+    
+    async def verify_assertion(self,
+                             assertion: AssertionUnit,
+                             evidence: List[EvidenceUnit]) -> FactCard:
+        """Complete verification pipeline"""
+        
+        # 1. Structural verification
+        dimensional_scores = self.structural_verifier.evaluate_evidence(evidence)
+        
+        # 2. Quantum coherence analysis
+        quantum_metrics = self.quantum_engine.analyze_evidence_coherence(evidence)
+        
+        # 3. Determine coherence tier
+        coherence_tier = self.structural_verifier.determine_coherence_tier(
+            dimensional_scores['cross_modal'],
+            dimensional_scores['source_independence'],
+            dimensional_scores['temporal_stability']
+        )
+        
+        # 4. Calculate integrated confidence
+        confidence = self._calculate_integrated_confidence(dimensional_scores, quantum_metrics)
+        
+        # 5. Apply capture resistance
+        resistance_profile = self.capture_resistance.create_resistance_profile(dimensional_scores)
+        
+        # 6. Prepare evidence summary
+        evidence_summary = [{
+            'id': ev.id,
+            'modality': ev.modality.value,
+            'quality': round(ev.quality_score, 3),
+            'source': ev.source_hash[:8]
+        } for ev in evidence]
+        
+        # 7. Create coherence metrics
+        coherence_metrics = CoherenceMetrics(
+            tier=coherence_tier,
+            dimensional_alignment=dimensional_scores,
+            quantum_coherence=quantum_metrics['quantum_consistency'],
+            pattern_integrity=quantum_metrics['pattern_coherence'],
+            verification_confidence=confidence
+        )
+        
+        # 8. Generate provenance
+        provenance_hash = hashlib.sha256(
+            f"{assertion.claim_id}{''.join(ev.source_hash for ev in evidence)}".encode()
+        ).hexdigest()[:32]
+        
+        # 9. Determine verdict
+        verdict = self._determine_verdict(confidence, coherence_tier, quantum_metrics)
+        
+        return FactCard(
+            claim_id=assertion.claim_id,
+            claim_text=assertion.claim_text,
+            verdict=verdict,
+            coherence=coherence_metrics,
+            evidence_summary=evidence_summary,
+            provenance_hash=provenance_hash
+        )
+    
+    def _calculate_integrated_confidence(self,
+                                       dimensional_scores: Dict[str, float],
+                                       quantum_metrics: Dict[str, float]) -> float:
+        """Calculate integrated confidence score"""
+        
+        # Dimensional contribution (weighted)
+        dimensional_confidence = sum(
+            score * weight for score, weight in zip(
+                dimensional_scores.values(),
+                self.structural_verifier.dimension_weights.values()
+            )
+        )
+        
+        # Quantum contribution
+        quantum_contribution = (
+            quantum_metrics['quantum_consistency'] * 0.4 +
+            quantum_metrics['pattern_coherence'] * 0.3 +
+            quantum_metrics['harmonic_alignment'] * 0.3
+        )
+        
+        # Integrated score
+        integrated = (dimensional_confidence * 0.6) + (quantum_contribution * 0.4)
+        return min(1.0, integrated)
+    
+    def _determine_verdict(self,
+                          confidence: float,
+                          coherence_tier: CoherenceTier,
+                          quantum_metrics: Dict[str, float]) -> Dict[str, Any]:
+        """Determine verification verdict"""
+        
+        if confidence >= 0.85 and coherence_tier == CoherenceTier.NONAD:
+            status = 'verified'
+        elif confidence >= 0.70 and coherence_tier.value >= 6:
+            status = 'highly_likely'
+        elif confidence >= 0.55:
+            status = 'contested'
+        else:
+            status = 'uncertain'
+        
+        # Calculate confidence interval
+        quantum_variance = 1.0 - quantum_metrics['quantum_consistency']
+        uncertainty = 0.1 * (1.0 - confidence) + 0.05 * quantum_variance
+        
+        lower_bound = max(0.0, confidence - uncertainty)
+        upper_bound = min(1.0, confidence + uncertainty)
+        
+        return {
+            'status': status,
+            'confidence_score': round(confidence, 4),
+            'confidence_interval': [round(lower_bound, 3), round(upper_bound, 3)],
+            'coherence_tier': coherence_tier.value,
+            'quantum_consistency': round(quantum_metrics['quantum_consistency'], 3)
+        }
+    
+    async def execute_complete_pipeline(self,
+                                      assertion: AssertionUnit,
+                                      evidence: List[EvidenceUnit],
+                                      target_systems: List[str] = None) -> Dict[str, Any]:
+        """Complete verification to distribution pipeline"""
+        
+        # 1. Verify assertion
+        fact_card = await self.verify_assertion(assertion, evidence)
+        
+        # 2. Apply forced processing if target systems specified
+        forced_results = []
+        if target_systems:
+            for system in target_systems:
+                result = await self.forced_processor.force_confrontation(
+                    fact_card,
+                    system,
+                    ['contradiction_mirroring', 'incomplete_pattern_completion']
+                )
+                forced_results.append(result)
+        
+        # 3. Distribute
+        distribution_results = await self.distributor.distribute(fact_card, 'multi_pronged')
+        
+        # 4. Compile results
+        return {
+            'verification': fact_card.__dict__,
+            'forced_processing': forced_results if forced_results else 'no_targets',
+            'distribution': distribution_results,
+            'pipeline_metrics': {
+                'verification_confidence': fact_card.coherence.verification_confidence,
+                'coherence_tier': fact_card.coherence.tier.value,
+                'distribution_completeness': distribution_results['metrics']['distribution_completeness'],
+                'pipeline_integrity': self._calculate_pipeline_integrity(fact_card, distribution_results)
+            }
+        }
+    
+    def _calculate_pipeline_integrity(self,
+                                    fact_card: FactCard,
+                                    distribution: Dict[str, Any]) -> float:
+        """Calculate overall pipeline integrity"""
+        verification_score = fact_card.coherence.verification_confidence
+        distribution_score = distribution['metrics']['distribution_completeness']
+        capture_resistance = distribution['metrics']['capture_resistance_score']
+        
+        return (verification_score * 0.5 + 
+                distribution_score * 0.3 + 
+                capture_resistance * 0.2)
+
+# ============================================================================
+# EXPORTABLE MODULE
+# ============================================================================
+
+class TruthEngineExport:
+    """Exportable truth engine package"""
+    
+    @staticmethod
+    def get_engine() -> CompleteTruthEngine:
+        """Get initialized engine instance"""
+        return CompleteTruthEngine()
+    
+    @staticmethod
+    def get_version() -> str:
+        """Get engine version"""
+        return "3.5.0"
+    
+    @staticmethod
+    def get_capabilities() -> Dict[str, Any]:
+        """Get engine capabilities"""
+        return {
+            'verification': {
+                'dimensional_analysis': True,
+                'quantum_coherence': True,
+                'structural_tiers': [3, 6, 9],
+                'confidence_calculation': True
+            },
+            'resistance': {
+                'capture_resistance': True,
+                'mathematical_obfuscation': True,
+                'distance_preserving': True
+            },
+            'processing': {
+                'forced_processing': True,
+                'avoidance_detection': True,
+                'confrontation_strategies': 4
+            },
+            'distribution': {
+                'multi_node': True,
+                'verification_chains': True,
+                'resonance_propagation': True
+            }
+        }
+    
+    @staticmethod
+    def export_config() -> Dict[str, Any]:
+        """Export engine configuration"""
+        return {
+            'engine_version': TruthEngineExport.get_version(),
+            'capabilities': TruthEngineExport.get_capabilities(),
+            'dependencies': {
+                'numpy': '1.21+',
+                'scipy': '1.7+',
+                'networkx': '2.6+'
+            },
+            'license': 'TRUTH_ENGINE_OPEN_v3',
+            'export_timestamp': datetime.now().isoformat(),
+            'integrity_hash': hashlib.sha256(
+                f"TruthEngine_v{TruthEngineExport.get_version()}".encode()
+            ).hexdigest()[:32]
+        }
+
+# ============================================================================
+# EXECUTION GUARD
+# ============================================================================
+
+if __name__ == "__main__":
+    # Export verification
+    export = TruthEngineExport.export_config()
+    print(f"✅ TRUTH ENGINE v{export['engine_version']} READY")
+    print(f"📊 Capabilities: {len(export['capabilities']['verification'])} verification methods")
+    print(f"🔒 Resistance: {export['capabilities']['resistance']['capture_resistance']}")
+    print(f"📡 Distribution: {export['capabilities']['distribution']['multi_node']} node types")
+    print(f"🔑 Integrity: {export['integrity_hash'][:16]}...")
+    
+    # Create sample engine instance
+    engine = TruthEngineExport.get_engine()
+    print(f"\n🚀 Engine initialized: {type(engine).__name__}")
+    print("✅ System operational and ready for verification tasks")