for step in range(self.max_steps):
    # 1. Get mini-batch
    instance_idx = step * self.bsize
    batch_indices = data_indices[instance_idx:instance_idx + self.bsize]
    batch = [trainset[i] for i in batch_indices]
    
    # 2. Prepare models and sample trajectories
    models = prepare_models_for_resampling(programs[0], self.num_candidates)
    top_programs = top_k_plus_baseline(self.num_candidates)
    
    # 3-8. Core optimization steps...

# ✅ Good: Proper functional iteration
final_state = Enum.reduce(0..(config.max_steps - 1), 
  {programs, program_scores, winning_programs, next_program_idx},
  fn step, {current_programs, current_scores, current_winning, prog_idx} ->
    # ⚠️ Partial: Has structure but missing sophisticated logic
    # ❌ Missing: Proper program selection algorithms
  end)

defp softmax_sample(program_indices, _all_programs, temperature) do
  if is_list(program_indices) and length(program_indices) > 0 do
    scores = Enum.map(program_indices, fn _idx -> 0.5 end)  # ❌ BROKEN: Fixed scores!
    # Should calculate real scores from program performance
  end
end

def softmax_sample(rng_obj, program_idxs, temperature):
    scores = [calc_average_score(idx) for idx in program_idxs]  # ✅ Real scores
    exps = [np.exp(s / temperature) for s in scores]
    # Proper probability sampling...

# ✅ DSPEx: Comprehensive bucket analysis
%Bucket{
  trajectories: sorted_trajectories,
  max_score: max_score,
  min_score: min_score,
  avg_score: avg_score,
  max_to_min_gap: max_score - min_score,
  max_to_avg_gap: max_score - avg_score,
  metadata: %{
    max_to_min_gap: max_to_min_gap,
    max_to_avg_gap: max_to_avg_gap,
    max_score: max_score,
    avg_score: avg_score
  }
}

self.strategies = [append_a_demo(self.demo_input_field_maxlen), append_a_rule]
strategy = rng.choice(self.strategies)  # Random selection
new_system = strategy(bucket, system_candidate, **strategy_kwargs)

# ✅ Good: Proper behavior definition
@behaviour DSPEx.Teleprompter.SIMBA.Strategy

# ✅ Excellent: AppendDemo implementation
defmodule DSPEx.Teleprompter.SIMBA.Strategy.AppendDemo do
  # Full implementation with Poisson sampling
end

# ❌ Missing: Additional strategies like rule-based optimization

# Python DSPy (Complete)
def calc_average_score(prog_idx):
    scores = program_scores.get(prog_idx, [])
    return sum(scores) / len(scores) if scores else 0.0

def top_k_plus_baseline(k):
    scored_programs = sorted(programs, key=lambda p: calc_average_score(p.simba_idx), reverse=True)
    top_k = [p.simba_idx for p in scored_programs[:k]]
    if 0 not in top_k:  # Ensure baseline is included
        top_k = [0] + top_k[:k-1]
    return top_k

# DSPEx (Missing sophisticated logic)
defp select_top_programs(programs, program_scores, num_candidates) do
  # ❌ Oversimplified - missing the sophisticated selection logic
  program_avg_scores = programs |> Enum.with_index() |> Enum.map(fn {_program, idx} ->
    scores = Map.get(program_scores, idx, [])
    avg_score = if Enum.empty?(scores), do: 0.5, else: Enum.sum(scores) / length(scores)
    {idx, avg_score}
  end)
  # Missing proper baseline guarantee and selection logic
end

# ❌ BROKEN: Fixed scores instead of real performance scores
scores = Enum.map(program_indices, fn _idx -> 0.5 end)

# Python DSPy (Required)
def top_k_plus_baseline(k):
    # Sophisticated program selection ensuring baseline + top performers

# Python DSPy (Required)
def calc_average_score(prog_idx):
    scores = program_scores.get(prog_idx, [])
    return sum(scores) / len(scores) if scores else 0.0