diff --git a/faiss/AutoTune.cpp b/faiss/AutoTune.cpp
index 216d88c05..c1017c56b 100644
--- a/faiss/AutoTune.cpp
+++ b/faiss/AutoTune.cpp
@@ -45,6 +45,8 @@ void AutoTuneCriterion::set_groundtruth(
         gt_D.resize(nq * gt_nnn_in);
         memcpy(gt_D.data(), gt_D_in, sizeof(gt_D[0]) * nq * gt_nnn_in);
     }
+    FAISS_THROW_IF_NOT_MSG(
+            gt_I_in != nullptr, "set_groundtruth: gt_I must be non-null");
     gt_I.resize(nq * gt_nnn_in);
     memcpy(gt_I.data(), gt_I_in, sizeof(gt_I[0]) * nq * gt_nnn_in);
 }
diff --git a/faiss/Clustering.cpp b/faiss/Clustering.cpp
index d0137ee30..b82b1be40 100644
--- a/faiss/Clustering.cpp
+++ b/faiss/Clustering.cpp
@@ -367,7 +367,7 @@ void Clustering::train_encoded(
                         ? std::numeric_limits<double>::max()
                         : std::abs(prev_obj - obj) / std::abs(prev_obj);
 
-                if (change >= 0 && change <= early_stop_threshold) {
+                if (change <= early_stop_threshold) {
                     if (verbose) {
                         printf("\n  Converged at iteration %d: "
                                "objective did not change\n",
@@ -439,6 +439,14 @@ float kmeans_clustering(
         size_t k,
         const float* x,
         float* centroids) {
+    FAISS_THROW_IF_NOT_FMT(
+            d <= static_cast<size_t>(std::numeric_limits<int>::max()),
+            "kmeans_clustering: d=%zu exceeds INT_MAX",
+            d);
+    FAISS_THROW_IF_NOT_FMT(
+            k <= static_cast<size_t>(std::numeric_limits<int>::max()),
+            "kmeans_clustering: k=%zu exceeds INT_MAX",
+            k);
     Clustering clus(static_cast<int>(d), static_cast<int>(k));
     clus.verbose = d * n * k > (size_t(1) << 30);
     // display logs if > 1Gflop per iteration
diff --git a/faiss/python/class_wrappers.py b/faiss/python/class_wrappers.py
index f35b719d4..2acbbd8ba 100644
--- a/faiss/python/class_wrappers.py
+++ b/faiss/python/class_wrappers.py
@@ -1171,7 +1171,7 @@ def handle_AutoTuneCriterion(the_class):
             assert I.shape == D.shape
         self.nq, self.gt_nnn = I.shape
         self.set_groundtruth_c(
-            self.gt_nnn, swig_ptr(D) if D else None, swig_ptr(I))
+            self.gt_nnn, swig_ptr(D) if D is not None else None, swig_ptr(I))
 
     def replacement_evaluate(self, D, I):
         assert I.shape == D.shape
diff --git a/tests/test_autotune.py b/tests/test_autotune.py
index 9cb830aa7..a3b6eee6c 100644
--- a/tests/test_autotune.py
+++ b/tests/test_autotune.py
@@ -5,6 +5,7 @@
 
 
 import unittest
+import numpy as np
 import faiss
 
 from common_faiss_tests import for_all_simd_levels
@@ -48,3 +49,26 @@ class TestParameterSpace(unittest.TestCase):
         ps.set_index_parameter(index, "quantizer_efSearch", 5)
         index2 = faiss.downcast_index(index.quantizer)
         self.assertEqual(index2.hnsw.efSearch, 5)
+
+
+class TestAutoTuneCriterion(unittest.TestCase):
+
+    def test_set_groundtruth_with_distances(self):
+        nq, gt_nnn = 10, 5
+        crit = faiss.OneRecallAtRCriterion(nq, gt_nnn)
+        rng = np.random.default_rng(42)
+        gt_I = rng.integers(0, 100, size=(nq, gt_nnn)).astype("int64")
+        gt_D = rng.random(size=(nq, gt_nnn)).astype("float32")
+        crit.set_groundtruth(gt_D, gt_I)
+        self.assertEqual(crit.gt_nnn, gt_nnn)
+        self.assertEqual(crit.gt_I.size(), nq * gt_nnn)
+
+    def test_set_groundtruth_null_distances(self):
+        # gt_D is documented as optional (None skips the distances copy).
+        nq, gt_nnn = 10, 5
+        crit = faiss.OneRecallAtRCriterion(nq, gt_nnn)
+        rng = np.random.default_rng(42)
+        gt_I = rng.integers(0, 100, size=(nq, gt_nnn)).astype("int64")
+        crit.set_groundtruth(None, gt_I)
+        self.assertEqual(crit.gt_nnn, gt_nnn)
+        self.assertEqual(crit.gt_I.size(), nq * gt_nnn)