diff --git a/helper_code.py b/helper_code.py
index cd486bd9f8..cefe2ec543 100644
--- a/helper_code.py
+++ b/helper_code.py
@@ -245,50 +245,75 @@ def get_signal_names(string):
 ### Evaluation functions
 
 # Compute the Challenge score.
-def compute_challenge_score(labels, outputs, fraction_capacity = 0.05, num_permutations = 10**4, seed=12345):
-    # Check the data.
-    assert len(labels) == len(outputs)
-    num_instances = len(labels)
-    capacity = int(fraction_capacity * num_instances)
-
-    # Convert the data to NumPy arrays, as needed, for easier indexing.
+def compute_challenge_score(labels, outputs, fraction_capacity=0.05, tie_tol=0.0, **kwargs):
+    """
+    Exact expected TPR under uniform random tie-breaking at the capacity cutoff.
+
+    Parameters
+    ----------
+    labels : array-like of shape (n,)
+        Binary labels, expected to be 0/1 (other values are treated as not-equal to 1.0).
+    outputs : array-like of shape (n,)
+        Scores used for ranking (higher is better).
+    fraction_capacity : float, default=0.05
+        Fraction of instances to select. Capacity C = floor(fraction_capacity * n).
+    tie_tol : float, default=0.0
+        Two scores s1, s2 are considered tied if |s1 - s2| <= tie_tol.
+        Use 0.0 to require exact equality, or a small tolerance (e.g., 1e-12) if desired.
+    **kwargs : additional keyword arguments (ignored). Included for compatibility with previous version.
+
+    Returns
+    -------
+    tpr : float
+        Expected true positive rate over uniform random tie-breaking.
+        Returns NaN if there are no positive labels.
+    """
     labels = np.asarray(labels, dtype=np.float64)
     outputs = np.asarray(outputs, dtype=np.float64)
-
-    # Permute the labels and outputs so that we can approximate the expected confusion matrix for "tied" probabilities.
-    tp = np.zeros(num_permutations)
-    fp = np.zeros(num_permutations)
-    fn = np.zeros(num_permutations)
-    tn = np.zeros(num_permutations)
-
-    if seed is not None:
-        np.random.seed(seed)
-
-    for i in range(num_permutations):
-        permuted_idx = np.random.permutation(np.arange(num_instances))
-        permuted_labels = labels[permuted_idx]
-        permuted_outputs = outputs[permuted_idx]
-
-        ordered_idx = np.argsort(permuted_outputs, stable=True)[::-1]
-        ordered_labels = permuted_labels[ordered_idx]
-
-        tp[i] = np.sum(ordered_labels[:capacity] == 1)
-        fp[i] = np.sum(ordered_labels[:capacity] == 0)
-        fn[i] = np.sum(ordered_labels[capacity:] == 1)
-        tn[i] = np.sum(ordered_labels[capacity:] == 0)
-
-    tp = np.mean(tp)
-    fp = np.mean(fp)
-    fn = np.mean(fn)
-    tn = np.mean(tn)
-
-    # Compute the true positive rate.
-    if tp + fn > 0:
-        tpr = tp / (tp + fn)
-    else:
-        tpr = float('nan')
-
-    return tpr
+    assert labels.shape == outputs.shape
+
+    n = labels.size
+    capacity = int(np.floor(fraction_capacity * n))
+    total_positives = float(np.sum(labels == 1.0))
+
+    # Handle degenerate cases first
+    if total_positives == 0.0:
+        return float('nan')           # undefined TPR if no positives
+    if capacity <= 0:
+        return 0.0
+    if capacity >= n:
+        return 1.0                    # everything selected; TP = P
+
+    # Sort by score descending (stable so equal scores remain contiguous)
+    order = np.argsort(outputs, kind='mergesort')[::-1]
+    scores_sorted = outputs[order]
+    labels_sorted = labels[order]
+
+    # If the boundary between included/excluded does not split a tie, deterministic case
+    v_incl = scores_sorted[capacity-1]
+    v_excl = scores_sorted[capacity]
+    if not np.isclose(v_incl, v_excl, atol=tie_tol):
+        tp = float(np.sum(labels_sorted[:capacity] == 1.0))
+        return tp / total_positives
+
+    # Tie is split at the boundary: find the contiguous tie-block [start, end)
+    tie_mask = np.isclose(scores_sorted, v_incl, atol=tie_tol)
+    tie_idxs = np.where(tie_mask)[0]          # contiguous because scores_sorted is sorted
+    start = int(tie_idxs[0])
+    end = int(tie_idxs[-1]) + 1               # exclusive
+    g = end - start                           # group size
+    m = capacity - start                      # number we must take from this group (0 < m < g)
+
+    # Deterministic contributions (strictly above the tie block)
+    pos_before = float(np.sum(labels_sorted[:start] == 1.0))
+
+    # Tie-block composition
+    k = float(np.sum(labels_sorted[start:end] == 1.0))   # positives in tie block
+
+    # Exact expectation via Hypergeometric: E[TP_from_tie] = m * (k/g)
+    expected_tp = pos_before + (m * (k / g))
+
+    return expected_tp / total_positives
 
 def compute_auc(labels, outputs):
     import sklearn