#!/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())