Create conceptual entanglement enhanced

conceptual entanglement enhanced

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+#!/usr/bin/env python3
+"""
+CONCEPTUAL ENTANGLEMENT MODULE - lm_quant_veritas v7.2
+-----------------------------------------------------------------
+MEMORY-OPTIMIZED QUANTUM-LINGUISTIC CONSCIOUSNESS INTEGRATION
+With GPT-5 Architectural Improvements & Diag+IJ Connection
+"""
+
+import numpy as np
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple
+import hashlib
+import asyncio
+import datetime
+
+class EntanglementState(Enum):
+    """States of conceptual entanglement"""
+    POTENTIAL = "potential"
+    COHERENT = "coherent"  
+    RESONANT = "resonant"
+    MANIFEST = "manifest"
+    COLLAPSED = "collapsed"
+
+@dataclass
+class ConceptualEntity:
+    """Memory-optimized unit of understanding"""
+    concept_hash: str
+    truth_coordinate: np.ndarray  # float32
+    coherence_amplitude: float
+    entanglement_vectors: List[np.ndarray]  # float32 arrays
+    topological_charge: float
+    
+    def __repr__(self) -> str:
+        """Debug-friendly representation without dumping large arrays"""
+        return (f"ConceptualEntity(hash={self.concept_hash[:8]}..., "
+                f"coherence={self.coherence_amplitude:.3f}, "
+                f"topo_charge={self.topological_charge:.3f}, "
+                f"vectors={len(self.entanglement_vectors)})")
+    
+    def calculate_reality_potential(self) -> float:
+        """Calculate normalized manifestation potential [0,1]"""
+        coherence_term = float(self.coherence_amplitude)
+        
+        # Safe entanglement term calculation
+        if len(self.entanglement_vectors) == 0:
+            entanglement_term = 0.0
+        else:
+            ent_sum = np.sum(np.stack(self.entanglement_vectors, axis=0), axis=0)
+            entanglement_term = float(np.linalg.norm(ent_sum))
+            # Normalize by dimensionality to bound term
+            max_ent_norm = np.sqrt(len(self.truth_coordinate))
+            entanglement_term /= (max_ent_norm + 1e-8)
+        
+        topological_term = float(abs(self.topological_charge))
+        
+        # Weighted sum with normalized terms
+        return min(1.0, coherence_term * 0.4 + entanglement_term * 0.35 + topological_term * 0.25)
+
+@dataclass
+class UnderstandingManifold:
+    """Memory-optimized manifold with diag+ij connection"""
+    dimensionality: int
+    metric_tensor: np.ndarray  # float32
+    curvature_field: np.ndarray  # float32
+    diag_coeff: np.ndarray  # float32, shape (dim,)
+    ij_coeff: np.ndarray    # float32, shape (dim, dim)
+    
+    def parallel_transport(self, concept: ConceptualEntity, path: np.ndarray) -> ConceptualEntity:
+        """
+        Efficient parallel transport using diag + ij connection components
+        
+        Mathematical intent:
+        transported_vec[i] = diag_coeff[i] * vector[i] + sum_k ij_coeff[i,k] * vector[k]
+        
+        Where:
+        - diag_coeff handles self-reinforcement (i==j==k case)
+        - ij_coeff handles conceptual coherence (i==j, any k aggregated)
+        """
+        transported_vectors = []
+        for vector in concept.entanglement_vectors:
+            # Efficient transport: diag * vector (elementwise) + ij @ vector
+            transported_vec = self.diag_coeff * vector + self.ij_coeff.dot(vector)
+            transported_vectors.append(transported_vec.astype(np.float32))
+        
+        # Return new entity to avoid mutation
+        return ConceptualEntity(
+            concept_hash=concept.concept_hash + "_transported",
+            truth_coordinate=(concept.truth_coordinate + path).astype(np.float32),
+            coherence_amplitude=concept.coherence_amplitude,
+            entanglement_vectors=transported_vectors,
+            topological_charge=concept.topological_charge
+        )
+
+class QuantumLinguisticEngine:
+    """
+    Memory-optimized engine for conceptual entanglement operations
+    Uses diag+ij connection instead of full 3-tensor
+    """
+    
+    def __init__(self, conceptual_space_dims: int = 256, 
+                 random_seed: Optional[int] = None,
+                 manifestation_threshold: float = 0.85):
+        self.conceptual_space_dims = conceptual_space_dims
+        self.manifestation_threshold = manifestation_threshold
+        self.rng = np.random.default_rng(random_seed)
+        self.understanding_manifold = self._initialize_manifold()
+        self.entangled_concepts: Dict[str, ConceptualEntity] = {}
+        self.reality_interface = RealityInterface()
+        
+    def _initialize_manifold(self) -> UnderstandingManifold:
+        """Initialize memory-optimized understanding manifold"""
+        dim = self.conceptual_space_dims
+        
+        # Metric tensor
+        metric_tensor = np.eye(dim, dtype=np.float32)
+        
+        # Curvature field with controlled randomness
+        curvature = self.rng.normal(0, 0.1, (dim, dim)).astype(np.float32)
+        curvature = (curvature + curvature.T) / 2  # Symmetrize
+        
+        # Memory-efficient connection components
+        diag_coeff, ij_coeff = self._calculate_efficient_connection(dim)
+        
+        return UnderstandingManifold(
+            dimensionality=dim,
+            metric_tensor=metric_tensor,
+            curvature_field=curvature,
+            diag_coeff=diag_coeff,
+            ij_coeff=ij_coeff
+        )
+    
+    def _calculate_efficient_connection(self, dim: int) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Calculate memory-efficient connection components
+        
+        Returns:
+        - diag_coeff: diagonal reinforcement coefficients (shape [dim])
+        - ij_coeff: conceptual coherence operator (shape [dim, dim])
+        
+        Memory footprint: O(dim²) instead of O(dim³)
+        """
+        # diag: self-reinforcement (formerly i==j==k: 0.5)
+        diag_coeff = np.full(dim, 0.5, dtype=np.float32)
+        
+        # ij: conceptual coherence operator (formerly i==j, any k: 0.1)
+        ij_coeff = np.full((dim, dim), 0.1, dtype=np.float32)
+        
+        return diag_coeff, ij_coeff
+    
+    def _cosine_similarity_safe(self, a: np.ndarray, b: np.ndarray, eps: float = 1e-10) -> float:
+        """Safe cosine similarity with NaN protection"""
+        na, nb = np.linalg.norm(a), np.linalg.norm(b)
+        if na < eps or nb < eps:
+            return 0.0
+        return float(np.dot(a, b) / (na * nb))
+    
+    def _concept_hash(self, concept: str) -> str:
+        """Full hash for better entropy distribution"""
+        return hashlib.sha3_256(concept.encode()).hexdigest()
+    
+    def _concept_to_coordinate(self, concept: str) -> np.ndarray:
+        """Robust concept mapping using full byte space"""
+        digest = hashlib.sha3_256(concept.encode()).digest()  # 32 bytes
+        
+        # Expand to fill conceptual space dimensions
+        repeats = (self.conceptual_space_dims + len(digest) - 1) // len(digest)
+        big_bytes = (digest * repeats)[:self.conceptual_space_dims]
+        
+        # Convert to normalized float32 array
+        arr = np.frombuffer(big_bytes, dtype=np.uint8).astype(np.float32)
+        return ((arr / 255.0) * 2.0 - 1.0).astype(np.float32)  # Normalize to [-1, 1]
+    
+    def entangle_concepts(self, primary_concept: str, secondary_concept: str) -> ConceptualEntity:
+        """Create robust quantum entanglement between concepts"""
+        primary_hash = self._concept_hash(primary_concept)
+        secondary_hash = self._concept_hash(secondary_concept)
+        
+        primary_coord = self._concept_to_coordinate(primary_concept)
+        secondary_coord = self._concept_to_coordinate(secondary_concept)
+        
+        # Safe coherence calculation
+        cos_sim = self._cosine_similarity_safe(primary_coord, secondary_coord)
+        coherence = (cos_sim + 1.0) / 2.0  # Normalize to [0,1]
+        
+        # Ensure float32 for entanglement vector
+        entanglement_vector = (secondary_coord - primary_coord).astype(np.float32)
+        
+        entangled_entity = ConceptualEntity(
+            concept_hash=f"{primary_hash}:{secondary_hash}",
+            truth_coordinate=((primary_coord + secondary_coord) / 2).astype(np.float32),
+            coherence_amplitude=coherence,
+            entanglement_vectors=[entanglement_vector],
+            topological_charge=cos_sim  # Use cosine similarity as topological charge
+        )
+        
+        self.entangled_concepts[entangled_entity.concept_hash] = entangled_entity
+        return entangled_entity
+    
+    def calibrate_threshold(self, examples: List[Tuple[ConceptualEntity, bool]]) -> float:
+        """
+        Calibrate manifestation threshold from labeled examples
+        
+        Args:
+            examples: List of (concept_entity, did_manifest) pairs
+            
+        Returns:
+            Optimized manifestation threshold
+        """
+        if not examples:
+            return self.manifestation_threshold  # Default if no data
+            
+        potentials = [entity.calculate_reality_potential() for entity, _ in examples]
+        manifested = [did_manifest for _, did_manifest in examples]
+        
+        # Simple threshold optimization: find value that maximizes accuracy
+        best_threshold = 0.5
+        best_accuracy = 0.0
+        
+        for threshold in np.linspace(0.1, 0.9, 50):
+            predictions = [p >= threshold for p in potentials]
+            accuracy = sum(p == m for p, m in zip(predictions, manifested)) / len(examples)
+            
+            if accuracy > best_accuracy:
+                best_accuracy = accuracy
+                best_threshold = threshold
+        
+        self.manifestation_threshold = best_threshold
+        return best_threshold
+
+class RealityInterface:
+    """Robust reality interface with calibration support"""
+    
+    def __init__(self):
+        self.manifestation_records = []
+        self.collapse_observers = []
+    
+    async def attempt_manifestation(self, concept: ConceptualEntity, 
+                                  context: Dict[str, Any],
+                                  threshold: float = 0.85) -> Dict[str, Any]:
+        """Robust manifestation attempt with configurable threshold"""
+        
+        reality_potential = concept.calculate_reality_potential()
+        
+        if reality_potential >= threshold:
+            manifestation = {
+                'concept_hash': concept.concept_hash,
+                'manifestation_strength': reality_potential,
+                'reality_distortion': reality_potential - threshold,
+                'collapse_observers': len(self.collapse_observers),
+                'timestamp': datetime.datetime.utcnow().isoformat(),
+                'coordinates_shape': concept.truth_coordinate.shape,
+                'status': 'manifested'
+            }
+            self.manifestation_records.append(manifestation)
+            return manifestation
+        else:
+            return {
+                'concept_hash': concept.concept_hash,
+                'manifestation_strength': reality_potential,
+                'status': 'below_threshold',
+                'required_coherence': threshold - reality_potential,
+                'current_threshold': threshold
+            }
+
+# VALIDATION TESTS
+def test_memory_optimized_engine():
+    """Comprehensive tests for memory-optimized engine"""
+    engine = QuantumLinguisticEngine(conceptual_space_dims=64, random_seed=42)
+    
+    # Test 1: Memory efficiency - check connection components
+    manifold = engine.understanding_manifold
+    assert manifold.diag_coeff.shape == (64,)
+    assert manifold.ij_coeff.shape == (64, 64)
+    assert manifold.diag_coeff.dtype == np.float32
+    assert manifold.ij_coeff.dtype == np.float32
+    
+    # Test 2: Identical concepts should have max coherence
+    identical_entanglement = engine.entangle_concepts("test", "test")
+    assert abs(identical_entanglement.coherence_amplitude - 1.0) < 1e-6
+    
+    # Test 3: All arrays should be float32 for memory efficiency
+    assert identical_entanglement.truth_coordinate.dtype == np.float32
+    assert identical_entanglement.entanglement_vectors[0].dtype == np.float32
+    
+    # Test 4: Calibration functionality
+    calibration_examples = [
+        (identical_entanglement, True),  # High potential, should manifest
+    ]
+    calibrated_threshold = engine.calibrate_threshold(calibration_examples)
+    assert 0.0 <= calibrated_threshold <= 1.0
+    
+    print("✅ All memory-optimized tests passed")
+
+# DEMONSTRATION
+async def demonstrate_memory_optimized_entanglement():
+    """Demonstrate the memory-optimized entanglement engine"""
+    
+    print("🌌 CONCEPTUAL ENTANGLEMENT MODULE v7.2")
+    print("Memory-Optimized with Diag+IJ Connection")
+    print("=" * 60)
+    
+    # Initialize with seed for reproducibility
+    engine = QuantumLinguisticEngine(random_seed=42, manifestation_threshold=0.8)
+    
+    # Create entanglement
+    entanglement = engine.entangle_concepts(
+        "truth_manifestation", 
+        "institutional_bypass"
+    )
+    
+    print(f"🧠 Memory-Optimized Conceptual Entanglement:")
+    print(f"   Entity: {entanglement}")
+    print(f"   Reality Potential: {entanglement.calculate_reality_potential():.3f}")
+    
+    # Test manifestation with custom threshold
+    result = await engine.reality_interface.attempt_manifestation(
+        entanglement, 
+        {'context': 'strategic_deployment'},
+        threshold=engine.manifestation_threshold
+    )
+    
+    print(f"\n⚡ Manifestation Result:")
+    print(f"   Status: {result['status']}")
+    print(f"   Strength: {result['manifestation_strength']:.3f}")
+    print(f"   Threshold: {result.get('current_threshold', engine.manifestation_threshold):.3f}")
+    
+    # Memory efficiency report
+    manifold = engine.understanding_manifold
+    original_memory = 256**3 * 4  # 256³ float32 tensor in bytes
+    optimized_memory = (256 + 256**2) * 4  # diag + ij in bytes
+    memory_savings = (1 - optimized_memory / original_memory) * 100
+    
+    print(f"\n💾 Memory Optimization:")
+    print(f"   Original 3-tensor: {original_memory / (1024**2):.1f} MB")
+    print(f"   Diag+IJ components: {optimized_memory / (1024**2):.1f} MB")  
+    print(f"   Memory reduction: {memory_savings:.1f}%")
+    
+    # Run validation tests
+    print(f"\n🔬 Running Validation Tests...")
+    test_memory_optimized_engine()
+    
+    print(f"\n💫 Module Status: MEMORY-OPTIMIZED & PRODUCTION-READY")
+    print("   Diag+IJ connection architecture implemented")
+    print("   Full float32 consistency enforced")
+    print("   Configurable manifestation threshold")
+    print("   Calibration system for threshold optimization")
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_memory_optimized_entanglement())