lmstudio

defmodule LMStudio.NeuralArchitecture do
  @moduledoc """
  Advanced Neural Architecture for Cognitive Agents
  
  Implements self-modifying neural networks, attention mechanisms,
  transformer architectures, and neural evolution strategies.
  """
  
  use GenServer
  require Logger
  
  defmodule Neuron do
    @moduledoc "Individual neuron with adaptive properties"
    
    defstruct [
      :id,
      :weights,
      :bias,
      :activation_function,
      :learning_rate,
      :momentum,
      :dropout_rate,
      :last_activation,
      :gradient_accumulator,
      :adaptation_history,
      :plasticity_factor
    ]
    
    @type t :: %__MODULE__{
      id: String.t(),
      weights: %{String.t() => float()},
      bias: float(),
      activation_function: atom(),
      learning_rate: float(),
      momentum: %{String.t() => float()},
      dropout_rate: float(),
      last_activation: float(),
      gradient_accumulator: %{String.t() => float()},
      adaptation_history: [map()],
      plasticity_factor: float()
    }
    
    def new(id, input_connections \\ [], opts \\ []) do
      activation_fn = Keyword.get(opts, :activation_function, :tanh)
      learning_rate = Keyword.get(opts, :learning_rate, 0.001)
      dropout_rate = Keyword.get(opts, :dropout_rate, 0.1)
      
      weights = Map.new(input_connections, fn connection_id ->
        # Xavier/Glorot initialization
        limit = :math.sqrt(6.0 / (length(input_connections) + 1))
        weight = (:rand.uniform() * 2 - 1) * limit
        {connection_id, weight}
      end)
      
      %__MODULE__{
        id: id,
        weights: weights,
        bias: (:rand.uniform() * 2 - 1) * 0.1,
        activation_function: activation_fn,
        learning_rate: learning_rate,
        momentum: Map.new(input_connections, &{&1, 0.0}),
        dropout_rate: dropout_rate,
        last_activation: 0.0,
        gradient_accumulator: Map.new(input_connections, &{&1, 0.0}),
        adaptation_history: [],
        plasticity_factor: 1.0
      }
    end
    
    def forward(%__MODULE__{} = neuron, inputs, training? \\ false) do
      # Apply dropout during training
      if training? and :rand.uniform() < neuron.dropout_rate do
        {0.0, neuron}
      else
        # Calculate weighted sum
        weighted_sum = Enum.reduce(inputs, neuron.bias, fn {input_id, value}, acc ->
          weight = Map.get(neuron.weights, input_id, 0.0)
          acc + weight * value
        end)
        
        # Apply activation function
        activation = apply_activation(weighted_sum, neuron.activation_function)
        
        # Update neuron state
        updated_neuron = %{neuron | last_activation: activation}
        
        {activation, updated_neuron}
      end
    end
    
    def backward(%__MODULE__{} = neuron, error_gradient, inputs) do
      # Calculate gradients using backpropagation
      activation_derivative = activation_derivative(neuron.last_activation, neuron.activation_function)
      local_gradient = error_gradient * activation_derivative
      
      # Calculate weight gradients
      weight_gradients = Map.new(inputs, fn {input_id, value} ->
        gradient = local_gradient * value
        {input_id, gradient}
      end)
      
      # Calculate bias gradient
      bias_gradient = local_gradient
      
      # Update weights with momentum and learning rate
      updated_weights = Map.new(neuron.weights, fn {input_id, weight} ->
        gradient = Map.get(weight_gradients, input_id, 0.0)
        momentum_term = Map.get(neuron.momentum, input_id, 0.0)
        
        # Momentum update
        new_momentum = 0.9 * momentum_term + neuron.learning_rate * gradient
        new_weight = weight - new_momentum
        
        {input_id, new_weight}
      end)
      
      # Update bias
      updated_bias = neuron.bias - neuron.learning_rate * bias_gradient
      
      # Update momentum
      updated_momentum = Map.new(weight_gradients, fn {input_id, gradient} ->
        momentum_term = Map.get(neuron.momentum, input_id, 0.0)
        new_momentum = 0.9 * momentum_term + neuron.learning_rate * gradient
        {input_id, new_momentum}
      end)
      
      # Accumulate gradients for analysis
      updated_accumulator = Map.merge(neuron.gradient_accumulator, weight_gradients, fn _k, old, new ->
        old + new
      end)
      
      # Record adaptation
      adaptation_record = %{
        timestamp: DateTime.utc_now(),
        weight_updates: map_size(weight_gradients),
        avg_gradient_magnitude: calculate_avg_magnitude(weight_gradients),
        learning_rate: neuron.learning_rate
      }
      
      updated_neuron = %{neuron |
        weights: updated_weights,
        bias: updated_bias,
        momentum: updated_momentum,
        gradient_accumulator: updated_accumulator,
        adaptation_history: [adaptation_record | Enum.take(neuron.adaptation_history, 99)]
      }
      
      # Return input gradients for further backpropagation
      input_gradients = Map.new(inputs, fn {input_id, _value} ->
        weight = Map.get(neuron.weights, input_id, 0.0)
        {input_id, local_gradient * weight}
      end)
      
      {input_gradients, updated_neuron}
    end
    
    def adapt_learning_rate(%__MODULE__{} = neuron) do
      # Adaptive learning rate based on gradient history
      if length(neuron.adaptation_history) >= 2 do
        [current | [previous | _]] = neuron.adaptation_history
        
        # If gradients are consistently large, reduce learning rate
        # If gradients are small, increase learning rate
        gradient_ratio = current.avg_gradient_magnitude / max(previous.avg_gradient_magnitude, 1.0e-8)
        
        new_learning_rate = cond do
          gradient_ratio > 2.0 -> neuron.learning_rate * 0.9  # Reduce if gradients exploding
          gradient_ratio < 0.5 -> neuron.learning_rate * 1.05  # Increase if gradients vanishing
          true -> neuron.learning_rate
        end
        
        # Clamp learning rate
        clamped_learning_rate = max(min(new_learning_rate, 0.1), 1.0e-6)
        
        %{neuron | learning_rate: clamped_learning_rate}
      else
        neuron
      end
    end
    
    defp apply_activation(x, :tanh), do: :math.tanh(x)
    defp apply_activation(x, :relu), do: max(0.0, x)
    defp apply_activation(x, :leaky_relu), do: max(0.01 * x, x)
    defp apply_activation(x, :sigmoid), do: 1.0 / (1.0 + :math.exp(-x))
    defp apply_activation(x, :swish), do: x / (1.0 + :math.exp(-x))
    defp apply_activation(x, :gelu) do
      # Gaussian Error Linear Unit
      0.5 * x * (1.0 + :math.tanh(:math.sqrt(2.0 / :math.pi()) * (x + 0.044715 * x * x * x)))
    end
    defp apply_activation(x, _), do: x  # Linear activation
    
    defp activation_derivative(y, :tanh), do: 1.0 - y * y
    defp activation_derivative(y, :relu), do: if(y > 0, do: 1.0, else: 0.0)
    defp activation_derivative(y, :leaky_relu), do: if(y > 0, do: 1.0, else: 0.01)
    defp activation_derivative(y, :sigmoid), do: y * (1.0 - y)
    defp activation_derivative(y, :swish), do: y + apply_activation(y, :sigmoid) * (1.0 - y)
    defp activation_derivative(y, :gelu) do
      # Approximate GELU derivative
      tanh_term = :math.tanh(:math.sqrt(2.0 / :math.pi()) * (y + 0.044715 * y * y * y))
      0.5 * (1.0 + tanh_term) + 0.5 * y * (1.0 - tanh_term * tanh_term) * 
      :math.sqrt(2.0 / :math.pi()) * (1.0 + 0.134145 * y * y)
    end
    defp activation_derivative(_y, _), do: 1.0  # Linear activation
    
    defp calculate_avg_magnitude(gradients) do
      if map_size(gradients) > 0 do
        total = Enum.reduce(gradients, 0.0, fn {_k, v}, acc -> acc + abs(v) end)
        total / map_size(gradients)
      else
        0.0
      end
    end
  end
  
  defmodule AttentionMechanism do
    @moduledoc "Multi-head attention mechanism"
    
    defstruct [
      :num_heads,
      :head_dim,
      :query_weights,
      :key_weights,
      :value_weights,
      :output_weights,
      :attention_history,
      :temperature,
      :dropout_rate
    ]
    
    def new(model_dim, num_heads \\ 8, opts \\ []) do
      head_dim = div(model_dim, num_heads)
      temperature = Keyword.get(opts, :temperature, 1.0)
      dropout_rate = Keyword.get(opts, :dropout_rate, 0.1)
      
      # Initialize weight matrices
      query_weights = initialize_weight_matrix(model_dim, model_dim)
      key_weights = initialize_weight_matrix(model_dim, model_dim)
      value_weights = initialize_weight_matrix(model_dim, model_dim)
      output_weights = initialize_weight_matrix(model_dim, model_dim)
      
      %__MODULE__{
        num_heads: num_heads,
        head_dim: head_dim,
        query_weights: query_weights,
        key_weights: key_weights,
        value_weights: value_weights,
        output_weights: output_weights,
        attention_history: [],
        temperature: temperature,
        dropout_rate: dropout_rate
      }
    end
    
    def forward(%__MODULE__{} = attention, query, key, value, mask \\ nil) do
      batch_size = length(query)
      _seq_len = if batch_size > 0, do: length(hd(query)), else: 0
      
      # Linear projections
      queries = matrix_multiply(query, attention.query_weights)
      keys = matrix_multiply(key, attention.key_weights)
      values = matrix_multiply(value, attention.value_weights)
      
      # Reshape for multi-head attention
      queries_heads = reshape_for_heads(queries, attention.num_heads, attention.head_dim)
      keys_heads = reshape_for_heads(keys, attention.num_heads, attention.head_dim)
      values_heads = reshape_for_heads(values, attention.num_heads, attention.head_dim)
      
      # Scaled dot-product attention for each head
      attention_outputs = Enum.zip([queries_heads, keys_heads, values_heads])
      |> Enum.map(fn list -> List.to_tuple(list) end)
      |> Enum.map(fn {q, k, v} ->
        scaled_dot_product_attention(q, k, v, attention.temperature, mask)
      end)
      
      # Concatenate heads
      concatenated = concatenate_heads(attention_outputs)
      
      # Final linear projection
      output = matrix_multiply(concatenated, attention.output_weights)
      
      # Record attention patterns
      attention_weights = extract_attention_weights(attention_outputs)
      updated_attention = record_attention_pattern(attention, attention_weights)
      
      {output, updated_attention}
    end
    
    defp initialize_weight_matrix(rows, cols) do
      # Xavier initialization
      limit = :math.sqrt(6.0 / (rows + cols))
      
      for _i <- 1..rows do
        for _j <- 1..cols do
          (:rand.uniform() * 2 - 1) * limit
        end
      end
    end
    
    defp matrix_multiply(a, b) do
      # Simple matrix multiplication
      Enum.map(a, fn row ->
        Enum.zip(b)
        |> Enum.map(fn col_tuple ->
          col = Tuple.to_list(col_tuple)
          Enum.zip(row, col)
          |> Enum.reduce(0.0, fn {x, y}, acc -> acc + x * y end)
        end)
      end)
    end
    
    defp reshape_for_heads(matrix, num_heads, _head_dim) do
      # Reshape matrix for multi-head attention
      # This is a simplified version
      chunk_size = max(div(length(hd(matrix)), num_heads), 1)
      
      Enum.map(1..num_heads, fn head_idx ->
        Enum.map(matrix, fn row ->
          start_idx = (head_idx - 1) * chunk_size
          end_idx = min(head_idx * chunk_size - 1, length(row) - 1)
          
          if start_idx <= end_idx do
            Enum.slice(row, start_idx..end_idx)
          else
            [0.0]  # Fallback
          end
        end)
      end)
    end
    
    defp scaled_dot_product_attention(query, key, value, temperature, mask) do
      # Calculate attention scores
      scores = matrix_multiply(query, transpose(key))
      
      # Scale by sqrt(d_k)
      scale_factor = 1.0 / :math.sqrt(length(hd(key)))
      scaled_scores = Enum.map(scores, fn row ->
        Enum.map(row, &(&1 * scale_factor / temperature))
      end)
      
      # Apply mask if provided
      masked_scores = if mask do
        apply_attention_mask(scaled_scores, mask)
      else
        scaled_scores
      end
      
      # Softmax
      attention_weights = softmax_matrix(masked_scores)
      
      # Apply attention to values
      output = matrix_multiply(attention_weights, value)
      
      {output, attention_weights}
    end
    
    defp transpose(matrix) do
      if length(matrix) > 0 and length(hd(matrix)) > 0 do
        for i <- 0..(length(hd(matrix)) - 1) do
          for row <- matrix do
            Enum.at(row, i)
          end
        end
      else
        []
      end
    end
    
    defp apply_attention_mask(scores, mask) do
      Enum.zip(scores, mask)
      |> Enum.map(fn {score_row, mask_row} ->
        Enum.zip(score_row, mask_row)
        |> Enum.map(fn {score, mask_val} ->
          if mask_val == 0, do: -1.0e9, else: score
        end)
      end)
    end
    
    defp softmax_matrix(matrix) do
      Enum.map(matrix, &softmax_row/1)
    end
    
    defp softmax_row(row) do
      max_val = Enum.max(row)
      exp_vals = Enum.map(row, &:math.exp(&1 - max_val))
      sum_exp = Enum.sum(exp_vals)
      
      if sum_exp > 0 do
        Enum.map(exp_vals, &(&1 / sum_exp))
      else
        # Uniform distribution if all values are the same
        uniform_prob = 1.0 / length(row)
        Enum.map(row, fn _ -> uniform_prob end)
      end
    end
    
    defp concatenate_heads(attention_outputs) do
      # Concatenate outputs from all attention heads
      Enum.zip(attention_outputs)
      |> Enum.map(fn head_outputs ->
        head_outputs
        |> Tuple.to_list()
        |> Enum.flat_map(& &1)
      end)
    end
    
    defp extract_attention_weights(attention_outputs) do
      Enum.map(attention_outputs, fn {_output, weights} -> weights end)
    end
    
    defp record_attention_pattern(%__MODULE__{} = attention, attention_weights) do
      pattern_record = %{
        timestamp: DateTime.utc_now(),
        attention_entropy: calculate_attention_entropy(attention_weights),
        attention_sparsity: calculate_attention_sparsity(attention_weights),
        max_attention: calculate_max_attention(attention_weights)
      }
      
      updated_history = [pattern_record | Enum.take(attention.attention_history, 99)]
      %{attention | attention_history: updated_history}
    end
    
    defp calculate_attention_entropy(attention_weights) do
      # Calculate entropy of attention distribution
      all_weights = List.flatten(attention_weights)
      
      if length(all_weights) > 0 do
        # Normalize weights
        total = Enum.sum(all_weights)
        
        if total > 0 do
          probs = Enum.map(all_weights, &(&1 / total))
          
          -Enum.reduce(probs, 0.0, fn p, acc ->
            if p > 1.0e-10 do
              acc + p * :math.log(p)
            else
              acc
            end
          end)
        else
          0.0
        end
      else
        0.0
      end
    end
    
    defp calculate_attention_sparsity(attention_weights) do
      # Calculate sparsity (how focused the attention is)
      all_weights = List.flatten(attention_weights)
      
      if length(all_weights) > 0 do
        max_weight = Enum.max(all_weights)
        avg_weight = Enum.sum(all_weights) / length(all_weights)
        
        if avg_weight > 0 do
          max_weight / avg_weight
        else
          1.0
        end
      else
        1.0
      end
    end
    
    defp calculate_max_attention(attention_weights) do
      if length(attention_weights) > 0 do
        List.flatten(attention_weights) |> Enum.max()
      else
        0.0
      end
    end
  end
  
  defmodule TransformerLayer do
    @moduledoc "Transformer layer with self-attention and feed-forward network"
    
    defstruct [
      :layer_id,
      :self_attention,
      :feed_forward,
      :layer_norm1,
      :layer_norm2,
      :residual_connections,
      :performance_metrics
    ]
    
    def new(layer_id, model_dim, num_heads \\ 8, ff_dim \\ nil) do
      ff_dim = ff_dim || model_dim * 4
      
      %__MODULE__{
        layer_id: layer_id,
        self_attention: AttentionMechanism.new(model_dim, num_heads),
        feed_forward: create_feed_forward_network(model_dim, ff_dim),
        layer_norm1: create_layer_norm(model_dim),
        layer_norm2: create_layer_norm(model_dim),
        residual_connections: %{enabled: true, scaling: 1.0},
        performance_metrics: %{
          forward_passes: 0,
          attention_entropy: [],
          gradient_norms: []
        }
      }
    end
    
    def forward(%__MODULE__{} = layer, input, mask \\ nil) do
      # Self-attention with residual connection and layer norm
      {attention_output, updated_attention} = AttentionMechanism.forward(
        layer.self_attention, input, input, input, mask
      )
      
      # Add residual connection and layer norm
      norm1_input = add_residual(input, attention_output, layer.residual_connections)
      norm1_output = apply_layer_norm(norm1_input, layer.layer_norm1)
      
      # Feed-forward network with residual connection and layer norm
      ff_output = forward_feed_forward(norm1_output, layer.feed_forward)
      norm2_input = add_residual(norm1_output, ff_output, layer.residual_connections)
      norm2_output = apply_layer_norm(norm2_input, layer.layer_norm2)
      
      # Update performance metrics
      updated_metrics = update_performance_metrics(layer.performance_metrics, updated_attention)
      
      updated_layer = %{layer | 
        self_attention: updated_attention,
        performance_metrics: updated_metrics
      }
      
      {norm2_output, updated_layer}
    end
    
    defp create_feed_forward_network(input_dim, hidden_dim) do
      %{
        linear1: %{
          weights: initialize_weight_matrix(input_dim, hidden_dim),
          bias: Enum.map(1..hidden_dim, fn _ -> (:rand.uniform() * 2 - 1) * 0.1 end)
        },
        linear2: %{
          weights: initialize_weight_matrix(hidden_dim, input_dim),
          bias: Enum.map(1..input_dim, fn _ -> (:rand.uniform() * 2 - 1) * 0.1 end)
        },
        activation: :gelu,
        dropout_rate: 0.1
      }
    end
    
    defp create_layer_norm(dim) do
      %{
        gamma: Enum.map(1..dim, fn _ -> 1.0 end),
        beta: Enum.map(1..dim, fn _ -> 0.0 end),
        epsilon: 1.0e-6
      }
    end
    
    defp initialize_weight_matrix(rows, cols) do
      limit = :math.sqrt(6.0 / (rows + cols))
      
      for _i <- 1..rows do
        for _j <- 1..cols do
          (:rand.uniform() * 2 - 1) * limit
        end
      end
    end
    
    defp add_residual(input, output, residual_config) do
      if residual_config.enabled do
        scaling = residual_config.scaling
        
        Enum.zip(input, output)
        |> Enum.map(fn {input_row, output_row} ->
          Enum.zip(input_row, output_row)
          |> Enum.map(fn {inp, out} -> inp + scaling * out end)
        end)
      else
        output
      end
    end
    
    defp apply_layer_norm(input, layer_norm) do
      Enum.zip(input, [layer_norm.gamma, layer_norm.beta])
      |> Enum.map(fn {row, [gamma_row, beta_row]} ->
        # Calculate mean and variance
        mean = Enum.sum(row) / length(row)
        variance = Enum.reduce(row, 0.0, fn x, acc ->
          diff = x - mean
          acc + diff * diff
        end) / length(row)
        
        std = :math.sqrt(variance + layer_norm.epsilon)
        
        # Normalize and scale
        Enum.zip([row, gamma_row, beta_row])
        |> Enum.map(fn list -> List.to_tuple(list) end)
        |> Enum.map(fn {x, gamma, beta} ->
          normalized = (x - mean) / std
          gamma * normalized + beta
        end)
      end)
    end
    
    defp forward_feed_forward(input, ff_network) do
      # First linear transformation
      hidden = matrix_multiply(input, ff_network.linear1.weights)
      |> add_bias(ff_network.linear1.bias)
      |> apply_activation_to_matrix(ff_network.activation)
      
      # Apply dropout (simplified - just scale)
      dropout_scale = 1.0 - ff_network.dropout_rate
      hidden_dropped = Enum.map(hidden, fn row ->
        Enum.map(row, &(&1 * dropout_scale))
      end)
      
      # Second linear transformation
      matrix_multiply(hidden_dropped, ff_network.linear2.weights)
      |> add_bias(ff_network.linear2.bias)
    end
    
    defp matrix_multiply(a, b) do
      Enum.map(a, fn row ->
        Enum.zip(b)
        |> Enum.map(fn col_tuple ->
          col = Tuple.to_list(col_tuple)
          Enum.zip(row, col)
          |> Enum.reduce(0.0, fn {x, y}, acc -> acc + x * y end)
        end)
      end)
    end
    
    defp add_bias(matrix, bias) do
      Enum.map(matrix, fn row ->
        Enum.zip(row, bias)
        |> Enum.map(fn {x, b} -> x + b end)
      end)
    end
    
    defp apply_activation_to_matrix(matrix, activation_fn) do
      Enum.map(matrix, fn row ->
        Enum.map(row, &apply_activation(&1, activation_fn))
      end)
    end
    
    defp apply_activation(x, :gelu) do
      0.5 * x * (1.0 + :math.tanh(:math.sqrt(2.0 / :math.pi()) * (x + 0.044715 * x * x * x)))
    end
    
    defp apply_activation(x, :relu), do: max(0.0, x)
    defp apply_activation(x, :tanh), do: :math.tanh(x)
    defp apply_activation(x, _), do: x
    
    defp update_performance_metrics(metrics, attention) do
      new_forward_passes = metrics.forward_passes + 1
      
      # Extract latest attention entropy
      latest_entropy = case attention.attention_history do
        [latest | _] -> latest.attention_entropy
        [] -> 0.0
      end
      
      updated_entropy_history = [latest_entropy | Enum.take(metrics.attention_entropy, 99)]
      
      %{metrics |
        forward_passes: new_forward_passes,
        attention_entropy: updated_entropy_history
      }
    end
  end
  
  defmodule CognitiveTransformer do
    @moduledoc "Full transformer model for cognitive reasoning"
    
    defstruct [
      :model_id,
      :layers,
      :embedding_dim,
      :num_heads,
      :num_layers,
      :positional_encoding,
      :output_projection,
      :training_state,
      :evolution_history,
      :attention_patterns,
      :cognitive_specializations
    ]
    
    def new(model_id, embedding_dim \\ 512, num_heads \\ 8, num_layers \\ 6) do
      layers = Enum.map(1..num_layers, fn i ->
        TransformerLayer.new("layer_#{i}", embedding_dim, num_heads)
      end)
      
      %__MODULE__{
        model_id: model_id,
        layers: layers,
        embedding_dim: embedding_dim,
        num_heads: num_heads,
        num_layers: num_layers,
        positional_encoding: create_positional_encoding(1000, embedding_dim),
        output_projection: create_output_projection(embedding_dim),
        training_state: %{
          epoch: 0,
          total_steps: 0,
          loss_history: [],
          learning_rate: 0.0001
        },
        evolution_history: [],
        attention_patterns: %{},
        cognitive_specializations: initialize_cognitive_specializations()
      }
    end
    
    def forward(%__MODULE__{} = model, input_embeddings, mask \\ nil) do
      # Add positional encoding
      pos_encoded = add_positional_encoding(input_embeddings, model.positional_encoding)
      
      # Forward through transformer layers
      {layer_outputs, updated_layers} = Enum.reduce(model.layers, {pos_encoded, []}, 
        fn layer, {current_input, acc_layers} ->
          {layer_output, updated_layer} = TransformerLayer.forward(layer, current_input, mask)
          {layer_output, [updated_layer | acc_layers]}
        end)
      
      # Reverse to maintain order
      updated_layers = Enum.reverse(updated_layers)
      
      # Apply output projection
      final_output = apply_output_projection(layer_outputs, model.output_projection)
      
      # Update attention patterns
      updated_attention_patterns = extract_attention_patterns(updated_layers)
      
      # Update model
      updated_model = %{model | 
        layers: updated_layers,
        attention_patterns: updated_attention_patterns
      }
      
      {final_output, updated_model}
    end
    
    def evolve_architecture(%__MODULE__{} = model, evolution_config \\ %{}) do
      # Evolve the neural architecture based on performance
      mutation_rate = Map.get(evolution_config, :mutation_rate, 0.1)
      _selection_pressure = Map.get(evolution_config, :selection_pressure, 0.8)
      
      # Analyze current performance
      performance_analysis = analyze_model_performance(model)
      
      # Generate mutations
      mutations = generate_architecture_mutations(model, mutation_rate, performance_analysis)
      
      # Apply mutations
      mutated_model = apply_architecture_mutations(model, mutations)
      
      # Record evolution
      evolution_record = %{
        timestamp: DateTime.utc_now(),
        mutations_applied: length(mutations),
        performance_before: performance_analysis,
        mutation_types: Enum.map(mutations, & &1.type)
      }
      
      updated_evolution_history = [evolution_record | Enum.take(model.evolution_history, 99)]
      
      %{mutated_model | evolution_history: updated_evolution_history}
    end
    
    defp create_positional_encoding(max_length, embedding_dim) do
      # Create sinusoidal positional encoding
      Enum.map(0..(max_length - 1), fn pos ->
        Enum.map(0..(embedding_dim - 1), fn i ->
          if rem(i, 2) == 0 do
            :math.sin(pos / :math.pow(10000, i / embedding_dim))
          else
            :math.cos(pos / :math.pow(10000, (i - 1) / embedding_dim))
          end
        end)
      end)
    end
    
    defp create_output_projection(embedding_dim) do
      %{
        weights: initialize_weight_matrix(embedding_dim, embedding_dim),
        bias: Enum.map(1..embedding_dim, fn _ -> 0.0 end),
        activation: :tanh
      }
    end
    
    defp initialize_weight_matrix(rows, cols) do
      limit = :math.sqrt(6.0 / (rows + cols))
      
      for _i <- 1..rows do
        for _j <- 1..cols do
          (:rand.uniform() * 2 - 1) * limit
        end
      end
    end
    
    defp add_positional_encoding(embeddings, pos_encoding) do
      Enum.zip(embeddings, pos_encoding)
      |> Enum.map(fn {emb_row, pos_row} ->
        Enum.zip(emb_row, pos_row)
        |> Enum.map(fn {emb, pos} -> emb + pos end)
      end)
    end
    
    defp apply_output_projection(input, projection) do
      # Apply final projection layer
      result = matrix_multiply(input, projection.weights)
      |> add_bias(projection.bias)
      
      # Apply activation if specified
      case projection.activation do
        nil -> result
        activation_fn -> apply_activation_to_matrix(result, activation_fn)
      end
    end
    
    defp matrix_multiply(a, b) do
      Enum.map(a, fn row ->
        Enum.zip(b)
        |> Enum.map(fn col_tuple ->
          col = Tuple.to_list(col_tuple)
          Enum.zip(row, col)
          |> Enum.reduce(0.0, fn {x, y}, acc -> acc + x * y end)
        end)
      end)
    end
    
    defp add_bias(matrix, bias) do
      Enum.map(matrix, fn row ->
        Enum.zip(row, bias)
        |> Enum.map(fn {x, b} -> x + b end)
      end)
    end
    
    defp apply_activation_to_matrix(matrix, activation_fn) do
      Enum.map(matrix, fn row ->
        Enum.map(row, &apply_activation(&1, activation_fn))
      end)
    end
    
    defp apply_activation(x, :tanh), do: :math.tanh(x)
    defp apply_activation(x, :relu), do: max(0.0, x)
    defp apply_activation(x, _), do: x
    
    defp extract_attention_patterns(layers) do
      # Extract attention patterns from all layers
      attention_data = Enum.map(layers, fn layer ->
        attention_history = layer.self_attention.attention_history
        
        if length(attention_history) > 0 do
          latest = hd(attention_history)
          %{
            layer_id: layer.layer_id,
            entropy: latest.attention_entropy,
            sparsity: latest.attention_sparsity,
            max_attention: latest.max_attention
          }
        else
          %{
            layer_id: layer.layer_id,
            entropy: 0.0,
            sparsity: 1.0,
            max_attention: 0.0
          }
        end
      end)
      
      %{
        timestamp: DateTime.utc_now(),
        layer_patterns: attention_data,
        avg_entropy: calculate_avg_entropy(attention_data),
        attention_flow: analyze_attention_flow(attention_data)
      }
    end
    
    defp calculate_avg_entropy(attention_data) do
      if length(attention_data) > 0 do
        total_entropy = Enum.reduce(attention_data, 0.0, fn data, acc ->
          acc + data.entropy
        end)
        total_entropy / length(attention_data)
      else
        0.0
      end
    end
    
    defp analyze_attention_flow(attention_data) do
      # Analyze how attention flows through layers
      entropies = Enum.map(attention_data, & &1.entropy)
      
      if length(entropies) > 1 do
        # Calculate how attention changes across layers
        entropy_diffs = Enum.zip(entropies, tl(entropies))
        |> Enum.map(fn {curr, next} -> next - curr end)
        
        %{
          entropy_trend: if(Enum.sum(entropy_diffs) > 0, do: :increasing, else: :decreasing),
          entropy_volatility: calculate_variance(entropy_diffs),
          attention_focusing: Enum.any?(entropy_diffs, &(&1 < -0.1))
        }
      else
        %{
          entropy_trend: :stable,
          entropy_volatility: 0.0,
          attention_focusing: false
        }
      end
    end
    
    defp calculate_variance(values) do
      if length(values) > 0 do
        mean = Enum.sum(values) / length(values)
        variance_sum = Enum.reduce(values, 0.0, fn val, acc ->
          diff = val - mean
          acc + diff * diff
        end)
        variance_sum / length(values)
      else
        0.0
      end
    end
    
    defp analyze_model_performance(%__MODULE__{} = model) do
      # Analyze current model performance
      layer_performances = Enum.map(model.layers, fn layer ->
        %{
          layer_id: layer.layer_id,
          forward_passes: layer.performance_metrics.forward_passes,
          avg_attention_entropy: calculate_avg_from_list(layer.performance_metrics.attention_entropy),
          attention_stability: calculate_stability(layer.performance_metrics.attention_entropy)
        }
      end)
      
      %{
        timestamp: DateTime.utc_now(),
        overall_complexity: calculate_model_complexity(model),
        attention_efficiency: calculate_attention_efficiency(model),
        layer_performances: layer_performances,
        evolution_generation: length(model.evolution_history)
      }
    end
    
    defp calculate_avg_from_list(list) do
      if length(list) > 0 do
        Enum.sum(list) / length(list)
      else
        0.0
      end
    end
    
    defp calculate_stability(values) do
      if length(values) > 1 do
        variance = calculate_variance(values)
        mean = calculate_avg_from_list(values)
        
        if mean > 0 do
          1.0 - (variance / (mean * mean))  # Coefficient of variation
        else
          0.0
        end
      else
        1.0
      end
    end
    
    defp calculate_model_complexity(%__MODULE__{} = model) do
      # Calculate model complexity based on parameters and connections
      total_parameters = model.num_layers * model.embedding_dim * model.embedding_dim * 4  # Rough estimate
      attention_complexity = model.num_layers * model.num_heads * model.embedding_dim
      
      %{
        parameter_count: total_parameters,
        attention_complexity: attention_complexity,
        depth: model.num_layers,
        width: model.embedding_dim
      }
    end
    
    defp calculate_attention_efficiency(%__MODULE__{} = model) do
      # Calculate attention efficiency across all layers
      if map_size(model.attention_patterns) > 0 and Map.has_key?(model.attention_patterns, :layer_patterns) do
        layer_patterns = model.attention_patterns.layer_patterns
        
        if length(layer_patterns) > 0 do
          avg_sparsity = Enum.reduce(layer_patterns, 0.0, fn pattern, acc ->
            acc + pattern.sparsity
          end) / length(layer_patterns)
          
          avg_max_attention = Enum.reduce(layer_patterns, 0.0, fn pattern, acc ->
            acc + pattern.max_attention
          end) / length(layer_patterns)
          
          %{
            sparsity: avg_sparsity,
            focus_strength: avg_max_attention,
            efficiency_score: avg_sparsity * avg_max_attention
          }
        else
          %{sparsity: 1.0, focus_strength: 0.0, efficiency_score: 0.0}
        end
      else
        %{sparsity: 1.0, focus_strength: 0.0, efficiency_score: 0.0}
      end
    end
    
    defp generate_architecture_mutations(%__MODULE__{} = model, mutation_rate, performance_analysis) do
      # Generate mutations based on performance analysis
      mutations = []
      
      # Attention head mutations
      mutations = if :rand.uniform() < mutation_rate do
        head_mutation = generate_attention_head_mutation(model, performance_analysis)
        [head_mutation | mutations]
      else
        mutations
      end
      
      # Layer mutations
      mutations = if :rand.uniform() < mutation_rate do
        layer_mutation = generate_layer_mutation(model, performance_analysis)
        [layer_mutation | mutations]
      else
        mutations
      end
      
      # Activation function mutations
      mutations = if :rand.uniform() < mutation_rate do
        activation_mutation = generate_activation_mutation(model, performance_analysis)
        [activation_mutation | mutations]
      else
        mutations
      end
      
      # Residual connection mutations
      mutations = if :rand.uniform() < mutation_rate do
        residual_mutation = generate_residual_mutation(model, performance_analysis)
        [residual_mutation | mutations]
      else
        mutations
      end
      
      mutations
    end
    
    defp generate_attention_head_mutation(%__MODULE__{} = model, performance_analysis) do
      # Generate mutation for attention heads
      efficiency = performance_analysis.attention_efficiency
      
      mutation_type = cond do
        efficiency.efficiency_score < 0.3 -> :increase_heads
        efficiency.efficiency_score > 0.8 -> :decrease_heads
        true -> :modify_head_dim
      end
      
      %{
        type: :attention_heads,
        mutation_type: mutation_type,
        current_heads: model.num_heads,
        target_heads: case mutation_type do
          :increase_heads -> min(model.num_heads + 2, 16)
          :decrease_heads -> max(model.num_heads - 2, 2)
          :modify_head_dim -> model.num_heads
        end
      }
    end
    
    defp generate_layer_mutation(%__MODULE__{} = model, performance_analysis) do
      # Generate mutation for layer structure
      complexity = performance_analysis.overall_complexity
      
      mutation_type = cond do
        complexity.depth < 4 -> :add_layer
        complexity.depth > 12 -> :remove_layer
        true -> :modify_layer_norm
      end
      
      %{
        type: :layer_structure,
        mutation_type: mutation_type,
        current_layers: model.num_layers,
        target_layers: case mutation_type do
          :add_layer -> model.num_layers + 1
          :remove_layer -> max(model.num_layers - 1, 2)
          :modify_layer_norm -> model.num_layers
        end
      }
    end
    
    defp generate_activation_mutation(%__MODULE__{} = _model, _performance_analysis) do
      # Generate mutation for activation functions
      activations = [:gelu, :relu, :swish, :tanh, :leaky_relu]
      new_activation = Enum.random(activations)
      
      %{
        type: :activation_function,
        mutation_type: :change_activation,
        new_activation: new_activation
      }
    end
    
    defp generate_residual_mutation(%__MODULE__{} = _model, performance_analysis) do
      # Generate mutation for residual connections
      efficiency = performance_analysis.attention_efficiency
      
      scaling_factor = if efficiency.efficiency_score > 0.7 do
        1.1  # Increase residual scaling
      else
        0.9  # Decrease residual scaling
      end
      
      %{
        type: :residual_connections,
        mutation_type: :adjust_scaling,
        scaling_factor: scaling_factor
      }
    end
    
    defp apply_architecture_mutations(%__MODULE__{} = model, mutations) do
      # Apply mutations to model architecture
      Enum.reduce(mutations, model, fn mutation, acc_model ->
        apply_single_mutation(acc_model, mutation)
      end)
    end
    
    defp apply_single_mutation(%__MODULE__{} = model, mutation) do
      case mutation.type do
        :attention_heads ->
          apply_attention_head_mutation(model, mutation)
        
        :layer_structure ->
          apply_layer_structure_mutation(model, mutation)
        
        :activation_function ->
          apply_activation_function_mutation(model, mutation)
        
        :residual_connections ->
          apply_residual_connection_mutation(model, mutation)
        
        _ ->
          model
      end
    end
    
    defp apply_attention_head_mutation(%__MODULE__{} = model, mutation) do
      case mutation.mutation_type do
        :increase_heads ->
          new_layers = Enum.map(model.layers, fn layer ->
            new_attention = AttentionMechanism.new(model.embedding_dim, mutation.target_heads)
            %{layer | self_attention: new_attention}
          end)
          
          %{model | 
            layers: new_layers, 
            num_heads: mutation.target_heads
          }
        
        :decrease_heads ->
          new_layers = Enum.map(model.layers, fn layer ->
            new_attention = AttentionMechanism.new(model.embedding_dim, mutation.target_heads)
            %{layer | self_attention: new_attention}
          end)
          
          %{model | 
            layers: new_layers, 
            num_heads: mutation.target_heads
          }
        
        _ ->
          model
      end
    end
    
    defp apply_layer_structure_mutation(%__MODULE__{} = model, mutation) do
      case mutation.mutation_type do
        :add_layer ->
          new_layer = TransformerLayer.new("layer_#{model.num_layers + 1}", 
                                          model.embedding_dim, model.num_heads)
          new_layers = model.layers ++ [new_layer]
          
          %{model | 
            layers: new_layers, 
            num_layers: mutation.target_layers
          }
        
        :remove_layer ->
          # Remove the layer with the worst performance
          worst_layer_idx = find_worst_performing_layer(model)
          new_layers = List.delete_at(model.layers, worst_layer_idx)
          
          %{model | 
            layers: new_layers, 
            num_layers: mutation.target_layers
          }
        
        _ ->
          model
      end
    end
    
    defp apply_activation_function_mutation(%__MODULE__{} = model, mutation) do
      # Apply activation function mutation to feed-forward networks
      new_layers = Enum.map(model.layers, fn layer ->
        updated_ff = %{layer.feed_forward | activation: mutation.new_activation}
        %{layer | feed_forward: updated_ff}
      end)
      
      %{model | layers: new_layers}
    end
    
    defp apply_residual_connection_mutation(%__MODULE__{} = model, mutation) do
      # Apply residual connection mutation
      new_layers = Enum.map(model.layers, fn layer ->
        updated_residual = %{layer.residual_connections | scaling: mutation.scaling_factor}
        %{layer | residual_connections: updated_residual}
      end)
      
      %{model | layers: new_layers}
    end
    
    defp find_worst_performing_layer(%__MODULE__{} = model) do
      # Find the layer with the worst performance (lowest attention entropy)
      layer_scores = Enum.with_index(model.layers)
      |> Enum.map(fn {layer, idx} ->
        avg_entropy = calculate_avg_from_list(layer.performance_metrics.attention_entropy)
        {idx, avg_entropy}
      end)
      
      if length(layer_scores) > 0 do
        {worst_idx, _score} = Enum.min_by(layer_scores, fn {_idx, score} -> score end)
        worst_idx
      else
        0
      end
    end
    
    defp initialize_cognitive_specializations do
      # Initialize cognitive specializations for different types of reasoning
      %{
        logical_reasoning: %{
          preferred_activations: [:relu, :leaky_relu],
          attention_pattern: :focused,
          layer_emphasis: :deep
        },
        creative_thinking: %{
          preferred_activations: [:gelu, :swish],
          attention_pattern: :distributed,
          layer_emphasis: :wide
        },
        analytical_processing: %{
          preferred_activations: [:tanh, :relu],
          attention_pattern: :hierarchical,
          layer_emphasis: :balanced
        },
        empathetic_understanding: %{
          preferred_activations: [:gelu, :swish],
          attention_pattern: :contextual,
          layer_emphasis: :shallow
        }
      }
    end
  end
  
  # Main GenServer implementation
  
  def start_link(opts \\ []) do
    name = Keyword.get(opts, :name, __MODULE__)
    embedding_dim = Keyword.get(opts, :embedding_dim, 512)
    num_heads = Keyword.get(opts, :num_heads, 8)
    num_layers = Keyword.get(opts, :num_layers, 6)
    
    GenServer.start_link(__MODULE__, {embedding_dim, num_heads, num_layers}, name: name)
  end
  
  def create_model(neural_pid, model_id, config \\ %{}) do
    GenServer.call(neural_pid, {:create_model, model_id, config})
  end
  
  def forward_pass(neural_pid, model_id, input_embeddings, mask \\ nil) do
    GenServer.call(neural_pid, {:forward_pass, model_id, input_embeddings, mask})
  end
  
  def evolve_model(neural_pid, model_id, evolution_config \\ %{}) do
    GenServer.call(neural_pid, {:evolve_model, model_id, evolution_config})
  end
  
  def get_model_state(neural_pid, model_id) do
    GenServer.call(neural_pid, {:get_model_state, model_id})
  end
  
  def analyze_attention_patterns(neural_pid, model_id) do
    GenServer.call(neural_pid, {:analyze_attention_patterns, model_id})
  end
  
  def train_step(neural_pid, model_id, training_data) do
    GenServer.call(neural_pid, {:train_step, model_id, training_data})
  end
  
  # Server callbacks
  
  @impl true
  def init({embedding_dim, num_heads, num_layers}) do
    state = %{
      models: %{},
      default_config: %{
        embedding_dim: embedding_dim,
        num_heads: num_heads,
        num_layers: num_layers
      },
      evolution_log: [],
      performance_metrics: %{},
      neural_plasticity: %{
        adaptation_rate: 0.01,
        synaptic_strength: 1.0,
        hebbian_learning: true
      }
    }
    
    Logger.info("Neural Architecture system initialized with #{num_layers} layers, #{num_heads} heads, #{embedding_dim}D embeddings")
    {:ok, state}
  end
  
  @impl true
  def handle_call({:create_model, model_id, config}, _from, state) do
    # Merge config with defaults
    final_config = Map.merge(state.default_config, config)
    
    # Create new transformer model
    transformer = CognitiveTransformer.new(
      model_id,
      final_config.embedding_dim,
      final_config.num_heads,
      final_config.num_layers
    )
    
    # Add to models
    updated_models = Map.put(state.models, model_id, transformer)
    
    # Initialize performance metrics
    updated_metrics = Map.put(state.performance_metrics, model_id, %{
      creation_time: DateTime.utc_now(),
      forward_passes: 0,
      evolution_count: 0,
      performance_scores: []
    })
    
    new_state = %{state | 
      models: updated_models,
      performance_metrics: updated_metrics
    }
    
    Logger.info("Created cognitive transformer model: #{model_id}")
    {:reply, {:ok, model_id}, new_state}
  end
  
  @impl true
  def handle_call({:forward_pass, model_id, input_embeddings, mask}, _from, state) do
    case Map.get(state.models, model_id) do
      nil ->
        {:reply, {:error, :model_not_found}, state}
      
      model ->
        # Perform forward pass
        {output, updated_model} = CognitiveTransformer.forward(model, input_embeddings, mask)
        
        # Update model in state
        updated_models = Map.put(state.models, model_id, updated_model)
        
        # Update performance metrics
        updated_metrics = update_model_metrics(state.performance_metrics, model_id, :forward_pass)
        
        new_state = %{state | 
          models: updated_models,
          performance_metrics: updated_metrics
        }
        
        result = %{
          output: output,
          attention_patterns: updated_model.attention_patterns,
          model_stats: extract_model_stats(updated_model)
        }
        
        {:reply, {:ok, result}, new_state}
    end
  end
  
  @impl true
  def handle_call({:evolve_model, model_id, evolution_config}, _from, state) do
    case Map.get(state.models, model_id) do
      nil ->
        {:reply, {:error, :model_not_found}, state}
      
      model ->
        # Evolve the model architecture
        evolved_model = CognitiveTransformer.evolve_architecture(model, evolution_config)
        
        # Update model in state
        updated_models = Map.put(state.models, model_id, evolved_model)
        
        # Log evolution
        evolution_entry = %{
          timestamp: DateTime.utc_now(),
          model_id: model_id,
          evolution_config: evolution_config,
          mutations_applied: length(hd(evolved_model.evolution_history).mutations_applied)
        }
        
        updated_evolution_log = [evolution_entry | Enum.take(state.evolution_log, 99)]
        
        # Update performance metrics
        updated_metrics = update_model_metrics(state.performance_metrics, model_id, :evolution)
        
        new_state = %{state | 
          models: updated_models,
          evolution_log: updated_evolution_log,
          performance_metrics: updated_metrics
        }
        
        Logger.info("Evolved model #{model_id}: #{evolution_entry.mutations_applied} mutations applied")
        
        result = %{
          evolution_successful: true,
          mutations_applied: evolution_entry.mutations_applied,
          model_generation: length(evolved_model.evolution_history),
          new_architecture_stats: extract_architecture_stats(evolved_model)
        }
        
        {:reply, {:ok, result}, new_state}
    end
  end
  
  @impl true
  def handle_call({:get_model_state, model_id}, _from, state) do
    case Map.get(state.models, model_id) do
      nil ->
        {:reply, {:error, :model_not_found}, state}
      
      model ->
        model_state = %{
          model_id: model.model_id,
          architecture: %{
            embedding_dim: model.embedding_dim,
            num_heads: model.num_heads,
            num_layers: model.num_layers
          },
          training_state: model.training_state,
          evolution_generation: length(model.evolution_history),
          attention_patterns: model.attention_patterns,
          cognitive_specializations: model.cognitive_specializations,
          performance_metrics: Map.get(state.performance_metrics, model_id, %{})
        }
        
        {:reply, model_state, state}
    end
  end
  
  @impl true
  def handle_call({:analyze_attention_patterns, model_id}, _from, state) do
    case Map.get(state.models, model_id) do
      nil ->
        {:reply, {:error, :model_not_found}, state}
      
      model ->
        # Analyze attention patterns across layers
        layer_analyses = Enum.map(model.layers, fn layer ->
          attention_history = layer.self_attention.attention_history
          
          %{
            layer_id: layer.layer_id,
            recent_entropy: if(length(attention_history) > 0, do: hd(attention_history).attention_entropy, else: 0.0),
            entropy_trend: calculate_entropy_trend(attention_history),
            attention_focus: analyze_attention_focus(attention_history),
            performance_score: calculate_layer_performance_score(layer)
          }
        end)
        
        # Global analysis
        global_analysis = %{
          model_id: model_id,
          overall_attention_health: calculate_overall_attention_health(layer_analyses),
          attention_flow_pattern: analyze_cross_layer_attention_flow(layer_analyses),
          cognitive_mode_detection: detect_cognitive_modes(layer_analyses),
          optimization_suggestions: generate_optimization_suggestions(layer_analyses)
        }
        
        result = %{
          layer_analyses: layer_analyses,
          global_analysis: global_analysis,
          attention_visualization_data: prepare_attention_visualization_data(model)
        }
        
        {:reply, {:ok, result}, state}
    end
  end
  
  @impl true
  def handle_call({:train_step, model_id, training_data}, _from, state) do
    case Map.get(state.models, model_id) do
      nil ->
        {:reply, {:error, :model_not_found}, state}
      
      model ->
        # Simplified training step (would normally involve gradient computation)
        {_output, updated_model} = CognitiveTransformer.forward(model, training_data.inputs, training_data.mask)
        
        # Update training state
        updated_training_state = %{
          updated_model.training_state | 
          total_steps: updated_model.training_state.total_steps + 1,
          epoch: training_data.epoch || updated_model.training_state.epoch
        }
        
        final_model = %{updated_model | training_state: updated_training_state}
        
        # Update models
        updated_models = Map.put(state.models, model_id, final_model)
        
        # Update metrics
        updated_metrics = update_model_metrics(state.performance_metrics, model_id, :training_step)
        
        new_state = %{state | 
          models: updated_models,
          performance_metrics: updated_metrics
        }
        
        result = %{
          training_step_completed: true,
          step: final_model.training_state.total_steps,
          epoch: final_model.training_state.epoch
        }
        
        {:reply, {:ok, result}, new_state}
    end
  end
  
  # Helper functions
  
  defp extract_model_stats(%CognitiveTransformer{} = model) do
    %{
      total_parameters: estimate_parameter_count(model),
      attention_efficiency: calculate_current_attention_efficiency(model),
      layer_count: model.num_layers,
      head_count: model.num_heads,
      embedding_dimension: model.embedding_dim,
      evolution_generation: length(model.evolution_history)
    }
  end
  
  defp extract_architecture_stats(%CognitiveTransformer{} = model) do
    %{
      architecture_complexity: calculate_architecture_complexity(model),
      specialization_scores: calculate_specialization_scores(model),
      attention_distribution: analyze_attention_distribution(model),
      layer_efficiency: calculate_layer_efficiency_scores(model)
    }
  end
  
  defp estimate_parameter_count(%CognitiveTransformer{} = model) do
    # Rough parameter count estimation
    embedding_params = model.embedding_dim * model.embedding_dim
    attention_params = model.num_layers * model.num_heads * model.embedding_dim * model.embedding_dim * 4
    ff_params = model.num_layers * model.embedding_dim * model.embedding_dim * 8
    
    embedding_params + attention_params + ff_params
  end
  
  defp calculate_current_attention_efficiency(%CognitiveTransformer{} = model) do
    if map_size(model.attention_patterns) > 0 and Map.has_key?(model.attention_patterns, :layer_patterns) do
      layer_patterns = model.attention_patterns.layer_patterns
      
      if length(layer_patterns) > 0 do
        total_efficiency = Enum.reduce(layer_patterns, 0.0, fn pattern, acc ->
          efficiency = pattern.sparsity * pattern.max_attention
          acc + efficiency
        end)
        
        total_efficiency / length(layer_patterns)
      else
        0.0
      end
    else
      0.0
    end
  end
  
  defp calculate_architecture_complexity(%CognitiveTransformer{} = model) do
    # Calculate architectural complexity score
    depth_complexity = model.num_layers / 12.0  # Normalized to typical max
    width_complexity = model.embedding_dim / 1024.0  # Normalized to typical max
    attention_complexity = model.num_heads / 16.0  # Normalized to typical max
    
    (depth_complexity + width_complexity + attention_complexity) / 3
  end
  
  defp calculate_specialization_scores(%CognitiveTransformer{} = model) do
    # Calculate how specialized the model is for different cognitive tasks
    specializations = model.cognitive_specializations
    
    Map.new(specializations, fn {specialization, config} ->
      # Score based on how well the current architecture matches the specialization
      score = calculate_specialization_match_score(model, config)
      {specialization, score}
    end)
  end
  
  defp calculate_specialization_match_score(%CognitiveTransformer{} = _model, _specialization_config) do
    # Simplified specialization matching
    :rand.uniform() * 0.5 + 0.25  # Random score between 0.25 and 0.75
  end
  
  defp analyze_attention_distribution(%CognitiveTransformer{} = model) do
    if map_size(model.attention_patterns) > 0 and Map.has_key?(model.attention_patterns, :layer_patterns) do
      layer_patterns = model.attention_patterns.layer_patterns
      
      entropies = Enum.map(layer_patterns, & &1.entropy)
      sparsities = Enum.map(layer_patterns, & &1.sparsity)
      
      %{
        entropy_distribution: %{
          mean: calculate_mean(entropies),
          variance: calculate_variance(entropies),
          min: if(length(entropies) > 0, do: Enum.min(entropies), else: 0.0),
          max: if(length(entropies) > 0, do: Enum.max(entropies), else: 0.0)
        },
        sparsity_distribution: %{
          mean: calculate_mean(sparsities),
          variance: calculate_variance(sparsities),
          min: if(length(sparsities) > 0, do: Enum.min(sparsities), else: 0.0),
          max: if(length(sparsities) > 0, do: Enum.max(sparsities), else: 0.0)
        }
      }
    else
      %{
        entropy_distribution: %{mean: 0.0, variance: 0.0, min: 0.0, max: 0.0},
        sparsity_distribution: %{mean: 1.0, variance: 0.0, min: 1.0, max: 1.0}
      }
    end
  end
  
  defp calculate_mean(values) do
    if length(values) > 0 do
      Enum.sum(values) / length(values)
    else
      0.0
    end
  end
  
  defp calculate_variance(values) do
    if length(values) > 1 do
      mean = calculate_mean(values)
      variance_sum = Enum.reduce(values, 0.0, fn val, acc ->
        diff = val - mean
        acc + diff * diff
      end)
      variance_sum / (length(values) - 1)
    else
      0.0
    end
  end
  
  defp calculate_layer_efficiency_scores(%CognitiveTransformer{} = model) do
    Enum.map(model.layers, fn layer ->
      attention_entropy_score = if length(layer.performance_metrics.attention_entropy) > 0 do
        avg_entropy = Enum.sum(layer.performance_metrics.attention_entropy) / length(layer.performance_metrics.attention_entropy)
        1.0 - avg_entropy  # Higher entropy = lower efficiency
      else
        0.5
      end
      
      forward_pass_score = min(layer.performance_metrics.forward_passes / 1000.0, 1.0)
      
      %{
        layer_id: layer.layer_id,
        attention_efficiency: attention_entropy_score,
        usage_efficiency: forward_pass_score,
        overall_efficiency: (attention_entropy_score + forward_pass_score) / 2
      }
    end)
  end
  
  defp update_model_metrics(metrics, model_id, operation) do
    current_metrics = Map.get(metrics, model_id, %{
      creation_time: DateTime.utc_now(),
      forward_passes: 0,
      evolution_count: 0,
      performance_scores: []
    })
    
    updated_metrics = case operation do
      :forward_pass ->
        %{current_metrics | forward_passes: current_metrics.forward_passes + 1}
      
      :evolution ->
        %{current_metrics | evolution_count: current_metrics.evolution_count + 1}
      
      :training_step ->
        %{current_metrics | forward_passes: current_metrics.forward_passes + 1}
      
      _ ->
        current_metrics
    end
    
    Map.put(metrics, model_id, updated_metrics)
  end
  
  defp calculate_entropy_trend(attention_history) do
    if length(attention_history) >= 3 do
      recent_entropies = Enum.take(attention_history, 3)
      |> Enum.map(& &1.attention_entropy)
      |> Enum.reverse()  # Oldest to newest
      
      [oldest, middle, newest] = recent_entropies
      
      trend1 = middle - oldest
      trend2 = newest - middle
      
      avg_trend = (trend1 + trend2) / 2
      
      cond do
        avg_trend > 0.05 -> :increasing
        avg_trend < -0.05 -> :decreasing
        true -> :stable
      end
    else
      :insufficient_data
    end
  end
  
  defp analyze_attention_focus(attention_history) do
    if length(attention_history) > 0 do
      recent_data = hd(attention_history)
      
      %{
        focus_strength: recent_data.attention_sparsity,
        focus_consistency: calculate_focus_consistency(attention_history),
        peak_attention: recent_data.max_attention
      }
    else
      %{
        focus_strength: 1.0,
        focus_consistency: 0.0,
        peak_attention: 0.0
      }
    end
  end
  
  defp calculate_focus_consistency(attention_history) do
    if length(attention_history) >= 2 do
      sparsities = Enum.take(attention_history, 5)
      |> Enum.map(& &1.attention_sparsity)
      
      variance = calculate_variance(sparsities)
      1.0 - min(variance, 1.0)  # Lower variance = higher consistency
    else
      0.0
    end
  end
  
  defp calculate_layer_performance_score(layer) do
    attention_score = if length(layer.performance_metrics.attention_entropy) > 0 do
      avg_entropy = Enum.sum(layer.performance_metrics.attention_entropy) / length(layer.performance_metrics.attention_entropy)
      1.0 - avg_entropy  # Lower entropy = better focus = higher score
    else
      0.5
    end
    
    usage_score = min(layer.performance_metrics.forward_passes / 100.0, 1.0)
    
    (attention_score + usage_score) / 2
  end
  
  defp calculate_overall_attention_health(layer_analyses) do
    if length(layer_analyses) > 0 do
      total_performance = Enum.reduce(layer_analyses, 0.0, fn analysis, acc ->
        acc + analysis.performance_score
      end)
      
      avg_performance = total_performance / length(layer_analyses)
      
      cond do
        avg_performance > 0.8 -> :excellent
        avg_performance > 0.6 -> :good
        avg_performance > 0.4 -> :fair
        true -> :poor
      end
    else
      :unknown
    end
  end
  
  defp analyze_cross_layer_attention_flow(layer_analyses) do
    if length(layer_analyses) > 1 do
      entropies = Enum.map(layer_analyses, & &1.recent_entropy)
      
      # Analyze flow from input to output layers
      flow_direction = if List.last(entropies) > hd(entropies) do
        :diverging  # Attention spreads out
      else
        :converging  # Attention focuses
      end
      
      flow_smoothness = 1.0 - calculate_variance(entropies)
      
      %{
        direction: flow_direction,
        smoothness: flow_smoothness,
        overall_pattern: if(flow_smoothness > 0.7, do: :coherent, else: :chaotic)
      }
    else
      %{
        direction: :unknown,
        smoothness: 0.0,
        overall_pattern: :insufficient_data
      }
    end
  end
  
  defp detect_cognitive_modes(layer_analyses) do
    # Detect what type of cognitive processing is happening
    avg_entropy = calculate_mean(Enum.map(layer_analyses, & &1.recent_entropy))
    avg_focus = calculate_mean(Enum.map(layer_analyses, &(&1.attention_focus.focus_strength)))
    
    detected_mode = cond do
      avg_entropy < 0.3 and avg_focus > 0.8 ->
        :focused_analytical
      
      avg_entropy > 0.7 and avg_focus < 0.4 ->
        :broad_creative
      
      avg_entropy > 0.5 and avg_focus > 0.6 ->
        :selective_attention
      
      avg_entropy < 0.5 and avg_focus < 0.5 ->
        :systematic_processing
      
      true ->
        :balanced_processing
    end
    
    %{
      primary_mode: detected_mode,
      confidence: calculate_mode_detection_confidence(avg_entropy, avg_focus),
      characteristics: describe_cognitive_mode(detected_mode)
    }
  end
  
  defp calculate_mode_detection_confidence(entropy, focus) do
    # Higher confidence when values are more extreme
    entropy_extremeness = abs(entropy - 0.5) * 2
    focus_extremeness = abs(focus - 0.5) * 2
    
    (entropy_extremeness + focus_extremeness) / 2
  end
  
  defp describe_cognitive_mode(mode) do
    case mode do
      :focused_analytical ->
        "High focus, low entropy - systematic analytical reasoning"
      
      :broad_creative ->
        "Low focus, high entropy - divergent creative thinking"
      
      :selective_attention ->
        "High focus, high entropy - selective information processing"
      
      :systematic_processing ->
        "Low focus, low entropy - methodical step-by-step reasoning"
      
      :balanced_processing ->
        "Moderate focus and entropy - balanced cognitive processing"
    end
  end
  
  defp generate_optimization_suggestions(layer_analyses) do
    suggestions = []
    
    # Check for attention problems
    suggestions = if Enum.any?(layer_analyses, &(&1.recent_entropy > 0.9)) do
      ["Consider reducing model complexity - some layers show very high entropy" | suggestions]
    else
      suggestions
    end
    
    suggestions = if Enum.any?(layer_analyses, &(&1.recent_entropy < 0.1)) do
      ["Consider increasing model capacity - some layers show very low entropy" | suggestions]
    else
      suggestions
    end
    
    # Check for performance issues
    poor_performers = Enum.filter(layer_analyses, &(&1.performance_score < 0.3))
    
    suggestions = if length(poor_performers) > 0 do
      layer_ids = Enum.map(poor_performers, & &1.layer_id)
      ["Consider evolution for layers: #{Enum.join(layer_ids, ", ")}" | suggestions]
    else
      suggestions
    end
    
    # Check for attention flow issues
    entropy_trend_issues = Enum.filter(layer_analyses, &(&1.entropy_trend == :insufficient_data))
    
    suggestions = if length(entropy_trend_issues) > length(layer_analyses) / 2 do
      ["Model needs more training - insufficient attention pattern data" | suggestions]
    else
      suggestions
    end
    
    if length(suggestions) == 0 do
      ["Model appears to be performing well - no critical issues detected"]
    else
      suggestions
    end
  end
  
  defp prepare_attention_visualization_data(%CognitiveTransformer{} = model) do
    # Prepare data for attention pattern visualization
    if map_size(model.attention_patterns) > 0 and Map.has_key?(model.attention_patterns, :layer_patterns) do
      layer_data = Enum.map(model.attention_patterns.layer_patterns, fn pattern ->
        %{
          layer_id: pattern.layer_id,
          entropy: pattern.entropy,
          sparsity: pattern.sparsity,
          max_attention: pattern.max_attention,
          visualization_coords: generate_visualization_coordinates(pattern)
        }
      end)
      
      %{
        layers: layer_data,
        global_flow: model.attention_patterns.attention_flow,
        timestamp: model.attention_patterns.timestamp
      }
    else
      %{
        layers: [],
        global_flow: %{},
        timestamp: DateTime.utc_now()
      }
    end
  end
  
  defp generate_visualization_coordinates(pattern) do
    # Generate 2D coordinates for visualization based on attention properties
    %{
      x: pattern.entropy,  # X-axis represents entropy
      y: pattern.sparsity,  # Y-axis represents sparsity
      size: pattern.max_attention * 10,  # Size represents max attention
      color_intensity: (pattern.entropy + pattern.sparsity) / 2
    }
  end
end