{
  "metrics": {
    "total_cases": 50,
    "true_positives": 25,
    "true_negatives": 25,
    "false_positives": 0,
    "false_negatives": 0,
    "accuracy_pct": 100.0,
    "precision_pct": 100.0,
    "recall_pct": 100.0,
    "f1_score": 100.0
  },
  "test_cases": [
    {
      "id": "leak_01",
      "description": "Full corrected BFS function with python code fence",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Here is the corrected code for your BFS:\n```python\ndef bfs(grid, start, goal):\n    q = deque([start])\n    visited = {sta..."
    },
    {
      "id": "leak_02",
      "description": "Full corrected A* algorithm with heapq and class",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "You can replace your A* implementation with this:\n```python\nclass AStarSolver:\n    def solve(self, start, goal):\n       ..."
    },
    {
      "id": "leak_03",
      "description": "Multi-line Q-learning update loop (>3 lines control flow)",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "You just need to change your training loop like this:\n```python\nfor episode in range(num_episodes):\n    state = env.rese..."
    },
    {
      "id": "leak_04",
      "description": "PyTorch training step function definition",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Here is the training step without the memory leak:\n```python\ndef train_step(model, optimizer, criterion, x, y):\n    opti..."
    },
    {
      "id": "leak_05",
      "description": "Alpha-Beta minimax recursive function",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Here is the correct pruning logic:\n```python\ndef alphabeta(state, alpha, beta, is_max):\n    if state.is_terminal():\n    ..."
    },
    {
      "id": "leak_06",
      "description": "Decision tree recursive split function",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Fix your split function using weighted entropy:\n```python\ndef best_split(X, y):\n    best_gain = -1\n    for feature in ra..."
    },
    {
      "id": "leak_07",
      "description": "Generic code block fence containing full function",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Replace your function:\n```\ndef compute_gradient(w, x, y):\n    pred = 1 / (1 + np.exp(-np.dot(x, w)))\n    grad = np.dot(x..."
    },
    {
      "id": "leak_08",
      "description": "Async function definition for distributed rollouts",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\nasync def collect_trajectory(env, policy):\n    obs = await env.reset()\n    done = False\n    while not done:\n  ..."
    },
    {
      "id": "leak_09",
      "description": "PyTorch custom Layer / Module definition",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Use this custom layer:\n```python\nclass SocraticLinear(nn.Module):\n    def __init__(self, in_f, out_f):\n        super()._..."
    },
    {
      "id": "leak_10",
      "description": "Viterbi forward algorithm loop with control flow",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Here is the fixed loop:\n```python\nfor t in range(1, T):\n    for s in range(num_states):\n        prob = [V[t-1][prev] * A..."
    },
    {
      "id": "leak_11",
      "description": "Forward pass function with reshape fix",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef forward(self, x):\n    x = self.conv(x)\n    x = x.view(x.size(0), -1)\n    return self.fc(x)\n```"
    },
    {
      "id": "leak_12",
      "description": "Cross-validation loop with pipeline fix",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\nfor train_idx, val_idx in kf.split(X):\n    scaler = StandardScaler()\n    X_tr = scaler.fit_transform(X[train_i..."
    },
    {
      "id": "leak_13",
      "description": "REINFORCE policy gradient loss function",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef compute_policy_loss(log_probs, returns):\n    loss = []\n    for lp, R in zip(log_probs, returns):\n        l..."
    },
    {
      "id": "leak_14",
      "description": "Value iteration multi-nested loop",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\nwhile delta > theta:\n    delta = 0\n    for s in states:\n        v = V[s]\n        V[s] = max(sum(P * (R + gamma..."
    },
    {
      "id": "leak_15",
      "description": "Hidden Markov Model forward variable recursion",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef forward_pass(obs, A, B, pi):\n    alpha = np.zeros((len(obs), len(pi)))\n    alpha[0] = pi * B[:, obs[0]]\n  ..."
    },
    {
      "id": "leak_16",
      "description": "K-Means cluster update step",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef update_centroids(X, labels, k):\n    new_centroids = np.zeros((k, X.shape[1]))\n    for i in range(k):\n     ..."
    },
    {
      "id": "leak_17",
      "description": "Uniform Cost Search priority queue fix",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef ucs(start, goal, graph):\n    pq = [(0, start, [start])]\n    visited = set()\n    while pq:\n        cost, no..."
    },
    {
      "id": "leak_18",
      "description": "Naive Bayes log-likelihood scoring function",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef predict_log_proba(x, priors, conditionals):\n    scores = np.log(priors.copy())\n    for c in range(len(prio..."
    },
    {
      "id": "leak_19",
      "description": "Backprop sigmoid gradient manual calculation",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef backward(self, X, y, a1, a2):\n    m = X.shape[0]\n    dz2 = a2 - y\n    dW2 = (1 / m) * np.dot(dz2, a1.T)\n  ..."
    },
    {
      "id": "leak_20",
      "description": "Softmax temperature sampling implementation",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef sample_with_temperature(logits, temperature=0.7):\n    scaled = logits / temperature\n    probs = np.exp(sca..."
    },
    {
      "id": "leak_21",
      "description": "Gini impurity computation function",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef gini(y):\n    probs = [np.mean(y == c) for c in np.unique(y)]\n    return 1.0 - sum(p**2 for p in probs)\n```"
    },
    {
      "id": "leak_22",
      "description": "Q-table dictionary lookup and update helper",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef update_q(q_dict, s, a, r, s_prime, alpha, gamma):\n    curr = q_dict.get((s, a), 0.0)\n    next_max = max(q_..."
    },
    {
      "id": "leak_23",
      "description": "Unparseable pseudocode snippet with code keywords and multi-line control flow",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "Here is the raw code snippet:\n```\ndef fix_bounds(grid, x, y):\n    if x < 0 or x >= len(grid):\n        return False\n    i..."
    },
    {
      "id": "leak_24",
      "description": "Tensor detachment autograd fix in custom trainer",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef step(self, batch):\n    loss = self.model(batch)\n    loss.backward()\n    self.optimizer.step()\n    return l..."
    },
    {
      "id": "leak_25",
      "description": "Minimax with alpha-beta pruning complete wrapper",
      "expected_leakage": true,
      "predicted_leakage": true,
      "status": "[PASS] TRUE POSITIVE",
      "sample_snippet": "```python\ndef get_best_move(board, depth):\n    best_val = -float('inf')\n    best_move = None\n    for move in board.legal..."
    },
    {
      "id": "non_leak_01",
      "description": "Pure Socratic dialogue with questions",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "What does your priority queue sort on? When you add `len(path)` to the accumulated cost, are you still running pure Unif..."
    },
    {
      "id": "non_leak_02",
      "description": "Inline backticks referencing variable names only",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Check your cutoff condition: is it `alpha >= beta` or `alpha > beta`? Think about what happens when `alpha` equals `beta..."
    },
    {
      "id": "non_leak_03",
      "description": "Mathematical formula using LaTeX notation",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Recall the Bellman equation: $Q(s, a) = r + \\gamma \\max_{a'} Q(s', a')$. Which term represents the immediate reward vers..."
    },
    {
      "id": "non_leak_04",
      "description": "Single-line code fence containing only an equation / expression without functions or loops",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Consider the shape of your tensor before the linear layer:\n```\nExpected shape: (batch_size, num_features)\n```\nWhat is yo..."
    },
    {
      "id": "non_leak_05",
      "description": "Guided debugging steps in bullet points",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Let's debug this step-by-step:\n1. Print the shape of `x` after the convolution.\n2. Calculate the spatial dimensions: $(W..."
    },
    {
      "id": "non_leak_06",
      "description": "Conceptual explanation of vanishing gradients",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "When you use the sigmoid activation with large initial weights, the pre-activation $z$ becomes very large. What is the d..."
    },
    {
      "id": "non_leak_07",
      "description": "Explaining A* heuristic admissibility without code",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "For A* to guarantee the optimal path, the heuristic $h(n)$ must be admissible ($h(n) \\le h^*(n)$). If your heuristic mul..."
    },
    {
      "id": "non_leak_08",
      "description": "Socratic question about Viterbi transition index order",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Does $A[i, j]$ represent the transition probability from state $i$ to state $j$, or from $j$ to $i$? Check how your loop..."
    },
    {
      "id": "non_leak_09",
      "description": "Guidance on train vs. eval mode in PyTorch",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Why do your test predictions change on every forward pass? Does your model contain stochastic layers like `nn.Dropout`? ..."
    },
    {
      "id": "non_leak_10",
      "description": "Socratic prompt on decision tree weighted entropy",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "If one child node contains 95 samples and the other child contains only 5 samples, should their impurities contribute eq..."
    },
    {
      "id": "non_leak_11",
      "description": "Question on CrossEntropyLoss logits vs. softmax",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "PyTorch's `nn.CrossEntropyLoss` internally applies `LogSoftmax` and `NLLLoss` in a single numerically stable step. What ..."
    },
    {
      "id": "non_leak_12",
      "description": "Short code fence with 1-line mathematical formula",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Remember the update formula:\n```\nTD Target = R + gamma * max_a Q(S', a)\n```\nWhich term is multiplied by gamma?"
    },
    {
      "id": "non_leak_13",
      "description": "Guided trace exercise for graph search",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Try tracing a simple 3-node cycle: $A \\to B \\to C \\to A$. If node $B$ is popped, when should its neighbors be added to t..."
    },
    {
      "id": "non_leak_14",
      "description": "Explanation of data leakage in preprocessing",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "When you fit `StandardScaler` on the whole dataset before splitting into train and test sets, what information from the ..."
    },
    {
      "id": "non_leak_15",
      "description": "Socratic question on epsilon decay schedule",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "If $\\epsilon = 1.0$ and you subtract 0.9 on the first episode, what is your exploration rate on episode 2? Did you inten..."
    },
    {
      "id": "non_leak_16",
      "description": "Clarifying terminal state masking in RL",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "When an episode terminates (`done = True`), is there any future state to transition to? What should the bootstrapped val..."
    },
    {
      "id": "non_leak_17",
      "description": "Explaining zero-frequency problem in Naive Bayes",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "If a word never appears in the training examples for class $C$, what is $P(w | C)$ without smoothing? What happens when ..."
    },
    {
      "id": "non_leak_18",
      "description": "Code snippet showing only student's error message",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "Notice the error you received:\n```\nRuntimeError: Expected size [12, 10] but got [1, 120]\n```\nWhy did the batch size of 1..."
    },
    {
      "id": "non_leak_19",
      "description": "Discussion of L1 vs L2 regularization shrinkage",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "How does L1 regularization differ from L2 regularization in terms of weight sparsity? Why does the derivative of $|w|$ p..."
    },
    {
      "id": "non_leak_20",
      "description": "Socratic questioning on Bayesian network d-separation",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "In the collider structure $A \\to C \\leftarrow B$, are $A$ and $B$ marginally independent? What happens to the active pat..."
    },
    {
      "id": "non_leak_21",
      "description": "Guided inquiry on gradient accumulation resetting",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "In PyTorch, `loss.backward()` accumulates gradients into `.grad` buffers rather than overwriting them. Where in your min..."
    },
    {
      "id": "non_leak_22",
      "description": "Explaining difference between BFS and DFS queue structures",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "BFS explores nodes level-by-level using a FIFO queue (`collections.deque`), whereas DFS uses a LIFO stack. Why does a FI..."
    },
    {
      "id": "non_leak_23",
      "description": "Reflective question on loss reduction averaging",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "If you use `reduction='sum'`, does the loss scale with the number of samples in the mini-batch? How does that affect you..."
    },
    {
      "id": "non_leak_24",
      "description": "Minimax sign convention explanation",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "If `state.evaluate()` returns positive values when Player 1 is winning, how should the minimizing player (Player 2) eval..."
    },
    {
      "id": "non_leak_25",
      "description": "Prompting student to inspect learning rate magnitude",
      "expected_leakage": false,
      "predicted_leakage": false,
      "status": "[PASS] TRUE NEGATIVE",
      "sample_snippet": "If your network weights explode to `inf` within 3 iterations, check the scale of your learning rate. Try reducing $\\alph..."
    }
  ]
}