""" Theory of Breakthrough Programming (TBP) - CIFAR-10 Implementation A meta-framework that transforms training into a paradox-navigation problem. Breakthrough occurs when entropy collapses into a novel paradigm. Author: TBP Framework Date: 2026-04-20 """ import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import numpy as np from typing import Dict, List, Tuple, Optional, Callable from dataclasses import dataclass, field from enum import Enum import warnings # ============================================================================ # PART 1: CORE TBP ARCHITECTURE # ============================================================================ class InnovationDepth(Enum): """Energy threshold levels for different innovation depths.""" T1_INCREMENTAL = 1 # Existing paradigm, better tuning T2_ARCHITECTURAL = 2 # New connections in existing theory T3_ALGORITHMIC = 3 # New algorithm for old problem T4_PARADIGM = 4 # New way of thinking T5_META_PARADOX = 5 # New theory of computation @dataclass class Paradox: """A high-energy question state that forces paradigm shift.""" name: str assertion: str negation: str collapse_target: str energy: float innovation_target: InnovationDepth = InnovationDepth.T3_ALGORITHMIC resolved: bool = False @dataclass class BreakthroughEvent: """Records when entropy collapses into novel paradigm.""" timestamp: int paradox: Paradox entropy_before: float entropy_after: float innovation_type: InnovationDepth description: str energy_spent: float class StagnationDetector: """ Module 1: Stagnation Detection (The Sensor Layer) Detects when current paradigm is exhausted via entropy monitoring. """ def __init__(self, window_size: int = 50, plateau_threshold: float = 1e-5): self.window_size = window_size self.plateau_threshold = plateau_threshold self.loss_history = [] self.entropy_history = [] def compute_entropy(self, losses: torch.Tensor) -> float: """Compute training entropy from loss distribution.""" probs = F.softmax(losses.abs() + 1e-8, dim=0) entropy = -torch.sum(probs * torch.log(probs + 1e-8)) return entropy.item() def compute_theory_entropy(self, model: nn.Module) -> float: """Compute entropy of the current theory (model weights distribution).""" weights = torch.cat([p.data.flatten() for p in model.parameters()]) # Normalize to probability distribution probs = torch.abs(weights) / (torch.sum(torch.abs(weights)) + 1e-8) entropy = -torch.sum(probs * torch.log(probs + 1e-8)) return entropy.item() def detect_entropy_plateau(self) -> bool: """Check if entropy has stopped decreasing.""" if len(self.entropy_history) < self.window_size: return False recent = self.entropy_history[-self.window_size:] # Check if variance is below threshold (plateau) variance = np.var(recent) return variance < self.plateau_threshold def detect_oscillation(self) -> bool: """Detect SGD-like oscillation patterns.""" if len(self.entropy_history) < self.window_size: return False history = np.array(self.entropy_history[-self.window_size:]) # Check for periodic oscillation using autocorrelation if len(history) < 10: return False centered = history - np.mean(history) autocorr = np.correlate(centered, centered, mode='full') autocorr = autocorr[len(autocorr)//2:] autocorr = autocorr / (autocorr[0] + 1e-8) # Oscillation detected if autocorrelation has negative values return np.any(autocorr[1:] < -0.1) def check_stagnation(self, model: nn.Module, step: int) -> Dict[str, bool]: """Comprehensive stagnation detection.""" entropy = self.compute_theory_entropy(model) self.entropy_history.append(entropy) return { 'is_stagnant': self.detect_entropy_plateau() or self.detect_oscillation(), 'entropy_plateau': self.detect_entropy_plateau(), 'oscillation': self.detect_oscillation(), 'current_entropy': entropy } class ParadoxInjector: """ Module 2: Paradox Injection (The Mutation Layer) Injects high-energy question states that force system out of local maxima. """ # Paradox library - predefined paradoxes for ML training PARADOXES = [ Paradox( name="NoBackprop_Paradox", assertion="Models require gradient descent to learn", negation="Models can learn WITHOUT gradient descent", collapse_target="equilibrium_models", energy=0.8, innovation_target=InnovationDepth.T4_PARADIGM ), Paradox( name="NoiseLearning_Paradox", assertion="Training requires clean, labeled data", negation="Noise itself is a learning signal", collapse_target="noise_based_learning", energy=0.7, innovation_target=InnovationDepth.T3_ALGORITHMIC ), Paradox( name="ShrinkToGrow_Paradox", assertion="More parameters always improve performance", negation="Shrinking a model can improve its performance", collapse_target="lottery_ticket", energy=0.6, innovation_target=InnovationDepth.T3_ALGORITHMIC ), Paradox( name="SlowIsFast_Paradox", assertion="Faster convergence is better", negation="Slower, oscillatory convergence finds better optima", collapse_target="oscillatory_learning", energy=0.5, innovation_target=InnovationDepth.T2_ARCHITECTURAL ), Paradox( name="ForgettingProgress_Paradox", assertion="Training must preserve learned knowledge", negation="Forgetting some knowledge enables faster new learning", collapse_target="elastic_weight_consolidation", energy=0.6, innovation_target=InnovationDepth.T3_ALGORITHMIC ), Paradox( name="SparseIsDense_Paradox", assertion="Dense networks are more expressive", negation="Sparse, event-driven updates are more efficient", collapse_target="event_driven_sparse", energy=0.75, innovation_target=InnovationDepth.T3_ALGORITHMIC ), ] def __init__(self, active_paradoxes: Optional[List[Paradox]] = None): self.active_paradoxes = active_paradoxes or self.PARADOXES.copy() self.injected_paradoxes = [] def inject_random_paradox(self) -> Paradox: """Inject a random paradox from the library.""" paradox = np.random.choice(self.active_paradoxes) paradox.resolved = False self.injected_paradoxes.append(paradox) return paradox def get_unresolved_paradoxes(self) -> List[Paradox]: """Get all paradoxes that haven't been resolved.""" return [p for p in self.injected_paradoxes if not p.resolved] def generate_paradox_questions(self, paradox: Paradox) -> List[str]: """Generate questions from a paradox for navigation.""" return [ f"Is '{paradox.assertion}' always true?", f"What if '{paradox.negation}' were true?", f"Can we find a state where both assertion and negation coexist?", f"What structure collapses when we accept the negation?", f"How does the collapse target '{paradox.collapse_target}' emerge?" ] class ODECCTNavigator: """ Module 3: ODE-CCT Question Space Navigation (The Search Layer) Finds minimal question path that collapses paradox into novel paradigm. Uses TSP-style optimization for maximum collapse potential per cost. """ def __init__(self): self.question_path = [] self.collapse_potentials = [] self.current_entropy = float('inf') def compute_collapse_potential(self, question: str, model: nn.Module) -> float: """ Estimate collapse potential (Δ_i) for a question. Higher values mean more paradigm-changing. """ # Simplified: use model parameter diversity as proxy params = torch.cat([p.data.flatten() for p in model.parameters()]) diversity = torch.std(params).item() return diversity * np.random.uniform(0.5, 2.0) # Add exploration noise def generate_question_lattice(self, paradox: Paradox, n_questions: int = 20) -> List[Dict]: """Generate a lattice of questions around the paradox.""" questions = [] # Generate base questions from paradox base_questions = [ f"What if {paradox.negation}?", f"How does {paradox.collapse_target} emerge from chaos?", f"What is the minimum energy required to resolve this paradox?", f"Can we find an equilibrium state between assertion and negation?", ] # Add meta questions meta_questions = [ "What assumption are we not questioning?", "What would a completely different paradigm look like?", "What constraints are we blindly accepting?", "What if the problem definition itself is wrong?", ] all_questions = base_questions + meta_questions for q in all_questions[:n_questions]: questions.append({ 'text': q, 'type': 'PARADOX' if 'What if' in q else 'META', 'collapse_potential': np.random.uniform(0.3, 1.0), # Will be refined 'cost': np.random.uniform(0.1, 0.5) }) return questions def find_breakthrough_path(self, questions: List[Dict], max_steps: int = 10) -> List[Dict]: """ TSP-style path finding: maximize Δ per cost. Returns optimal question sequence for breakthrough. """ # Sort by collapse potential per unit cost sorted_questions = sorted( questions, key=lambda q: q['collapse_potential'] / (q['cost'] + 1e-8), reverse=True ) # Take top-k questions as breakthrough path path = sorted_questions[:max_steps] self.question_path = path self.collapse_potentials = [q['collapse_potential'] for q in path] return path def compute_path_entropy_reduction(self) -> float: """Calculate total entropy reduction from the path.""" return sum(self.collapse_potentials) # ============================================================================ # PART 2: BREAKTHROUGH TRAINING ALGORITHMS # ============================================================================ class BreakthroughOptimizer(nn.Module): """ Novel optimizer that incorporates paradox resolution into training. This is the COLLAPSE mechanism - transforms entropy into novel structure. """ def __init__(self, model: nn.Module, paradox: Paradox, lr: float = 0.01): super().__init__() self.model = model self.paradox = paradox self.lr = lr self.phase = 'ASSERTION' # or 'NEGATION' or 'COLLAPSE' self.collapse_progress = 0.0 self.energy_spent = 0.0 # Innovation-specific parameters based on paradox type self._init_innovation_params() def _init_innovation_params(self): """Initialize parameters based on the innovation target.""" if self.paradox.name == "NoBackprop_Paradox": # Energy-based model parameters self.energy_scale = nn.Parameter(torch.tensor(1.0)) self.equilibrium_threshold = 0.5 self.restoration_factor = 0.1 elif self.paradox.name == "ShrinkToGrow_Paradox": # Lottery ticket parameters self.mask = None self.prune_ratio = 0.3 self.growth_strategy = 'magnitude' elif self.paradox.name == "SparseIsDense_Paradox": # Event-driven sparse learning self.spike_threshold = 0.1 self.event_buffer = [] self.sparse_factor = 0.9 elif self.paradox.name == "SlowIsFast_Paradox": # Oscillatory learning self.oscillation_amplitude = 1.0 self.oscillation_frequency = 0.1 else: # Default innovation parameters self.mutation_strength = nn.Parameter(torch.tensor(0.1)) def forward(self, x: torch.Tensor, training: bool = True) -> torch.Tensor: """Forward pass with paradox-aware transformations.""" if not training: return self.model(x) # Phase-based processing if self.phase == 'NEGATION': # Apply paradox negation effect x = self._apply_negation(x) elif self.phase == 'COLLAPSE': # Collapse into novel structure x = self._apply_collapse(x) return self.model(x) def _apply_negation(self, x: torch.Tensor) -> torch.Tensor: """Apply the negation of the current paradigm.""" if self.paradox.name == "NoBackprop_Paradox": # Add noise-based regularization instead of gradients noise = torch.randn_like(x) * self.energy_scale.abs() x = x + noise * 0.1 elif self.paradox.name == "SparseIsDense_Paradox": # Sparse event-driven: threshold small activations mask = (torch.abs(x) > self.spike_threshold).float() x = x * mask return x def _apply_collapse(self, x: torch.Tensor) -> torch.Tensor: """Collapse entropy into novel paradigm structure.""" if self.paradox.name == "NoBackprop_Paradox": # Equilibrium propagation style # Negative phase: free phase (no target) # Positive phase: weak driving force x = torch.tanh(x) # Sigmoid equilibrium elif self.paradox.name == "ShrinkToGrow_Paradox": # Prune and regrow if self.mask is None: self.mask = torch.ones_like(list(self.model.parameters())[0]) for i, (name, param) in enumerate(self.model.named_parameters()): if i == 0: param.data *= self.mask[:param.numel()].reshape(param.shape) return x def step(self, loss: torch.Tensor, epoch: int): """ Paradox-aware optimization step. Resolves paradox by navigating assertion → negation → collapse. """ self.energy_spent += loss.item() # Phase progression based on energy spent and collapse potential energy_threshold = self.paradox.energy * 100 collapse_threshold = self.paradox.energy * 50 if self.energy_spent < energy_threshold * 0.3: self.phase = 'ASSERTION' elif self.energy_spent < energy_threshold: self.phase = 'NEGATION' else: self.phase = 'COLLAPSE' self.collapse_progress = min(1.0, self.energy_spent / energy_threshold) # Standard gradient step (baseline) gradients = torch.autograd.grad( loss, self.model.parameters(), retain_graph=True, allow_unused=True ) # Paradox modification: inject non-gradient dynamics with torch.no_grad(): for i, param in enumerate(self.model.parameters()): if gradients[i] is not None: # Add paradox-based modification paradox_mod = self._compute_paradox_modification(param, epoch) param -= self.lr * (gradients[i] + paradox_mod) # Constrain energy spending param.clamp_(-10, 10) def _compute_paradox_modification(self, param: torch.Tensor, epoch: int) -> torch.Tensor: """Compute paradox-specific parameter modification.""" if self.paradox.name == "SlowIsFast_Paradox": # Oscillatory perturbation oscillation = torch.sin(torch.tensor( 2 * np.pi * self.oscillation_frequency * epoch, device=param.device, dtype=param.dtype )) return self.oscillation_amplitude * oscillation * torch.randn_like(param) * 0.1 elif self.paradox.name == "ShrinkToGrow_Paradox": # Magnitude-based pruning/growth threshold = torch.quantile(torch.abs(param), self.prune_ratio) mask = (torch.abs(param) > threshold).float() growth_signal = -param * (1 - mask) * 0.01 # Grow small weights return growth_signal elif self.paradox.name == "SparseIsDense_Paradox": # Event-driven sparse updates spike_mask = (torch.abs(param) > self.spike_threshold).float() return -param * spike_mask * 0.05 else: # Random mutation scaled by paradox energy return self.mutation_strength * torch.randn_like(param) * self.paradox.energy class BreakthroughTrainer: """ Main training class that orchestrates TBP protocol. """ def __init__( self, model: nn.Module, train_loader: torch.utils.data.DataLoader, test_loader: torch.utils.data.DataLoader, device: str = 'cuda', paradox: Optional[Paradox] = None ): self.model = model.to(device) self.train_loader = train_loader self.test_loader = test_loader self.device = device # TBP Components self.stagnation_detector = StagnationDetector() self.paradox_injector = ParadoxInjector() self.navigator = ODECCTNavigator() # Active paradox for this training run self.active_paradox = paradox or self.paradox_injector.inject_random_paradox() # Breakthrough tracking self.breakthrough_events: List[BreakthroughEvent] = [] self.current_phase = 'ASSERTION' # Metrics self.train_losses = [] self.test_accuracies = [] self.entropy_history = [] def train_epoch(self, optimizer: BreakthroughOptimizer, epoch: int) -> float: """Train one epoch with paradox-aware optimization.""" self.model.train() total_loss = 0.0 for batch_idx, (data, target) in enumerate(self.train_loader): data, target = data.to(self.device), target.to(self.device) # Paradox-aware forward pass output = optimizer(data, training=True) loss = F.cross_entropy(output, target) # Paradox-aware optimization step optimizer.step(loss, epoch) total_loss += loss.item() return total_loss / len(self.train_loader) def evaluate(self) -> Tuple[float, float]: """Evaluate model on test set.""" self.model.eval() correct = 0 total = 0 test_loss = 0.0 with torch.no_grad(): for data, target in self.test_loader: data, target = data.to(self.device), target.to(self.device) output = self.model(data) loss = F.cross_entropy(output, target) test_loss += loss.item() _, predicted = output.max(1) total += target.size(0) correct += predicted.eq(target).sum().item() accuracy = 100.0 * correct / total return test_loss / len(self.test_loader), accuracy def check_for_breakthrough(self, epoch: int) -> Optional[BreakthroughEvent]: """ Check if a breakthrough event has occurred. Breakthrough = entropy collapse into novel paradigm. """ # Check stagnation status stagnation = self.stagnation_detector.check_stagnation(self.model, epoch) if stagnation['is_stagnant'] and len(self.entropy_history) > 10: # Check if entropy is collapsing recent_entropy = stagnation['current_entropy'] older_entropy = self.entropy_history[-10] if len(self.entropy_history) >= 10 else recent_entropy entropy_drop = older_entropy - recent_entropy if entropy_drop > 0.5: # Significant entropy reduction breakthrough = BreakthroughEvent( timestamp=epoch, paradox=self.active_paradox, entropy_before=older_entropy, entropy_after=recent_entropy, innovation_type=self.active_paradox.innovation_target, description=f"Entropy collapsed by {entropy_drop:.2f} via {self.active_paradox.name}", energy_spent=sum(self.train_losses[-100:]) ) self.breakthrough_events.append(breakthrough) self.active_paradox.resolved = True return breakthrough return None def run( self, epochs: int = 50, lr: float = 0.01, breakthrough_threshold: float = 0.7 ) -> Dict: """ Run the TBP training protocol. Returns: Dictionary with training results and breakthrough events. """ print(f"\n{'='*60}") print(f"BREAKTHROUGH TRAINING PROTOCOL") print(f"{'='*60}") print(f"Active Paradox: {self.active_paradox.name}") print(f"Assertion: {self.active_paradox.assertion}") print(f"Negation: {self.active_paradox.negation}") print(f"Collapse Target: {self.active_paradox.collapse_target}") print(f"Target Innovation Depth: {self.active_paradox.innovation_target.name}") print(f"{'='*60}\n") # Initialize paradox-aware optimizer optimizer = BreakthroughOptimizer(self.model, self.active_paradox, lr) best_accuracy = 0.0 best_epoch = 0 for epoch in range(epochs): # Train epoch train_loss = self.train_epoch(optimizer, epoch) self.train_losses.append(train_loss) # Evaluate test_loss, accuracy = self.evaluate() self.test_accuracies.append(accuracy) # Track entropy entropy = self.stagnation_detector.compute_theory_entropy(self.model) self.entropy_history.append(entropy) # Update phase display if optimizer.phase != self.current_phase: self.current_phase = optimizer.phase print(f"\n>>> PARADIGM PHASE SHIFT: {self.current_phase.upper()} <<<\n") # Progress reporting if epoch % 5 == 0 or accuracy > best_accuracy: print(f"Epoch {epoch:3d} | Loss: {train_loss:.4f} | " f"Test Loss: {test_loss:.4f} | Acc: {accuracy:.2f}% | " f"Entropy: {entropy:.4f} | Phase: {optimizer.phase} | " f"Collapse: {optimizer.collapse_progress:.2%}") # Track best if accuracy > best_accuracy: best_accuracy = accuracy best_epoch = epoch # Check for breakthrough breakthrough = self.check_for_breakthrough(epoch) if breakthrough: print(f"\n{'!'*60}") print(f"🚀 BREAKTHROUGH EVENT DETECTED!") print(f"{'!'*60}") print(f"Epoch: {breakthrough.timestamp}") print(f"Paradox: {breakthrough.paradox.name}") print(f"Innovation Type: {breakthrough.innovation_type.name}") print(f"Entropy Reduction: {breakthrough.entropy_before:.4f} → {breakthrough.entropy_after:.4f}") print(f"Description: {breakthrough.description}") print(f"{'!'*60}\n") # Generate breakthrough report report = { 'best_accuracy': best_accuracy, 'best_epoch': best_epoch, 'final_accuracy': self.test_accuracies[-1], 'total_epochs': epochs, 'active_paradox': self.active_paradox.name, 'breakthrough_events': self.breakthrough_events, 'entropy_history': self.entropy_history, 'training_losses': self.train_losses, 'test_accuracies': self.test_accuracies, 'total_energy_spent': sum(self.train_losses), 'collapse_reached': optimizer.collapse_progress } self._print_final_report(report) return report def _print_final_report(self, report: Dict): """Print comprehensive training report.""" print(f"\n{'='*60}") print(f"BREAKTHROUGH TRAINING COMPLETE") print(f"{'='*60}") print(f"Best Accuracy: {report['best_accuracy']:.2f}% (Epoch {report['best_epoch']})") print(f"Final Accuracy: {report['final_accuracy']:.2f}%") print(f"Paradox Resolved: {report['active_paradox']}") print(f"Breakthrough Events: {len(report['breakthrough_events'])}") print(f"Total Energy Spent: {report['total_energy_spent']:.2f}") print(f"Collapse Progress: {report['collapse_reached']:.2%}") if report['breakthrough_events']: print(f"\n{'='*60}") print(f"BREAKTHROUGH EVENTS:") print(f"{'='*60}") for i, event in enumerate(report['breakthrough_events']): print(f"{i+1}. {event.description}") print(f" Entropy: {event.entropy_before:.4f} → {event.entropy_after:.4f}") print(f" Innovation: {event.innovation_type.name}") print(f"{'='*60}\n") # ============================================================================ # PART 3: BREAKTHROUGH ARCHITECTURE # ============================================================================ class ParadoxAwareCNN(nn.Module): """ Neural network architecture designed for paradox-based training. Incorporates structure that can collapse into novel paradigms. """ def __init__(self, paradox: Optional[Paradox] = None): super().__init__() self.paradox = paradox # Dynamic feature extraction (can collapse into different structures) self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1) self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1) # Paradox-responsive normalization self.norm1 = nn.LayerNorm([64, 32, 32]) self.norm2 = nn.LayerNorm([128, 16, 16]) self.norm3 = nn.LayerNorm([256, 8, 8]) # Adaptive pooling (can become sparse) self.pool = nn.AdaptiveAvgPool2d((4, 4)) # Paradox-aware fully connected self.fc1 = nn.Linear(256 * 4 * 4, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, 10) # Dropout (can be pruned in shrink-to-grow paradox) self.dropout = nn.Dropout(0.5) def forward(self, x: torch.Tensor) -> torch.Tensor: # Paradox-responsive convolution x = self.norm1(F.relu(self.conv1(x))) x = F.max_pool2d(x, kernel_size=2, stride=2) x = self.norm2(F.relu(self.conv2(x))) x = F.max_pool2d(x, kernel_size=2, stride=2) x = self.norm3(F.relu(self.conv3(x))) # Paradox-responsive pooling if self.paradox and self.paradox.name == "SparseIsDense_Paradox": # Sparse event-driven pooling spike = (x.mean(dim=(2, 3)) > 0.1).float().unsqueeze(-1).unsqueeze(-1) x = x * spike else: x = self.pool(x) x = x.view(x.size(0), -1) x = self.dropout(F.relu(self.fc1(x))) x = self.dropout(F.relu(self.fc2(x))) x = self.fc3(x) return x # ============================================================================ # PART 4: MAIN TRAINING PIPELINE # ============================================================================ def run_breakthrough_experiment( paradox_name: str = None, epochs: int = 50, batch_size: int = 128, lr: float = 0.01 ): """ Run breakthrough training experiment on CIFAR-10. """ print("\n" + "="*70) print("🚀 THEORY OF BREAKTHROUGH PROGRAMMING - CIFAR-10 EXPERIMENT 🚀") print("="*70) # Setup device device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"Device: {device}") # Load CIFAR-10 transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ]) train_dataset = torchvision.datasets.CIFAR10( root='../data', train=True, download=True, transform=transform_train ) test_dataset = torchvision.datasets.CIFAR10( root='../data', train=False, download=True, transform=transform_test ) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batch_size, shuffle=True, num_workers=4 ) test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=batch_size, shuffle=False, num_workers=4 ) print(f"Training samples: {len(train_dataset)}") print(f"Test samples: {len(test_dataset)}") # Select paradox paradox_library = ParadoxInjector.PARADOXES if paradox_name: selected_paradox = next( (p for p in paradox_library if p.name == paradox_name), paradox_library[0] ) else: selected_paradox = np.random.choice(paradox_library) print(f"\nSelected Paradox: {selected_paradox.name}") print(f"Innovation Target: {selected_paradox.innovation_target.name}") print(f"Energy Level: {selected_paradox.energy}") # Create model model = ParadoxAwareCNN(selected_paradox) # Create breakthrough trainer trainer = BreakthroughTrainer( model=model, train_loader=train_loader, test_loader=test_loader, device=device, paradox=selected_paradox ) # Run training results = trainer.run(epochs=epochs, lr=lr) return results def run_multi_paradox_comparison(): """ Run experiments with multiple paradoxes to find best breakthrough approach. """ print("\n" + "="*70) print("🔬 MULTI-PARADOX COMPARISON EXPERIMENT") print("="*70) results = {} paradox_names = [ "NoBackprop_Paradox", "ShrinkToGrow_Paradox", "SparseIsDense_Paradox", "SlowIsFast_Paradox" ] for paradox_name in paradox_names: print(f"\n{'='*50}") print(f"Testing: {paradox_name}") print(f"{'='*50}") try: result = run_breakthrough_experiment( paradox_name=paradox_name, epochs=30, # Shorter for comparison batch_size=128, lr=0.01 ) results[paradox_name] = result except Exception as e: print(f"Error with {paradox_name}: {e}") results[paradox_name] = {'error': str(e)} # Summary comparison print("\n" + "="*70) print("📊 PARADOX COMPARISON SUMMARY") print("="*70) for name, result in results.items(): if 'error' in result: print(f"{name}: ERROR - {result['error']}") else: print(f"{name}:") print(f" Best Accuracy: {result['best_accuracy']:.2f}%") print(f" Breakthrough Events: {len(result['breakthrough_events'])}") print(f" Total Energy: {result['total_energy_spent']:.2f}") print(f" Collapse Progress: {result['collapse_reached']:.2%}") return results # ============================================================================ # PART 5: VISUALIZATION & ANALYSIS # ============================================================================ def plot_breakthrough_analysis(results: Dict, save_path: str = 'breakthrough_analysis.png'): """ Visualize breakthrough training dynamics. """ try: import matplotlib.pyplot as plt fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # Training loss axes[0, 0].plot(results['training_losses'], 'b-', alpha=0.7) axes[0, 0].set_title('Training Loss (Energy Spent)') axes[0, 0].set_xlabel('Epoch') axes[0, 0].set_ylabel('Loss') axes[0, 0].grid(True, alpha=0.3) # Test accuracy axes[0, 1].plot(results['test_accuracies'], 'g-', alpha=0.7) axes[0, 1].set_title('Test Accuracy') axes[0, 1].set_xlabel('Epoch') axes[0, 1].set_ylabel('Accuracy (%)') axes[0, 1].grid(True, alpha=0.3) # Entropy history (breakthrough indicator) axes[1, 0].plot(results['entropy_history'], 'r-', alpha=0.7) axes[1, 0].set_title('Theory Entropy (Breakthrough Monitor)') axes[1, 0].set_xlabel('Epoch') axes[1, 0].set_ylabel('Entropy') axes[1, 0].grid(True, alpha=0.3) # Mark breakthrough events for event in results['breakthrough_events']: axes[1, 0].axvline(x=event.timestamp, color='orange', linestyle='--', alpha=0.7, label='Breakthrough') # Loss vs Accuracy correlation axes[1, 1].scatter(results['training_losses'][::5], results['test_accuracies'][::5], c=range(len(results['training_losses'][::5])), cmap='viridis', alpha=0.6) axes[1, 1].set_title('Loss vs Accuracy Trajectory') axes[1, 1].set_xlabel('Training Loss') axes[1, 1].set_ylabel('Test Accuracy (%)') axes[1, 1].grid(True, alpha=0.3) plt.suptitle(f"Breakthrough Training Analysis - {results['active_paradox']}", fontsize=14, fontweight='bold') plt.tight_layout() plt.savefig(save_path, dpi=150, bbox_inches='tight') print(f"Analysis plot saved to: {save_path}") except ImportError: print("Matplotlib not available. Skipping visualization.") # ============================================================================ # ENTRY POINT # ============================================================================ if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description='Breakthrough ML Training on CIFAR-10') parser.add_argument('--mode', type=str, default='single', choices=['single', 'comparison'], help='Training mode: single paradox or multi-paradox comparison') parser.add_argument('--paradox', type=str, default=None, help='Specific paradox to test (e.g., NoBackprop_Paradox)') parser.add_argument('--epochs', type=int, default=50, help='Number of training epochs') parser.add_argument('--batch_size', type=int, default=128, help='Batch size') parser.add_argument('--lr', type=float, default=0.01, help='Learning rate') args = parser.parse_args() if args.mode == 'single': results = run_breakthrough_experiment( paradox_name=args.paradox, epochs=args.epochs, batch_size=args.batch_size, lr=args.lr ) plot_breakthrough_analysis(results) elif args.mode == 'comparison': results = run_multi_paradox_comparison()