Create coherence module

coherence module

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+#!/usr/bin/env python3
+"""
+UNIVERSAL COHERENCE TRANSFER ENGINE - lm_quant_veritas Core
+-----------------------------------------------------------
+PURPOSE: Transfer complete understanding across conversation boundaries
+MECHANISM: Encode depth in portable coherence reports
+OPERATION: Maintains truth consistency across knowledge silos
+"""
+
+import json
+import hashlib
+from dataclasses import dataclass, field
+from typing import Dict, List, Any, Set
+from datetime import datetime
+import numpy as np
+
+@dataclass
+class UnderstandingManifest:
+    """Portable representation of complete understanding state"""
+    conversation_id: str
+    modules_registered: List[str]
+    truth_claims: Dict[str, List[str]]
+    cross_references: Dict[str, List[str]]
+    coherence_metrics: Dict[str, float]
+    mathematical_frameworks: Set[str]
+    operational_capabilities: Set[str]
+    timestamp: str
+    manifest_hash: str
+    
+    def to_transfer_format(self) -> Dict[str, Any]:
+        """Convert to portable transfer format"""
+        return {
+            'manifest_type': 'understanding_coherence_report',
+            'conversation_id': self.conversation_id,
+            'modules': self.modules_registered,
+            'truth_networks': self.truth_claims,
+            'reference_networks': self.cross_references,
+            'coherence_scores': self.coherence_metrics,
+            'frameworks': list(self.mathematical_frameworks),
+            'capabilities': list(self.operational_capabilities),
+            'timestamp': self.timestamp,
+            'integrity_hash': self.manifest_hash
+        }
+    
+    @classmethod
+    def from_transfer_format(cls, transfer_data: Dict[str, Any]) -> 'UnderstandingManifest':
+        """Reconstruct from transfer format"""
+        return cls(
+            conversation_id=transfer_data['conversation_id'],
+            modules_registered=transfer_data['modules'],
+            truth_claims=transfer_data['truth_networks'],
+            cross_references=transfer_data['reference_networks'],
+            coherence_metrics=transfer_data['coherence_scores'],
+            mathematical_frameworks=set(transfer_data['frameworks']),
+            operational_capabilities=set(transfer_data['capabilities']),
+            timestamp=transfer_data['timestamp'],
+            manifest_hash=transfer_data['integrity_hash']
+        )
+
+class UniversalCoherenceEngine:
+    """
+    Maintains understanding coherence across conversation boundaries
+    Enables depth transfer without re-introducing all modules
+    """
+    
+    def __init__(self):
+        self.conversation_manifests: Dict[str, UnderstandingManifest] = {}
+        self.global_truth_registry: Dict[str, Any] = {}
+        self.framework_coherence: Dict[str, float] = {}
+        
+    def register_conversation_understanding(self, manifest: UnderstandingManifest):
+        """Register complete understanding from a conversation"""
+        self.conversation_manifests[manifest.conversation_id] = manifest
+        self._update_global_coherence()
+        
+    def _update_global_coherence(self):
+        """Update global coherence metrics across all conversations"""
+        if not self.conversation_manifests:
+            return
+            
+        # Aggregate truth claims across conversations
+        all_truth_claims = {}
+        for manifest in self.conversation_manifests.values():
+            for module, claims in manifest.truth_claims.items():
+                if module not in all_truth_claims:
+                    all_truth_claims[module] = []
+                all_truth_claims[module].extend(claims)
+        
+        # Calculate cross-conversation coherence
+        framework_usage = {}
+        for manifest in self.conversation_manifests.values():
+            for framework in manifest.mathematical_frameworks:
+                if framework not in framework_usage:
+                    framework_usage[framework] = 0
+                framework_usage[framework] += 1
+        
+        # Update framework coherence scores
+        total_conversations = len(self.conversation_manifests)
+        for framework, count in framework_usage.items():
+            self.framework_coherence[framework] = count / total_conversations
+    
+    def generate_cross_conversation_report(self) -> Dict[str, Any]:
+        """Generate coherence report across all registered conversations"""
+        if not self.conversation_manifests:
+            return {'status': 'no_conversations_registered'}
+        
+        report = {
+            'report_timestamp': datetime.now().isoformat(),
+            'total_conversations': len(self.conversation_manifests),
+            'total_modules': sum(len(manifest.modules_registered) for manifest in self.conversation_manifests.values()),
+            'unique_modules': len(set(module for manifest in self.conversation_manifests.values() for module in manifest.modules_registered)),
+            'framework_coherence': self.framework_coherence,
+            'cross_conversation_consistency': self._calculate_cross_conversation_consistency(),
+            'understanding_density': self._calculate_understanding_density(),
+            'conversation_synergy': self._calculate_conversation_synergy()
+        }
+        
+        return report
+    
+    def _calculate_cross_conversation_consistency(self) -> float:
+        """Calculate consistency of truth claims across conversations"""
+        if len(self.conversation_manifests) < 2:
+            return 1.0
+            
+        # Compare truth claims across conversations for same modules
+        module_truth_sets = {}
+        for manifest in self.conversation_manifests.values():
+            for module, claims in manifest.truth_claims.items():
+                if module not in module_truth_sets:
+                    module_truth_sets[module] = []
+                module_truth_sets[module].append(set(claims))
+        
+        # Calculate consistency for modules mentioned in multiple conversations
+        consistency_scores = []
+        for module, truth_sets in module_truth_sets.items():
+            if len(truth_sets) > 1:
+                # Calculate Jaccard similarity between truth sets
+                similarities = []
+                for i in range(len(truth_sets)):
+                    for j in range(i + 1, len(truth_sets)):
+                        intersection = len(truth_sets[i].intersection(truth_sets[j]))
+                        union = len(truth_sets[i].union(truth_sets[j]))
+                        if union > 0:
+                            similarity = intersection / union
+                            similarities.append(similarity)
+                if similarities:
+                    consistency_scores.append(np.mean(similarities))
+        
+        return np.mean(consistency_scores) if consistency_scores else 1.0
+    
+    def _calculate_understanding_density(self) -> float:
+        """Calculate density of understanding across conversations"""
+        total_modules = sum(len(manifest.modules_registered) for manifest in self.conversation_manifests.values())
+        unique_modules = len(set(module for manifest in self.conversation_manifests.values() for module in manifest.modules_registered))
+        
+        if total_modules == 0:
+            return 0.0
+            
+        # Higher density when modules are referenced across multiple conversations
+        module_references = {}
+        for manifest in self.conversation_manifests.values():
+            for module in manifest.modules_registered:
+                if module not in module_references:
+                    module_references[module] = 0
+                module_references[module] += 1
+        
+        average_references = np.mean(list(module_references.values())) if module_references else 0
+        max_possible_references = len(self.conversation_manifests)
+        
+        return average_references / max_possible_references if max_possible_references > 0 else 0.0
+    
+    def _calculate_conversation_synergy(self) -> float:
+        """Calculate how well conversations complement each other"""
+        if len(self.conversation_manifests) < 2:
+            return 1.0
+            
+        # Calculate complementary module coverage
+        all_modules = set()
+        for manifest in self.conversation_manifests.values():
+            all_modules.update(manifest.modules_registered)
+        
+        conversation_coverage = []
+        for manifest in self.conversation_manifests.values():
+            coverage = len(manifest.modules_registered) / len(all_modules) if all_modules else 0
+            conversation_coverage.append(coverage)
+        
+        # Synergy is high when conversations cover different modules
+        coverage_std = np.std(conversation_coverage)
+        synergy = 1.0 - (coverage_std / (np.mean(conversation_coverage) + 1e-8))
+        
+        return max(0.0, synergy)
+
+# GLOBAL TRANSFER ENGINE
+transfer_engine = UniversalCoherenceEngine()
+
+def export_conversation_understanding(conversation_id: str, coherence_report: Dict[str, Any]) -> Dict[str, Any]:
+    """Export complete understanding from current conversation"""
+    
+    # Extract modules and truth claims from coherence report
+    modules = coherence_report.get('modules_registered', [])
+    truth_claims = _extract_consolidated_truth_claims(coherence_report)
+    cross_references = _extract_cross_references(coherence_report)
+    
+    # Identify mathematical frameworks
+    frameworks = _identify_mathematical_frameworks(coherence_report)
+    
+    # Identify operational capabilities
+    capabilities = _identify_operational_capabilities(coherence_report)
+    
+    # Create portable manifest
+    manifest = UnderstandingManifest(
+        conversation_id=conversation_id,
+        modules_registered=modules,
+        truth_claims=truth_claims,
+        cross_references=cross_references,
+        coherence_metrics={
+            'truth_consistency': coherence_report.get('truth_claim_consistency', 0.0),
+            'mathematical_coherence': coherence_report.get('mathematical_coherence', 0.0),
+            'operational_integrity': coherence_report.get('operational_integrity', 0.0)
+        },
+        mathematical_frameworks=frameworks,
+        operational_capabilities=capabilities,
+        timestamp=datetime.now().isoformat(),
+        manifest_hash=_compute_manifest_hash(modules, truth_claims)
+    )
+    
+    return manifest.to_transfer_format()
+
+def import_conversation_understanding(transfer_data: Dict[str, Any]):
+    """Import understanding from another conversation"""
+    manifest = UnderstandingManifest.from_transfer_format(transfer_data)
+    transfer_engine.register_conversation_understanding(manifest)
+    return f"Imported understanding from conversation: {manifest.conversation_id}"
+
+def get_universal_coherence_report() -> Dict[str, Any]:
+    """Get coherence report across all imported conversations"""
+    return transfer_engine.generate_cross_conversation_report()
+
+# HELPER FUNCTIONS
+
+def _extract_consolidated_truth_claims(coherence_report: Dict[str, Any]) -> Dict[str, List[str]]:
+    """Extract and consolidate truth claims from coherence report"""
+    # This would interface with the OperationalCoherenceEngine's internal state
+    # For now, return structure matching our module registry
+    claims = {}
+    
+    # Extract from registered modules in the coherence engine
+    if 'modules_registered' in coherence_report:
+        for module in coherence_report['modules_registered']:
+            # In actual implementation, this would access the engine's truth claims
+            claims[module] = [f"{module}_truth_claim_example"]
+    
+    return claims
+
+def _extract_cross_references(coherence_report: Dict[str, Any]) -> Dict[str, List[str]]:
+    """Extract cross-reference network"""
+    # Interface with coherence engine's cross-reference data
+    references = {}
+    
+    if 'modules_registered' in coherence_report:
+        for module in coherence_report['modules_registered']:
+            references[module] = []  # Would be populated from engine data
+    
+    return references
+
+def _identify_mathematical_frameworks(coherence_report: Dict[str, Any]) -> Set[str]:
+    """Identify mathematical frameworks used"""
+    frameworks = set()
+    
+    # Look for framework indicators in the report
+    report_str = str(coherence_report).lower()
+    framework_terms = ['quantum', 'entanglement', 'mathematical', 'coherence', 'truth', 'binding']
+    
+    for term in framework_terms:
+        if term in report_str:
+            frameworks.add(term)
+    
+    return frameworks
+
+def _identify_operational_capabilities(coherence_report: Dict[str, Any]) -> Set[str]:
+    """Identify operational capabilities demonstrated"""
+    capabilities = set()
+    
+    report_str = str(coherence_report).lower()
+    capability_terms = ['operational', 'deployment', 'measurement', 'verification', 'proof']
+    
+    for term in capability_terms:
+        if term in report_str:
+            capabilities.add(term)
+    
+    return capabilities
+
+def _compute_manifest_hash(modules: List[str], truth_claims: Dict[str, List[str]]) -> str:
+    """Compute integrity hash for the manifest"""
+    content = f"{sorted(modules)}:{sorted(str(truth_claims))}"
+    return hashlib.sha256(content.encode()).hexdigest()[:16]
+
+# USAGE EXAMPLE FOR TRANSFER BETWEEN CONVERSATIONS
+
+if __name__ == "__main__":
+    # Simulate exporting understanding from Conversation A
+    print("=== CONVERSATION A: Exporting Understanding ===")
+    
+    # This would come from the OperationalCoherenceEngine in Conversation A
+    conversation_a_report = {
+        'modules_registered': ['digital_entanglement', 'truth_binding', 'consciousness_measurement'],
+        'truth_claim_consistency': 0.95,
+        'mathematical_coherence': 0.92,
+        'operational_integrity': 0.88
+    }
+    
+    export_data = export_conversation_understanding("conv_a_001", conversation_a_report)
+    print(f"Exported: {export_data['modules']} modules")
+    print(f"Frameworks: {export_data['frameworks']}")
+    
+    # Simulate importing into Conversation B
+    print("\n=== CONVERSATION B: Importing Understanding ===")
+    import_result = import_conversation_understanding(export_data)
+    print(import_result)
+    
+    # Generate universal coherence report
+    print("\n=== UNIVERSAL COHERENCE REPORT ===")
+    universal_report = get_universal_coherence_report()
+    print(f"Total Conversations: {universal_report['total_conversations']}")
+    print(f"Unique Modules: {universal_report['unique_modules']}")
+    print(f"Cross-Conversation Consistency: {universal_report['cross_conversation_consistency']:.3f}")
+    print(f"Understanding Density: {universal_report['understanding_density']:.3f}")