Merge nucleic/sleek-ember-seal-uady into dev

tests/test_deep_contract.py

@@ -121,6 +121,8 @@ class TargetAndMetricTests(unittest.TestCase):
         self.assertEqual(0, metrics["primaryHardSlice"]["accuracy"])
         self.assertAlmostEqual(1 / 6, metrics["selectionScore"])
         self.assertEqual(1, metrics["secondary"]["accuracy"])
+        self.assertEqual(["writing"], metrics["secondary"]["supportedLabels"])
+        self.assertEqual(1, metrics["secondary"]["supportedMacroRecall"])
         self.assertEqual(1, metrics["mixed"]["f1"])
 
     def test_mixed_threshold_prefers_higher_threshold_on_f1_tie(self):

tests/test_deep_model_mlx.py

@@ -18,6 +18,7 @@ from deep_model_mlx import (
     save_weights,
 )
 from purpose_data import DataError, LABELS
+from train_deep_mlx import _distillation_loss
 
 
 def tiny_config(*, checkpointing=False):
@@ -97,6 +98,23 @@ class DeepModelTests(unittest.TestCase):
         mx.eval(value, gradients)
         self.assertTrue(float(value.item()) > 0)
 
+    def test_distillation_loss_matches_teacher_and_backpropagates(self):
+        teacher = mx.array([[2.0, 0.0, -1.0]])
+
+        def loss(student):
+            return _distillation_loss(
+                mx,
+                student,
+                teacher,
+                temperature=2.0,
+                weights=mx.ones((1,)),
+            )
+
+        value, gradient = mx.value_and_grad(loss)(teacher)
+        mx.eval(value, gradient)
+        self.assertAlmostEqual(0.0, float(value.item()), places=6)
+        self.assertEqual(teacher.shape, gradient.shape)
+
     def test_checkpoint_round_trip(self):
         model = ModernBertForPurposeClassification(tiny_config())
         with tempfile.TemporaryDirectory() as temp: