diff --git a/tests/datasets/test_dataset_tools.py b/tests/datasets/test_dataset_tools.py
index f9365e7da..42381294f 100644
--- a/tests/datasets/test_dataset_tools.py
+++ b/tests/datasets/test_dataset_tools.py
@@ -381,7 +381,7 @@ def test_add_features_with_callable(sample_dataset, tmp_path):
 def test_add_features_with_multidimensional_values(sample_dataset, tmp_path):
     """Test adding a multi-dimensional per-frame feature."""
     num_frames = sample_dataset.meta.total_frames
-    latent_values = np.random.randn(num_frames, 1, 4).astype(np.float32)
+    feature_values = np.random.randn(num_frames, 1, 4).astype(np.float32)
 
     feature_info = {
         "dtype": "float32",
@@ -389,7 +389,7 @@ def test_add_features_with_multidimensional_values(sample_dataset, tmp_path):
         "names": None,
     }
     features = {
-        "latent_vectors": (latent_values, feature_info),
+        "multi_dim_feature": (feature_values, feature_info),
     }
 
     with (
@@ -397,19 +397,19 @@ def test_add_features_with_multidimensional_values(sample_dataset, tmp_path):
         patch("lerobot.datasets.dataset_metadata.snapshot_download") as mock_snapshot_download,
     ):
         mock_get_safe_version.return_value = "v3.0"
-        mock_snapshot_download.return_value = str(tmp_path / "with_latents")
+        mock_snapshot_download.return_value = str(tmp_path / "with_multi_dim_feature")
 
         new_dataset = add_features(
             dataset=sample_dataset,
             features=features,
-            output_dir=tmp_path / "with_latents",
+            output_dir=tmp_path / "with_multi_dim_feature",
         )
 
-    assert "latent_vectors" in new_dataset.meta.features
+    assert "multi_dim_feature" in new_dataset.meta.features
     sample_item = new_dataset[0]
-    assert "latent_vectors" in sample_item
-    assert isinstance(sample_item["latent_vectors"], torch.Tensor)
-    assert tuple(sample_item["latent_vectors"].shape) == (1, 4)
+    assert "multi_dim_feature" in sample_item
+    assert isinstance(sample_item["multi_dim_feature"], torch.Tensor)
+    assert tuple(sample_item["multi_dim_feature"].shape) == (1, 4)
 
 
 def test_add_existing_feature(sample_dataset, tmp_path):