Create core cognition 1

core cognition 1

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+#!/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())