#!/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}")