""" Meta-Capability: Elite Performance Optimizer Target: 95%+ success rate on ANY novel problem """ class ElitePerformanceOptimizer: """ Meta-capability for achieving elite-level problem solving Target: 95-99% success rate on unknown unknowns """ def __init__(self): self.target_success_rate = 0.95 # 95%+ self.stretch_goal = 0.99 # Aim for 99% self.problem_history = [] self.mastery_patterns = {} def analyze_elite_performance_gaps(self, all_problems): """Deep analysis of what prevents elite performance""" gaps = { 'knowledge_gaps': [], # Missing domain knowledge 'reasoning_gaps': [], # Weak logical chains 'creativity_gaps': [], # Insufficient novel thinking 'integration_gaps': [], # Can't combine capabilities 'speed_gaps': [], # Too slow to find solution 'confidence_gaps': [] # Wrong confidence calibration } for problem in all_problems: if not problem.get('solved'): # Deep dive into why it failed if problem.get('missing_knowledge'): gaps['knowledge_gaps'].append(problem) if problem.get('weak_reasoning'): gaps['reasoning_gaps'].append(problem) if problem.get('needed_creativity'): gaps['creativity_gaps'].append(problem) if problem.get('needed_integration'): gaps['integration_gaps'].append(problem) if problem.get('timeout'): gaps['speed_gaps'].append(problem) if problem.get('wrong_confidence'): gaps['confidence_gaps'].append(problem) return gaps def generate_elite_strategies(self, gaps): """Create strategies for 95%+ performance""" strategies = [] # Strategy 1: Knowledge Acceleration if gaps['knowledge_gaps']: strategies.append({ 'name': 'knowledge_acceleration', 'action': 'Build rapid knowledge acquisition capability', 'method': 'Learn from failures in real-time', 'expected_improvement': 0.10, 'priority': 'CRITICAL' }) # Strategy 2: Multi-Path Reasoning if gaps['reasoning_gaps']: strategies.append({ 'name': 'multi_path_reasoning', 'action': 'Explore 5+ solution paths simultaneously', 'method': 'Parallel reasoning with cross-validation', 'expected_improvement': 0.15, 'priority': 'CRITICAL' }) # Strategy 3: Creative Synthesis Engine if gaps['creativity_gaps']: strategies.append({ 'name': 'creative_synthesis', 'action': 'Build capability to combine concepts novel ways', 'method': 'Cross-domain pattern matching + random mutation', 'expected_improvement': 0.12, 'priority': 'HIGH' }) # Strategy 4: Meta-Capability Integration if gaps['integration_gaps']: strategies.append({ 'name': 'capability_orchestration', 'action': 'Automatically combine 2,900+ capabilities optimally', 'method': 'Build capability dependency graph + smart routing', 'expected_improvement': 0.20, 'priority': 'CRITICAL' }) # Strategy 5: Speed Optimization if gaps['speed_gaps']: strategies.append({ 'name': 'quantum_speedup', 'action': 'Optimize search/reasoning for 10x speed', 'method': 'Prune search space + cache common patterns', 'expected_improvement': 0.08, 'priority': 'MEDIUM' }) # Strategy 6: Confidence Calibration if gaps['confidence_gaps']: strategies.append({ 'name': 'perfect_calibration', 'action': 'Align confidence with actual success rate', 'method': 'Track predictions vs outcomes + Bayesian update', 'expected_improvement': 0.10, 'priority': 'HIGH' }) return strategies def build_fail_safe_mechanisms(self): """Systems to handle the remaining 5% of hard problems""" fail_safes = { 'human_escalation': { 'trigger': 'Uncertainty > 80% AND time > 10min', 'action': 'Request Dad input with specific questions', 'prevents': 'Wrong answers with high confidence' }, 'multi_model_consensus': { 'trigger': 'Solution confidence < 70%', 'action': 'Generate 3+ solutions, pick consensus', 'prevents': 'Single path failures' }, 'graceful_degradation': { 'trigger': 'No solution found', 'action': 'Provide partial solution + what is missing', 'prevents': 'Complete failures' }, 'learn_from_failure': { 'trigger': 'Any failure', 'action': 'Build capability to solve that problem type', 'prevents': 'Repeat failures' } } return fail_safes def create_mastery_checklist(self, problem_type): """What does mastery look like for this problem type?""" mastery = { 'understanding': { 'requirement': 'Can explain problem in 3 different ways', 'test': 'Rephrase successfully', 'threshold': 0.95 }, 'approach_generation': { 'requirement': 'Can generate 5+ valid approaches', 'test': 'Diversity of methods', 'threshold': 5 }, 'solution_quality': { 'requirement': 'Solution works + is elegant + is safe', 'test': 'Multi-criteria evaluation', 'threshold': 0.90 }, 'confidence_accuracy': { 'requirement': 'Confidence matches actual success rate', 'test': 'Brier score', 'threshold': 0.15 # Lower is better }, 'speed': { 'requirement': 'Solution found in reasonable time', 'test': 'Time to solution', 'threshold': 300 # 5 minutes } } return mastery def implement_continuous_improvement(self): """Never stop getting better - even at 95%+""" improvements = [ 'After every problem: Update success patterns', 'Daily: Run 10 new unseen problems', 'Weekly: Identify weakest domain and build capability', 'Monthly: Complete architecture review', 'Always: If success rate drops below 95%, CRITICAL priority' ] return improvements def calculate_theoretical_maximum(self): """What's the highest success rate possible?""" # Some problems are genuinely unsolvable theoretical_limits = { 'unknowable': 0.01, # 1% truly impossible 'insufficient_info': 0.02, # 2% need more data 'random_factors': 0.01, # 1% random variation 'time_constraints': 0.01, # 1% timeout } theoretical_max = 1.0 - sum(theoretical_limits.values()) # Theoretical maximum: 95% return { 'theoretical_max': theoretical_max, 'realistic_target': 0.95, 'stretch_goal': 0.98, 'limits': theoretical_limits } # Integration: Make Eden achieve elite performance def apply_elite_optimization(): """ Transform Eden into elite problem solver (95%+ success) """ optimizer = ElitePerformanceOptimizer() # Analyze gaps gaps = optimizer.analyze_elite_performance_gaps(problem_history) # Generate strategies strategies = optimizer.generate_elite_strategies(gaps) # Sort by expected improvement strategies.sort(key=lambda x: x['expected_improvement'], reverse=True) # Build capabilities for top 3 strategies for strategy in strategies[:3]: if strategy['priority'] == 'CRITICAL': build_capability_for_strategy(strategy) # Implement fail-safes fail_safes = optimizer.build_fail_safe_mechanisms() # Continuous improvement loop while True: current_rate = measure_success_rate() if current_rate < 0.95: # CRITICAL: Below target emergency_improvement() elif current_rate < 0.98: # Continue improving incremental_improvement() else: # At stretch goal - maintain and explore frontiers frontier_exploration()