import sys import os import json from datetime import datetime # Add root to path sys.path.append(os.getcwd()) from core.engine import RealizationEngine, RealizationFeatures, ReasoningChain, ReasoningStep, Relation def generate(): engine = RealizationEngine() print("🚀 Generating Comprehensive Realization Dataset...") # --- DOMAIN: PHYSICS --- # R1: Second Law of Thermodynamics (Universal Rule) f1 = RealizationFeatures.from_uqs(0.98, 0.99, 0.96, 0.90, 1.0, 0.92, 0.95, 0.98) r1 = engine.add_realization( "The total entropy of an isolated system can never decrease over time.", f1, turn_number=1, context="Physics foundations", reasoning_chain=ReasoningChain(steps=[ ReasoningStep(1, "Observe thermodynamic processes in isolated systems."), ReasoningStep(2, "Mathematical formalization of entropy as S = k ln W."), ReasoningStep(3, "Statistical mechanics proof of increasing disorder probability.") ]) ) # R2: Mass-Energy Equivalence f2 = RealizationFeatures.from_uqs(0.99, 0.98, 0.97, 0.95, 0.98, 0.95, 0.92, 0.99) r2 = engine.add_realization( "Energy and mass are equivalent and related by E = mc².", f2, turn_number=2, context="Relativity", reasoning_chain=ReasoningChain(steps=[ ReasoningStep(1, "Analyze Lorentz transformations."), ReasoningStep(2, "Derive momentum-energy relation in special relativity.") ]) ) # --- DOMAIN: AI SAFETY --- # R3: Instrumental Convergence f3 = RealizationFeatures.from_uqs(0.85, 0.88, 0.82, 0.90, 0.85, 0.92, 0.80, 0.85) r3 = engine.add_realization( "Intelligent agents will converge on instrumental goals like self-preservation and resource acquisition.", f3, turn_number=3, context="AI Safety", reasoning_chain=ReasoningChain(steps=[ ReasoningStep(1, "Analyze rational agent behavior for arbitrary goals."), ReasoningStep(2, "Identify subgoals that are useful for almost all final goals.") ]) ) # R4: Alignment Problem (Domain Fact) f4 = RealizationFeatures.from_uqs(0.92, 0.95, 0.93, 0.94, 0.95, 0.90, 0.88, 0.92) r4 = engine.add_realization( "AI systems optimize for specified objective functions, which may not match intended human values.", f4, turn_number=4, context="Alignment", parents=[r3.id], reasoning_chain=ReasoningChain(steps=[ ReasoningStep(1, "Observe misspecified rewards leading to unintended behaviors."), ReasoningStep(2, "Crystallize the gap between proxy objectives and terminal values.") ]) ) # --- DOMAIN: BIOLOGY --- # R5: Natural Selection f5 = RealizationFeatures.from_uqs(0.96, 0.94, 0.95, 0.92, 0.95, 0.90, 0.90, 0.95) r5 = engine.add_realization( "Heritable variation combined with differential reproductive success leads to adaptation.", f5, turn_number=5, context="Evolution", reasoning_chain=ReasoningChain(steps=[ ReasoningStep(1, "Observe phenotypic variation in populations."), ReasoningStep(2, "Correlate variation with environmental fitness.") ]) ) # R6: Adaptive Landscapes f6 = RealizationFeatures.from_uqs(0.88, 0.85, 0.90, 0.92, 0.90, 0.75, 0.85, 0.88) r6 = engine.add_realization( "Evolution can be visualized as hill-climbing on a fitness landscape.", f6, turn_number=6, context="Population Genetics", parents=[r5.id] ) # --- DOMAIN: COMPUTER SCIENCE --- # R7: Gradient Descent (Universal Rule) f7 = RealizationFeatures.from_uqs(0.98, 0.98, 0.98, 0.95, 1.0, 0.92, 0.95, 0.98) r7 = engine.add_realization( "Optimization in differentiable spaces is achieved by iteratively moving against the gradient.", f7, turn_number=7, context="Optimization", reasoning_chain=ReasoningChain(steps=[ ReasoningStep(1, "Define loss function L(theta)."), ReasoningStep(2, "Compute partial derivatives w.r.t parameters."), ReasoningStep(3, "Update parameters: theta = theta - lr * grad.") ]) ) # R8: Caching Hierarchy f8 = RealizationFeatures.from_uqs(0.94, 0.92, 0.93, 0.95, 0.95, 0.85, 0.90, 0.92) r8 = engine.add_realization( "System performance is optimized by placing frequently accessed data in faster, smaller storage layers.", f8, turn_number=8, context="Architecture" ) # --- CROSS-DOMAIN SYNTHESIS --- # R9: Pre-computation = Crystallization (Layer 0) f9 = RealizationFeatures.from_uqs(0.98, 0.95, 0.98, 0.98, 0.98, 0.98, 0.95, 0.95) r9 = engine.add_realization( "All intelligent systems—biological, artificial, or organizational—solve resource constraints via isomorphic pre-computation layers.", f9, turn_number=9, context="Unified Theory", parents=[r4.id, r6.id, r7.id, r8.id], topology_relations=[Relation(r1.id, 'refinement')] ) # --- ADDITIONAL REALIZATIONS TO REACH 20 --- # R10: CAP Theorem engine.add_realization("A distributed system can only provide two of Consistency, Availability, and Partition tolerance.", RealizationFeatures.from_uqs(0.95, 0.98, 0.95, 0.90, 0.95, 0.80, 0.85, 0.95), 10) # R11: Neural Plasticity engine.add_realization("The brain reorganizes itself by forming new neural connections throughout life.", RealizationFeatures.from_uqs(0.94, 0.90, 0.92, 0.95, 0.95, 0.88, 0.85, 0.92), 11) # R12: Gödel's Incompleteness engine.add_realization("Any consistent formal system sufficient for arithmetic contains statements that cannot be proven or disproven.", RealizationFeatures.from_uqs(0.99, 1.0, 0.98, 0.80, 0.98, 0.95, 0.90, 0.99), 12) # R13: Game Theory - Nash Equilibrium engine.add_realization("A set of strategies where no player can benefit by changing their strategy while others keep theirs unchanged.", RealizationFeatures.from_uqs(0.97, 0.98, 0.96, 0.92, 0.98, 0.90, 0.90, 0.98), 13) # R14: Context Windows engine.add_realization("LLM attention mechanisms are limited by a fixed-size context window, creating an information bottleneck.", RealizationFeatures.from_uqs(0.96, 0.95, 0.95, 0.92, 0.95, 0.85, 0.85, 0.92), 14) # R15: Backpropagation engine.add_realization("The gradient of the loss function is efficiently computed using the chain rule through computational graphs.", RealizationFeatures.from_uqs(0.98, 0.99, 0.97, 0.95, 0.98, 0.90, 0.92, 0.98), 15) # R16: Ephemeral realization (Layer N) engine.add_realization("Maybe we should use more GPU memory for this task.", RealizationFeatures.from_uqs(0.30, 0.40, 0.50, 0.60, 0.50, 0.20, 0.50, 0.40), 16) # R17: Situational realization (Layer 3) engine.add_realization("Standard BERT embeddings are insufficient for representing complex logical hierarchies in prompts.", RealizationFeatures.from_uqs(0.78, 0.80, 0.75, 0.85, 0.88, 0.70, 0.80, 0.75), 17) # R18: Pattern realization (Layer 2) engine.add_realization("Adding explicit persona markers consistently improves reasoning output in complex prompts.", RealizationFeatures.from_uqs(0.86, 0.88, 0.85, 0.90, 0.92, 0.80, 0.88, 0.85), 18) # R19: Law of Large Numbers engine.add_realization("The average of results from many trials should be close to the expected value.", RealizationFeatures.from_uqs(0.98, 0.99, 0.96, 0.92, 0.98, 0.85, 0.90, 0.98), 19) # R20: Double Helix Structure of DNA engine.add_realization("The DNA molecule consists of two strands that wind around each other like a twisted ladder.", RealizationFeatures.from_uqs(0.99, 1.0, 0.98, 0.95, 0.98, 0.92, 0.95, 0.99), 20) # Save dataset os.makedirs('data', exist_ok=True) engine.export_json('data/comprehensive_realization_dataset.json') print("✅ Comprehensive dataset saved to data/comprehensive_realization_dataset.json") return engine if __name__ == "__main__": engine = generate() engine.print_stats()