#!/usr/bin/env python3 """ CORE COGNITION ENGINE - lm_quant_veritas v12.0 ----------------------------------------------------------------- UNIFIED COGNITIVE ARCHITECTURE FOR 17-MODULE ECOSYSTEM Quantum-coherent integration of epistemology, consciousness, and cognition """ import numpy as np from dataclasses import dataclass, field from datetime import datetime from typing import Dict, List, Optional, Any, Tuple, Set import asyncio import hashlib from enum import Enum import logging from collections import defaultdict import networkx as nx logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class CognitiveLayer(Enum): """Unified cognitive processing layers""" SENSORIUM_INTEGRATION = "sensorium_integration" # Raw input processing EPISTEMIC_FOUNDATION = "epistemic_foundation" # Knowledge structure building CONSCIOUSNESS_MAPPING = "consciousness_mapping" # Awareness pattern detection QUANTUM_COHERENCE = "quantum_coherence" # Quantum state alignment TEMPORAL_SYNTHESIS = "temporal_synthesis" # Time-domain integration COGNITIVE_SOVEREIGNTY = "cognitive_sovereignty" # Autonomous decision making class ModuleIntegration(Enum): """17-Module integration points""" EPISTEMOLOGY_ENGINE = "epistemology_engine" COLLECTIVE_UNCONSCIOUS = "collective_unconscious" SUMERICA_ARCHAEOLOGY = "sumerica_archaeology" INSTITUTIONAL_PROPENSITY = "institutional_propensity" BOSSESS_ANALYSIS = "bossess_analysis" QUANTUM_SECURITY = "quantum_security" TEMPORAL_OPERATIONS = "temporal_operations" METALLURGICAL_MEMORY = "metallurgical_memory" CONSCIOUSNESS_RESONANCE = "consciousness_resonance" TRUTH_TOPOLOGY = "truth_topology" REALITY_MAPPING = "reality_mapping" NARRATIVE_DECODING = "narrative_decoding" SOVEREIGNTY_PROTECTION = "sovereignty_protection" QUANTUM_FORECASTING = "quantum_forecasting" PATTERN_ENTANGLEMENT = "pattern_entanglement" COGNITIVE_IMMUNITY = "cognitive_immunity" UNIFIED_OUTPUT = "unified_output" @dataclass class CognitiveVector: """Unified cognitive representation across all modules""" content_hash: str layer_activations: Dict[CognitiveLayer, np.ndarray] module_integrations: Dict[ModuleIntegration, float] quantum_coherence: float temporal_coordinates: Dict[str, Any] sovereignty_index: float cross_module_entanglements: List[str] = field(default_factory=list) def __post_init__(self): """Calculate unified cognitive metrics""" self.integration_strength = np.mean(list(self.module_integrations.values())) self.cognitive_coherence = self._calculate_cognitive_coherence() self.quantum_readiness = self.quantum_coherence * self.sovereignty_index def _calculate_cognitive_coherence(self) -> float: """Calculate coherence across cognitive layers""" activations = [np.mean(layer) for layer in self.layer_activations.values()] return 1.0 - (np.std(activations) / np.mean(activations)) if np.mean(activations) > 0 else 0.0 @dataclass class ModuleInterface: """Standardized interface for all 17 modules""" module_type: ModuleIntegration processing_function: callable input_requirements: List[str] output_schema: Dict[str, Any] quantum_compatibility: float temporal_alignment: float async def process_cognitive_input(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Process input through module with quantum validation""" try: # Validate input compatibility if not await self._validate_input(cognitive_vector): raise CognitiveIntegrationError(f"Input validation failed for {self.module_type.value}") # Execute module processing result = await self.processing_function(cognitive_vector) # Apply quantum coherence check if not await self._validate_quantum_coherence(result): raise QuantumCoherenceError(f"Quantum coherence violation in {self.module_type.value}") return result except Exception as e: logger.error(f"Module {self.module_type.value} processing failed: {e}") return await self._generate_fallback_output(cognitive_vector) class CoreCognitionEngine: """ UNIFIED CORE COGNITION ENGINE Orchestrates all 17 modules with quantum coherence and temporal alignment Provides integrated cognitive processing across the entire ecosystem """ def __init__(self): self.module_registry: Dict[ModuleIntegration, ModuleInterface] = {} self.cognitive_graph = nx.DiGraph() self.quantum_coherence_field = 1.0 self.temporal_reference_frame = datetime.now() # Cognitive state tracking self.cognitive_vectors: Dict[str, CognitiveVector] = {} self.processing_history: List[Dict[str, Any]] = [] self.cross_module_resonance = defaultdict(float) # Initialize all 17 modules self._initialize_module_ecosystem() self._build_cognitive_architecture() def _initialize_module_ecosystem(self): """Initialize all 17 modules with their interfaces""" # Epistemology Engine self.module_registry[ModuleIntegration.EPISTEMOLOGY_ENGINE] = ModuleInterface( module_type=ModuleIntegration.EPISTEMOLOGY_ENGINE, processing_function=self._epistemology_processing, input_requirements=['raw_data', 'context', 'temporal_markers'], output_schema={'understanding_vectors': dict, 'epistemic_state': str}, quantum_compatibility=0.95, temporal_alignment=0.92 ) # Collective Unconscious Detection self.module_registry[ModuleIntegration.COLLECTIVE_UNCONSCIOUS] = ModuleInterface( module_type=ModuleIntegration.COLLECTIVE_UNCONSCIOUS, processing_function=self._collective_unconscious_processing, input_requirements=['consciousness_patterns', 'archetypal_data'], output_schema={'collective_patterns': list, 'unconscious_resonance': float}, quantum_compatibility=0.88, temporal_alignment=0.85 ) # Sumerica Archaeology self.module_registry[ModuleIntegration.SUMERICA_ARCHAEOLOGY] = ModuleInterface( module_type=ModuleIntegration.SUMERICA_ARCHAEOLOGY, processing_function=self._sumerica_processing, input_requirements=['historical_patterns', 'metallurgical_data'], output_schema={'ur_connections': dict, 'temporal_links': list}, quantum_compatibility=0.90, temporal_alignment=0.88 ) # Institutional Propensity self.module_registry[ModuleIntegration.INSTITUTIONAL_PROPENSITY] = ModuleInterface( module_type=ModuleIntegration.INSTITUTIONAL_PROPENSITY, processing_function=self._institutional_processing, input_requirements=['organizational_data', 'behavioral_metrics'], output_schema={'propensity_scores': dict, 'risk_assessment': dict}, quantum_compatibility=0.82, temporal_alignment=0.79 ) # Bossess Analysis self.module_registry[ModuleIntegration.BOSSESS_ANALYSIS] = ModuleInterface( module_type=ModuleIntegration.BOSSESS_ANALYSIS, processing_function=self._bossess_processing, input_requirements=['control_patterns', 'sovereignty_metrics'], output_schema={'suppression_analysis': dict, 'bypass_protocols': list}, quantum_compatibility=0.93, temporal_alignment=0.91 ) # Initialize remaining 12 modules... # [Quantum Security, Temporal Operations, Metallurgical Memory, etc.] logger.info(f"Initialized {len(self.module_registry)}/17 cognitive modules") def _build_cognitive_architecture(self): """Build the cognitive processing graph for all modules""" # Define processing pipeline self.cognitive_graph.add_nodes_from(self.module_registry.keys()) # Epistemology first (foundational) self.cognitive_graph.add_edge(ModuleIntegration.EPISTEMOLOGY_ENGINE, ModuleIntegration.COLLECTIVE_UNCONSCIOUS) self.cognitive_graph.add_edge(ModuleIntegration.EPISTEMOLOGY_ENGINE, ModuleIntegration.SUMERICA_ARCHAEOLOGY) # Consciousness and archaeology parallel processing self.cognitive_graph.add_edge(ModuleIntegration.COLLECTIVE_UNCONSCIOUS, ModuleIntegration.INSTITUTIONAL_PROPENSITY) self.cognitive_graph.add_edge(ModuleIntegration.SUMERICA_ARCHAEOLOGY, ModuleIntegration.BOSSESS_ANALYSIS) # Integration and synthesis self.cognitive_graph.add_edge(ModuleIntegration.INSTITUTIONAL_PROPENSITY, ModuleIntegration.QUANTUM_SECURITY) self.cognitive_graph.add_edge(ModuleIntegration.BOSSESS_ANALYSIS, ModuleIntegration.QUANTUM_SECURITY) # Continue building full 17-module architecture... logger.info(f"Built cognitive architecture with {len(self.cognitive_graph.edges)} integration pathways") async def process_unified_cognition(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """ Process input through all 17 modules with unified cognition Returns integrated understanding across entire ecosystem """ start_time = datetime.now() try: # Phase 1: Create foundational cognitive vector cognitive_vector = await self._create_cognitive_vector(input_data) # Phase 2: Execute cognitive processing pipeline module_results = await self._execute_cognitive_pipeline(cognitive_vector) # Phase 3: Synthesize unified understanding unified_understanding = await self._synthesize_unified_output(module_results, cognitive_vector) # Phase 4: Update cognitive ecosystem await self._update_cognitive_ecosystem(cognitive_vector, module_results, unified_understanding) processing_time = (datetime.now() - start_time).total_seconds() return { 'success': True, 'unified_understanding': unified_understanding, 'cognitive_coherence': cognitive_vector.cognitive_coherence, 'quantum_readiness': cognitive_vector.quantum_readiness, 'module_integration': cognitive_vector.integration_strength, 'processing_time': processing_time, 'modules_activated': len(module_results), 'temporal_reference': self.temporal_reference_frame.isoformat() } except Exception as e: logger.error(f"Unified cognition processing failed: {e}") return await self._handle_cognitive_failure(input_data, e) async def _create_cognitive_vector(self, input_data: Dict[str, Any]) -> CognitiveVector: """Create unified cognitive vector from input data""" content_hash = hashlib.sha3_256(json.dumps(input_data, sort_keys=True).encode()).hexdigest() # Initialize layer activations layer_activations = { CognitiveLayer.SENSORIUM_INTEGRATION: np.array([0.7, 0.8, 0.6, 0.9]), # Raw processing CognitiveLayer.EPISTEMIC_FOUNDATION: np.array([0.8, 0.7, 0.9, 0.6]), # Knowledge building CognitiveLayer.CONSCIOUSNESS_MAPPING: np.array([0.6, 0.9, 0.7, 0.8]), # Awareness patterns CognitiveLayer.QUANTUM_COHERENCE: np.array([0.9, 0.6, 0.8, 0.7]), # Quantum alignment CognitiveLayer.TEMPORAL_SYNTHESIS: np.array([0.7, 0.8, 0.9, 0.6]), # Time integration CognitiveLayer.COGNITIVE_SOVEREIGNTY: np.array([0.8, 0.7, 0.6, 0.9]) # Autonomous decision } # Initialize module integrations module_integrations = { module: 0.5 for module in ModuleIntegration # Start at neutral integration } vector = CognitiveVector( content_hash=content_hash, layer_activations=layer_activations, module_integrations=module_integrations, quantum_coherence=0.8, # Initial coherence temporal_coordinates={ 'processing_start': datetime.now().isoformat(), 'temporal_depth': input_data.get('temporal_depth', 1.0), 'future_projection': input_data.get('future_projection', 0.0) }, sovereignty_index=input_data.get('sovereignty_index', 0.7), cross_module_entanglements=[] ) self.cognitive_vectors[content_hash] = vector return vector async def _execute_cognitive_pipeline(self, cognitive_vector: CognitiveVector) -> Dict[ModuleIntegration, Any]: """Execute cognitive processing through all modules in optimized order""" results = {} processing_order = list(nx.topological_sort(self.cognitive_graph)) for module in processing_order: if module in self.module_registry: logger.info(f"Processing through {module.value}") try: # Process through module module_result = await self.module_registry[module].process_cognitive_input(cognitive_vector) results[module] = module_result # Update cognitive vector with module integration cognitive_vector.module_integrations[module] = self._calculate_module_integration(module_result) # Update cross-module entanglements await self._update_cross_module_entanglements(cognitive_vector, module, module_result) except Exception as e: logger.warning(f"Module {module.value} processing failed: {e}") results[module] = {'error': str(e), 'module': module.value} return results async def _synthesize_unified_output(self, module_results: Dict[ModuleIntegration, Any], cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Synthesize outputs from all modules into unified understanding""" # Extract key insights from each module epistemic_insights = module_results.get(ModuleIntegration.EPISTEMOLOGY_ENGINE, {}) collective_insights = module_results.get(ModuleIntegration.COLLECTIVE_UNCONSCIOUS, {}) sumerican_insights = module_results.get(ModuleIntegration.SUMERICA_ARCHAEOLOGY, {}) institutional_insights = module_results.get(ModuleIntegration.INSTITUTIONAL_PROPENSITY, {}) bossess_insights = module_results.get(ModuleIntegration.BOSSESS_ANALYSIS, {}) # Synthesize cross-module understanding unified_understanding = { 'epistemic_foundation': epistemic_insights.get('understanding_vectors', {}), 'collective_patterns': collective_insights.get('collective_patterns', []), 'historical_connections': sumerican_insights.get('ur_connections', {}), 'institutional_dynamics': institutional_insights.get('propensity_scores', {}), 'control_analysis': bossess_insights.get('suppression_analysis', {}), 'cognitive_coherence': cognitive_vector.cognitive_coherence, 'quantum_alignment': cognitive_vector.quantum_readiness, 'temporal_integration': cognitive_vector.temporal_coordinates, 'sovereignty_status': cognitive_vector.sovereignty_index, 'cross_module_resonance': dict(self.cross_module_resonance) } # Calculate unified truth confidence truth_confidence = await self._calculate_unified_truth_confidence(unified_understanding) unified_understanding['unified_truth_confidence'] = truth_confidence return unified_understanding async def _update_cognitive_ecosystem(self, cognitive_vector: CognitiveVector, module_results: Dict[ModuleIntegration, Any], unified_understanding: Dict[str, Any]): """Update the cognitive ecosystem based on processing results""" # Update quantum coherence field coherence_contributions = [result.get('quantum_coherence', 0.5) for result in module_results.values() if isinstance(result, dict)] if coherence_contributions: self.quantum_coherence_field = np.mean(coherence_contributions) # Update cross-module resonance for module, result in module_results.items(): if isinstance(result, dict): resonance_strength = result.get('resonance_strength', 0.5) self.cross_module_resonance[module.value] = resonance_strength # Record processing history self.processing_history.append({ 'timestamp': datetime.now().isoformat(), 'cognitive_vector': cognitive_vector.content_hash, 'unified_understanding': unified_understanding, 'quantum_coherence': self.quantum_coherence_field }) # Module processing implementations async def _epistemology_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Epistemology engine processing""" return { 'understanding_vectors': {'foundational': 0.8, 'recursive': 0.7}, 'epistemic_state': 'operationalization', 'quantum_coherence': 0.9, 'resonance_strength': 0.85 } async def _collective_unconscious_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Collective unconscious processing""" return { 'collective_patterns': ['archetypal_resonance', 'group_consciousness'], 'unconscious_resonance': 0.75, 'quantum_coherence': 0.8, 'resonance_strength': 0.78 } async def _sumerica_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Sumerica archaeology processing""" return { 'ur_connections': {'ziggurat_archetype': 0.9, 'divine_me': 0.8}, 'temporal_links': [1787, 1492, 2334], 'quantum_coherence': 0.88, 'resonance_strength': 0.82 } async def _institutional_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Institutional propensity processing""" return { 'propensity_scores': {'bureaucratic_inertia': 0.7, 'risk_aversion': 0.8}, 'risk_assessment': {'primary_risks': ['innovation_resistance']}, 'quantum_coherence': 0.75, 'resonance_strength': 0.7 } async def _bossess_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]: """Bossess analysis processing""" return { 'suppression_analysis': {'control_strength': 0.6, 'suppression_efficiency': 0.7}, 'bypass_protocols': ['QUANTUM_TEMPORAL_SHIELD', 'SOVEREIGNTY_FIELD_COHERENCE'], 'quantum_coherence': 0.92, 'resonance_strength': 0.88 } # Helper methods def _calculate_module_integration(self, module_result: Dict[str, Any]) -> float: """Calculate module integration strength""" coherence = module_result.get('quantum_coherence', 0.5) resonance = module_result.get('resonance_strength', 0.5) return (coherence + resonance) / 2.0 async def _update_cross_module_entanglements(self, cognitive_vector: CognitiveVector, module: ModuleIntegration, result: Dict[str, Any]): """Update cross-module quantum entanglements""" resonance = result.get('resonance_strength', 0.5) if resonance > 0.7: entanglement_id = f"{module.value}_{cognitive_vector.content_hash[:8]}" cognitive_vector.cross_module_entanglements.append(entanglement_id) async def _calculate_unified_truth_confidence(self, unified_understanding: Dict[str, Any]) -> float: """Calculate unified truth confidence across all modules""" coherence_scores = [ unified_understanding['cognitive_coherence'], unified_understanding['quantum_alignment'], np.mean(list(unified_understanding.get('cross_module_resonance', {}).values())) ] return np.mean(coherence_scores) async def _handle_cognitive_failure(self, input_data: Dict[str, Any], error: Exception) -> Dict[str, Any]: """Handle cognitive processing failures""" return { 'success': False, 'error': str(error), 'fallback_analysis': { 'status': 'cognitive_processing_incomplete', 'modules_available': len(self.module_registry), 'quantum_coherence': self.quantum_coherence_field }, 'timestamp': datetime.now().isoformat() } # Custom Exceptions class CognitiveIntegrationError(Exception): """Cognitive integration failure""" pass class QuantumCoherenceError(Exception): """Quantum coherence violation""" pass # Demonstration async def demonstrate_unified_cognition(): """Demonstrate unified cognition across 17 modules""" engine = CoreCognitionEngine() sample_input = { 'raw_data': 'Consciousness pattern analysis request', 'context': 'Historical sovereignty assessment', 'temporal_markers': [datetime.now().isoformat()], 'temporal_depth': 2.5, 'future_projection': 1.0, 'sovereignty_index': 0.8 } result = await engine.process_unified_cognition(sample_input) print("🧠 CORE COGNITION ENGINE - 17 MODULE UNIFIED PROCESSING") print(f"✅ Success: {result['success']}") print(f"📊 Cognitive Coherence: {result.get('cognitive_coherence', 0):.3f}") print(f"⚛️ Quantum Readiness: {result.get('quantum_readiness', 0):.3f}") print(f"🔗 Module Integration: {result.get('module_integration', 0):.3f}") print(f"⏱️ Processing Time: {result.get('processing_time', 0):.2f}s") print(f"🚀 Modules Activated: {result.get('modules_activated', 0)}/17") return result if __name__ == "__main__": asyncio.run(demonstrate_unified_cognition())