# 5_research_frontiers.py
import mlx.core as mx
import mlx.nn as nn

print("🚀 RESEARCH FRONTIERS")
print("=" * 50)

# Set global dtype for consistency
DTYPE = mx.float32

# 1. META-LEARNING: Learning to learn
print("\n--- Meta-Learning Theory ---")

class SimpleMetaLearner(nn.Module):
    """Simplified meta-learning concept"""
    def __init__(self, dim):
        super().__init__()
        self.base_layer = nn.Linear(dim, dim)
        self.meta_layer = nn.Linear(dim, dim)  # Learns how to adapt
    
    def __call__(self, x):
        return self.base_layer(x)
    
    def meta_forward(self, x, adaptation_signal):
        """Forward pass with meta-learning adaptation"""
        # Meta-layer modifies base computation
        adaptation = self.meta_layer(adaptation_signal)
        base_output = self.base_layer(x)
        return base_output + adaptation  # Additive adaptation

# Demonstrate meta-learning concept
meta_model = SimpleMetaLearner(10)
x = mx.random.normal((5, 10), dtype=DTYPE)
adaptation_signal = mx.random.normal((5, 10), dtype=DTYPE)

base_output = meta_model(x)
adapted_output = meta_model.meta_forward(x, adaptation_signal)

print(f"Base output norm: {mx.sqrt(mx.sum(base_output**2)):.4f}")
print(f"Adapted output norm: {mx.sqrt(mx.sum(adapted_output**2)):.4f}")
print(f"Adaptation magnitude: {mx.sqrt(mx.sum((adapted_output - base_output)**2)):.4f}")

# 2. NEURAL SCALING LAWS: How performance scales with size
print("\n--- Neural Scaling Laws ---")

def estimate_scaling_law(model_sizes, data_sizes):
    """
    Scaling laws: Loss ∝ N^(-α) where N is parameters/data
    Power law relationships in neural networks
    """
    results = {}
    
    for model_size in model_sizes:
        for data_size in data_sizes:
            # Simplified scaling relationship
            # Real scaling laws: L(N,D) = A*N^(-α) + B*D^(-β) + E
            alpha, beta = 0.076, 0.095  # Empirical constants from research
            A, B, E = 1.0, 1.0, 1.3     # More empirical constants
            
            loss = A * (model_size ** -alpha) + B * (data_size ** -beta) + E
            results[(model_size, data_size)] = loss
    
    return results

model_sizes = [1e6, 1e7, 1e8, 1e9]  # 1M to 1B parameters
data_sizes = [1e6, 1e7, 1e8, 1e9]   # 1M to 1B tokens

scaling_results = estimate_scaling_law(model_sizes, data_sizes)

print("Scaling law predictions (loss vs model/data size):")
for (model_size, data_size), loss in list(scaling_results.items())[:4]:
    print(f"Model: {model_size:.0e}, Data: {data_size:.0e} → Loss: {loss:.4f}")

# 3. LOTTERY TICKET HYPOTHESIS: Sparse networks within dense ones
print("\n--- Lottery Ticket Hypothesis ---")

def find_lottery_ticket(model, pruning_ratio=0.9):
    """
    Conceptual lottery ticket: find sparse subnetwork
    Real implementation requires iterative magnitude pruning
    """
    # Collect all weights
    all_weights = []
    
    for name, param in model.parameters().items():
        if 'weight' in name and hasattr(param, 'flatten'):
            all_weights.append(param.flatten())
    
    if not all_weights:
        print("No weights found to prune")
        return None
    
    # Concatenate all weights
    weight_vector = mx.concatenate(all_weights)
    
    # Find magnitude threshold for pruning
    sorted_weights = mx.sort(mx.abs(weight_vector))
    threshold_idx = int(len(sorted_weights) * pruning_ratio)
    threshold = sorted_weights[threshold_idx]
    
    # Create binary mask (the "winning ticket")
    mask = mx.abs(weight_vector) > threshold
    sparsity = 1.0 - mx.mean(mask.astype(DTYPE))
    
    print(f"Lottery ticket sparsity: {sparsity:.1%}")
    print(f"Remaining parameters: {mx.sum(mask)} / {len(weight_vector)}")
    
    return mask

# Test lottery ticket on small network
test_model = nn.Linear(100, 50)
lottery_mask = find_lottery_ticket(test_model)

# 4. GROKKING: Sudden generalization after memorization
print("\n--- Grokking Phenomenon ---")

def simulate_grokking_dynamics(steps=1000):
    """
    Grokking: model suddenly generalizes after long period of memorization
    This happens in algorithmic tasks like modular arithmetic
    """
    memorization_phase = 300
    generalization_phase = 700
    
    train_losses = []
    test_losses = []
    
    for step in range(steps):
        if step < memorization_phase:
            # Memorization: train loss decreases, test loss stays high
            train_loss = 2.0 * mx.exp(mx.array(-step / 100, dtype=DTYPE))
            test_loss = mx.array(2.0, dtype=DTYPE) + 0.1 * mx.random.normal((), dtype=DTYPE)
        elif step < generalization_phase:
            # Transition phase: both losses high but unstable  
            prev_train = train_losses[-1] if train_losses else mx.array(0.5, dtype=DTYPE)
            prev_test = test_losses[-1] if test_losses else mx.array(2.0, dtype=DTYPE)
            train_loss = prev_train + 0.1 * mx.random.normal((), dtype=DTYPE)
            test_loss = prev_test + 0.2 * mx.random.normal((), dtype=DTYPE)
        else:
            # Grokking: sudden generalization
            progress = (step - generalization_phase) / (steps - generalization_phase)
            train_loss = mx.array(0.1, dtype=DTYPE) + 0.05 * mx.exp(mx.array(-progress * 5, dtype=DTYPE))
            test_loss = mx.array(0.1, dtype=DTYPE) + 0.05 * mx.exp(mx.array(-progress * 5, dtype=DTYPE))
        
        train_losses.append(float(train_loss))
        test_losses.append(float(test_loss))
    
    return train_losses, test_losses

train_losses, test_losses = simulate_grokking_dynamics()
print(f"Grokking simulation:")
print(f"Pre-grokking test loss: {test_losses[300]:.4f}")
print(f"Post-grokking test loss: {test_losses[-1]:.4f}")
print(f"Generalization improvement: {test_losses[300] / test_losses[-1]:.1f}x")

# 5. EMERGENCE: Phase transitions in neural networks
print("\n--- Emergent Capabilities ---")

def model_capability_curve(model_sizes, task_complexity):
    """
    Model emergent capabilities as phase transition
    Some capabilities only emerge at certain scales
    """
    capabilities = []
    
    for size in model_sizes:
        # Sigmoid emergence: capability suddenly appears at threshold
        threshold = task_complexity * 1e8  # Task-dependent threshold
        steepness = 10  # How sharp the emergence is
        
        # Logistic function for emergence
        exponent = -steepness * (size - threshold) / threshold
        capability = 1 / (1 + mx.exp(mx.array(exponent, dtype=DTYPE)))
        capabilities.append(float(capability))
    
    return capabilities

model_sizes_array = [1e6, 1e7, 1e8, 1e9, 1e10]  # 1M to 10B parameters
tasks = {
    "simple_arithmetic": 0.1,
    "complex_reasoning": 1.0, 
    "few_shot_learning": 2.0,
    "emergent_reasoning": 5.0
}

print("Emergent capabilities by model size:")
for task_name, complexity in tasks.items():
    capabilities = model_capability_curve(model_sizes_array, complexity)
    for i, (size, capability) in enumerate(zip(model_sizes_array, capabilities)):
        if i % 2 == 0:  # Print every other size
            print(f"{task_name} @ {size:.0e} params: {capability:.3f}")
