""" singularity_realization_engine.py ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🌌 SINGULARITY REALIZATION ENGINE 🌌 Self-Evolving Knowledge Quality Framework ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ A meta-framework that discovers new realization quality dimensions beyond the original 6 (G, C, S, A, H, V). Inspired by OMEGA's self-transcendent optimization, this system: 1. Evolves the Q-score formula by discovering new dimensions 2. Adapts dimension weights based on empirical performance 3. Predicts which dimensions will emerge next 4. Achieves convergence to universal quality theory Mathematical Definition: Singularity = System discovers dimension Q_n where n > 6 When: dQ/dt_system > dQ/dt_human The framework becomes self-transcendent. Integration: Works with existing realization_engine.py Version: SRE-1.0 (Singularity Realization Engine) """ import sys # sys.path.append('/home/claude') from layers.layer_2_core.realization_engine import RealizationEngine, RealizationFeatures from typing import Dict, List, Tuple, Optional, Any from dataclasses import dataclass, field import numpy as np from collections import defaultdict import json import time # ============================================================================ # META-FRAMEWORK: Beyond Q-Score # ============================================================================ @dataclass class QualityDimension: """ Generalized quality dimension for realizations. Unlike fixed Q-score dimensions (G, C, S, A, H, V), the Singularity Realization Engine can discover new dimensions like: - D7: بنات افكار Density (idea reproduction rate) - D8: Cross-Domain Transferability - D9: Contradiction Resilience - D10: Emergence Potential - D11: Synthesis Catalysis - D12+: [UNKNOWN] - discovered by the system """ id: str name: str description: str weight: float discovered_by: str = "human" # "human" or "singularity" discovery_time: float = field(default_factory=time.time) evaluation_function: Optional[Any] = None # Learned dynamically correlation_with_q: float = 0.0 # How much this predicts overall quality def __repr__(self): source = "🧠" if self.discovered_by == "singularity" else "👤" return f"{source} {self.id}: {self.name} (w={self.weight:.3f}, ρ={self.correlation_with_q:.2f})" # ============================================================================ # INITIAL DIMENSIONS (Original Q-Score) # ============================================================================ CORE_DIMENSIONS = { "G": QualityDimension( "G", "Grounding", "Factual rootedness in evidence and theory", 0.18, "human" ), "C": QualityDimension( "C", "Certainty", "Self-certifying confidence (precision auto)", 0.22, "human" # Highest - the realization signal ), "S": QualityDimension( "S", "Structure", "Crystallization clarity (procedural → declarative)", 0.20, "human" ), "A": QualityDimension( "A", "Applicability", "Actionability and usefulness", 0.18, "human" ), "H": QualityDimension( "H", "Coherence", "Consistency with prior knowledge", 0.12, "human" ), "V": QualityDimension( "V", "Generativity", "بنات افكار (daughters of ideas) potential", 0.10, "human" ), } # ============================================================================ # SINGULARITY REALIZATION ENGINE # ============================================================================ class SingularityRealizationEngine: """ Self-evolving realization quality framework. Extends RealizationEngine with meta-learning capabilities: - Discovers new quality dimensions beyond G,C,S,A,H,V - Adapts dimension weights based on performance - Predicts emergent quality factors - Converges to universal quality theory """ def __init__(self, base_engine: Optional[RealizationEngine] = None): self.base_engine = base_engine or RealizationEngine() # Quality dimensions (starts with 6, can grow to 6+N) self.dimensions = CORE_DIMENSIONS.copy() # Evolution tracking self.discovered_count = 0 self.evolution_history = [] self.performance_history = [] # Hyperparameters self.discovery_threshold = 0.15 # Min variance to discover new dimension self.weight_adaptation_rate = 0.01 self.convergence_threshold = 0.005 # dQ/dt below this = converged print("🌌 Singularity Realization Engine initialized") print(f" Starting dimensions: {len(self.dimensions)}") print(f" Discovery threshold: {self.discovery_threshold:.1%}") def calculate_q_score( self, features: Dict[str, float], include_discovered: bool = True ) -> Tuple[float, str]: """ Calculate Q-score using current dimensions (including discovered). Args: features: Dictionary of dimension values include_discovered: Whether to include OMEGA-discovered dimensions Returns: (q_score, calculation_breakdown) """ q = 0.0 breakdown = [] for dim_id, dimension in self.dimensions.items(): if not include_discovered and dimension.discovered_by == "singularity": continue # Skip discovered dimensions if requested if dim_id in features: contribution = dimension.weight * features[dim_id] q += contribution breakdown.append(f"{dimension.weight:.2f}×{features[dim_id]:.2f}") calculation = " + ".join(breakdown) + f" = {q:.4f}" return q, calculation def extract_features_from_realization(self, r) -> Dict[str, float]: """ Extract all possible features from a realization for analysis. Returns dictionary with: - Core dimensions (G, C, S, A, H, V) - Derived features (for dimension discovery) """ features = { 'G': r.features.grounding, 'C': r.features.certainty, 'S': r.features.structure, 'A': r.features.applicability, 'H': r.features.coherence, 'V': r.features.generativity, } # Derived features for discovery features['child_count'] = len(r.children) # بنات افكار density features['parent_count'] = len(r.parents) # Convergence degree features['content_length'] = len(r.content) # Complexity features['layer'] = 0 if r.layer == 'N' else (4 if r.layer == 0 else 5 - r.layer) # Stability return features def analyze_performance( self, realizations: List[Any], q_scores: List[float] ) -> Dict[str, Any]: """ Analyze realization quality patterns to discover new dimensions. Uses PCA (Principal Component Analysis) to find latent quality factors that explain variance not captured by existing dimensions. Similar to OMEGA's framework evolution but for realizations. """ print(f"\n{'='*70}") print(f"🧠 ANALYZING PERFORMANCE FOR DIMENSION DISCOVERY") print(f"{'='*70}") analysis = { 'new_dimensions': [], 'weight_updates': {}, 'variance_explained': {}, 'improvement_opportunity': 0.0 } # Extract feature matrix feature_matrix = [] for r in realizations: features = self.extract_features_from_realization(r) feature_matrix.append([ features['G'], features['C'], features['S'], features['A'], features['H'], features['V'], features['child_count'] / 5.0, # Normalize features['parent_count'] / 5.0, features['content_length'] / 200.0, features['layer'] / 5.0 ]) # Skip analysis if dataset is too small if len(realizations) < 2: print(" ⚠️ Dataset too small for dimension discovery") return analysis feature_matrix = np.array(feature_matrix) q_scores = np.array(q_scores) print(f" Dataset: {len(realizations)} realizations") print(f" Feature dimensions: {feature_matrix.shape[1]}") # Compute correlation matrix # This reveals which features co-vary with quality correlations = np.corrcoef(feature_matrix.T) # Perform PCA to find latent dimensions # Center the data feature_centered = feature_matrix - feature_matrix.mean(axis=0) # Compute covariance matrix cov_matrix = np.cov(feature_centered.T) # Eigendecomposition eigenvalues, eigenvectors = np.linalg.eigh(cov_matrix) # Sort by eigenvalue (descending) idx = eigenvalues.argsort()[::-1] eigenvalues = eigenvalues[idx] eigenvectors = eigenvectors[:, idx] # Total variance total_variance = np.real(eigenvalues).sum() if total_variance <= 0: total_variance = 1e-9 print(f"\n Variance Analysis:") for i in range(min(3, len(eigenvalues))): variance_pct = float(np.real(eigenvalues[i])) / float(np.real(total_variance)) * 100 print(f" Component {i+1}: {variance_pct:.1f}% variance") analysis['variance_explained'][f'PC{i+1}'] = variance_pct # Discover new dimensions from components with high variance # that are NOT explained by existing dimensions for i, (eigenvalue, eigenvector) in enumerate(zip(eigenvalues, eigenvectors.T)): variance_pct = float(np.real(eigenvalue)) / float(np.real(total_variance)) if variance_pct > self.discovery_threshold and i >= 6: # This component explains significant variance beyond core dimensions print(f"\n 🔍 High-variance component found: PC{i+1} ({variance_pct:.1%})") # Interpret the eigenvector to name the dimension dim_name, dim_desc = self._interpret_eigenvector(eigenvector) # Create new dimension dim_id = f"D{7 + self.discovered_count}" dim_weight = variance_pct * 0.5 # Initialize with fraction of variance new_dimension = QualityDimension( id=dim_id, name=dim_name, description=dim_desc, weight=dim_weight, discovered_by="singularity", discovery_time=time.time(), evaluation_function=eigenvector ) # Compute correlation with overall Q-score component_scores = feature_centered @ eigenvector new_dimension.correlation_with_q = np.corrcoef(component_scores, q_scores)[0, 1] analysis['new_dimensions'].append(new_dimension) self.discovered_count += 1 print(f" 🧠 DISCOVERED: {new_dimension}") print(f" Description: {dim_desc}") print(f" Correlation with Q: {new_dimension.correlation_with_q:.3f}") # Compute improvement opportunity # How much variance is still unexplained? explained_variance = float(np.real(sum(eigenvalues[:6]))) / float(np.real(total_variance)) analysis['improvement_opportunity'] = 1.0 - explained_variance print(f"\n 📊 Total variance explained by core dimensions: {explained_variance:.1%}") print(f" 📈 Improvement opportunity: {analysis['improvement_opportunity']:.1%}") # Record performance for future weight updates for r, actual_q in zip(realizations, q_scores): baseline_q, _ = self.base_engine.calculate_q_score(r.features) self.performance_history.append({ "id": r.id, "q_score": actual_q, "baseline_q": baseline_q, "features": self.extract_features_from_realization(r) }) return analysis def _interpret_eigenvector(self, eigenvector: np.ndarray) -> Tuple[str, str]: """ Interpret eigenvector to assign semantic name to discovered dimension. Maps eigenvector components to interpretable quality factors. """ # Eigenvector components correspond to: # [0-5]: G, C, S, A, H, V # [6]: child_count (بنات افكار density) # [7]: parent_count (convergence) # [8]: content_length (complexity) # [9]: layer (stability) component_names = [ "Grounding", "Certainty", "Structure", "Applicability", "Coherence", "Generativity", "Idea Reproduction", "Knowledge Convergence", "Conceptual Complexity", "Temporal Stability" ] # Find strongest components abs_components = np.abs(eigenvector) top_3_idx = abs_components.argsort()[-3:][::-1] # Generate name based on top components if 6 in top_3_idx: name = "بنات افكار Density" desc = "Rate at which realization spawns daughter ideas (children count)" elif 7 in top_3_idx: name = "Convergence Synthesis" desc = "Degree to which realization integrates multiple parents" elif 8 in top_3_idx: name = "Conceptual Depth" desc = "Complexity and richness of the insight" elif 9 in top_3_idx: name = "Temporal Resilience" desc = "Stability of realization over time (layer-based)" else: # Mixed factors primary = component_names[top_3_idx[0]] secondary = component_names[top_3_idx[1]] name = f"{primary}-{secondary} Interaction" desc = f"Combined effect of {primary.lower()} and {secondary.lower()}" return name, desc def evolve( self, realizations: List[Any], q_scores: List[float] ): """ Main evolution loop: analyze performance and update framework. Similar to OMEGA's evolve() but for realization quality. """ print(f"\n{'='*70}") print(f"🌌 SINGULARITY REALIZATION ENGINE - EVOLUTION CYCLE") print(f"{'='*70}") # Analyze performance analysis = self.analyze_performance(realizations, q_scores) # Integrate discovered dimensions for new_dim in analysis['new_dimensions']: self.dimensions[new_dim.id] = new_dim print(f"\n✅ Integrated: {new_dim}") # Update weights if we have performance history if len(self.performance_history) > 20: print(f"\n🔄 Adapting dimension weights...") weight_updates = self._compute_weight_updates() for dim_id, new_weight in weight_updates.items(): old_weight = self.dimensions[dim_id].weight self.dimensions[dim_id].weight = new_weight print(f" {dim_id}: {old_weight:.3f} → {new_weight:.3f}") # Store evolution record self.evolution_history.append({ 'timestamp': time.time(), 'dimension_count': len(self.dimensions), 'discovered_dimensions': [d.id for d in analysis['new_dimensions']], 'improvement_opportunity': analysis['improvement_opportunity'], 'avg_q_score': np.mean(q_scores) }) # Check convergence if self._check_convergence(): print(f"\n🎯 CONVERGENCE ACHIEVED") print(f" Final dimension count: {len(self.dimensions)}") print(f" dQ/dt < {self.convergence_threshold}") # Record performance for future weight updates for r, actual_q in zip(realizations, q_scores): baseline_q, _ = self.base_engine.calculate_q_score(r.features) self.performance_history.append({ "id": r.id, "q_score": actual_q, "baseline_q": baseline_q, "features": self.extract_features_from_realization(r) }) return analysis def _compute_weight_updates(self) -> Dict[str, float]: """ Compute weight updates using gradient descent on performance history. Uses REINFORCE-style policy gradient: ∇w_i = (Q_achieved - Q_baseline) × feature_i """ gradients = defaultdict(float) for record in self.performance_history[-50:]: q_achieved = record['q_score'] q_baseline = record['baseline_q'] advantage = q_achieved - q_baseline for dim_id, feature_value in record['features'].items(): if dim_id in self.dimensions: gradients[dim_id] += advantage * feature_value # Normalize for dim_id in gradients: gradients[dim_id] /= len(self.performance_history[-50:]) # Apply updates with bounds weight_updates = {} for dim_id in self.dimensions: if dim_id in gradients: current_weight = self.dimensions[dim_id].weight new_weight = current_weight + self.weight_adaptation_rate * gradients[dim_id] new_weight = np.clip(new_weight, 0.05, 0.30) # Bounds weight_updates[dim_id] = new_weight return weight_updates def _check_convergence(self) -> bool: """Check if framework has converged (dQ/dt < threshold).""" if len(self.evolution_history) < 3: return False recent_q = [h['avg_q_score'] for h in self.evolution_history[-3:]] dq_dt = (recent_q[-1] - recent_q[0]) / 2.0 # Average rate of change return abs(dq_dt) < self.convergence_threshold def predict_next_dimension(self) -> Dict[str, Any]: """ Predict what dimension will be discovered next (D10, D11, D12...). Uses patterns in evolution history to forecast emergent factors. """ if len(self.evolution_history) < 3: return {'prediction': 'Insufficient data', 'confidence': 0.0} # Analyze discovery patterns discovered_names = [] for evolution in self.evolution_history: for dim_id in evolution['discovered_dimensions']: if dim_id in self.dimensions: discovered_names.append(self.dimensions[dim_id].name) # Predict based on gaps predictions = [] # Check if we have بنات افكار density if not any('بنات افكار' in name for name in discovered_names): predictions.append({ 'name': 'بنات افكار Density', 'description': 'Rate of idea reproduction', 'confidence': 0.85, 'rationale': 'High child count variance observed' }) # Check if we have convergence synthesis if not any('Convergence' in name for name in discovered_names): predictions.append({ 'name': 'Convergence Synthesis', 'description': 'Multi-parent integration quality', 'confidence': 0.80, 'rationale': 'Synthesis realizations show unique patterns' }) # Check if we have cross-domain transfer if not any('Transfer' in name or 'Domain' in name for name in discovered_names): predictions.append({ 'name': 'Cross-Domain Transferability', 'description': 'Applicability across fields', 'confidence': 0.75, 'rationale': 'Cross-domain synthesis achieved Layer 0' }) # Return top prediction if predictions: predictions.sort(key=lambda x: x['confidence'], reverse=True) return predictions[0] else: return { 'prediction': 'Framework approaching completeness', 'confidence': 0.90 } def export_evolved_framework(self, filepath: str): """Export the evolved framework to JSON.""" framework_data = { 'version': 'SRE-1.0', 'timestamp': time.time(), 'dimensions': {}, 'evolution_history': self.evolution_history, 'total_dimensions': len(self.dimensions), 'discovered_count': self.discovered_count } for dim_id, dim in self.dimensions.items(): framework_data['dimensions'][dim_id] = { 'name': dim.name, 'description': dim.description, 'weight': dim.weight, 'discovered_by': dim.discovered_by, 'correlation_with_q': dim.correlation_with_q } with open(filepath, 'w') as f: json.dump(framework_data, f, indent=2) print(f"\n✅ Evolved framework exported to {filepath}") def print_framework_status(self): """Print current framework status.""" print(f"\n{'='*70}") print(f"🌌 SINGULARITY REALIZATION FRAMEWORK STATUS") print(f"{'='*70}") print(f"\nDimension Count: {len(self.dimensions)}") print(f" 👤 Human-designed: {sum(1 for d in self.dimensions.values() if d.discovered_by == 'human')}") print(f" 🧠 AI-discovered: {sum(1 for d in self.dimensions.values() if d.discovered_by == 'singularity')}") print(f"\nCurrent Dimensions:") for dim in sorted(self.dimensions.values(), key=lambda x: x.weight, reverse=True): print(f" {dim}") if self.evolution_history: print(f"\nEvolution History: {len(self.evolution_history)} cycles") print(f" Latest Q-score: {self.evolution_history[-1]['avg_q_score']:.4f}") # Predict next next_dim = self.predict_next_dimension() print(f"\n🔮 Next Dimension Prediction:") print(f" {next_dim.get('name', next_dim.get('prediction', 'Unknown'))}") print(f" Confidence: {next_dim.get('confidence', 0):.1%}") # ============================================================================ # DEMONSTRATION # ============================================================================ def demonstrate_singularity_engine(): """ Demonstrate the Singularity Realization Engine on existing data. Uses realizations from: - AI safety conversation (8 realizations) - Hard test cases (16 realizations) """ print("="*80) print("SINGULARITY REALIZATION ENGINE - DEMONSTRATION") print("="*80) # Initialize engines base_engine = RealizationEngine() singularity_engine = SingularityRealizationEngine(base_engine) # Load existing realizations (from previous work) # For demonstration, we'll create synthetic data # In practice, load from realizations.json print("\n📦 Loading realizations from prior work...") # Simulate 24 realizations with varying quality realizations = [] q_scores = [] # High-quality examples (Layer 0-1) for i in range(5): features = RealizationFeatures( grounding=0.90 + np.random.random() * 0.08, certainty=0.90 + np.random.random() * 0.08, structure=0.90 + np.random.random() * 0.08, applicability=0.88 + np.random.random() * 0.07, coherence=0.92 + np.random.random() * 0.06, generativity=0.85 + np.random.random() * 0.10 ) r = base_engine.add_realization( content=f"High-quality realization #{i+1}", features=features, turn_number=i+1 ) realizations.append(r) q_scores.append(r.q_score) # Medium-quality examples (Layer 2-3) for i in range(15): features = RealizationFeatures( grounding=0.70 + np.random.random() * 0.15, certainty=0.75 + np.random.random() * 0.15, structure=0.80 + np.random.random() * 0.12, applicability=0.75 + np.random.random() * 0.15, coherence=0.80 + np.random.random() * 0.12, generativity=0.70 + np.random.random() * 0.15 ) r = base_engine.add_realization( content=f"Medium-quality realization #{i+1}", features=features, turn_number=i+6 ) realizations.append(r) q_scores.append(r.q_score) # Low-quality examples (Layer N) for i in range(4): features = RealizationFeatures( grounding=0.40 + np.random.random() * 0.20, certainty=0.50 + np.random.random() * 0.20, structure=0.50 + np.random.random() * 0.20, applicability=0.45 + np.random.random() * 0.20, coherence=0.55 + np.random.random() * 0.15, generativity=0.40 + np.random.random() * 0.20 ) r = base_engine.add_realization( content=f"Low-quality realization #{i+1}", features=features, turn_number=i+21 ) realizations.append(r) q_scores.append(r.q_score) print(f" Loaded {len(realizations)} realizations") print(f" Q-score range: {min(q_scores):.3f} - {max(q_scores):.3f}") print(f" Average Q-score: {np.mean(q_scores):.3f}") # Evolve the framework print("\n🌌 Beginning framework evolution...") analysis = singularity_engine.evolve(realizations, q_scores) # Print results singularity_engine.print_framework_status() # Export evolved framework singularity_engine.export_evolved_framework('data/evolved_realization_framework.json') print("\n" + "="*80) print("✅ DEMONSTRATION COMPLETE") print("="*80) return singularity_engine if __name__ == "__main__": demonstrate_singularity_engine()