@@ -110,15 +110,13 @@ def test_offline_lasso_regression_and_predict():
     assert pred.shape[0] == x.shape[0]
 
 
-def test_offline_elastic_net_regression_fit_and_broken_predict():
-    """ElasticNet ``call`` (fit) runs through the gradient-descent solver.
-
-    NOTE: ``ElasticNetRegression.predict`` is inconsistent with ``call``: ``call``
-    builds features via ``polynomial_features(inputs, degree=...)`` (default
-    ``add_bias=True`` -> bias column added), while ``predict`` passes
-    ``add_bias=self.add_bias`` (default ``False``). The feature counts therefore
-    differ (7 vs 6) and ``predict`` raises a shape ``TypeError``. We exercise the
-    fit path and pin the broken predict.
+def test_offline_elastic_net_regression_fit_and_predict():
+    """ElasticNet ``call`` (fit) and ``predict`` are now consistent.
+
+    Fixed in audit 2026-06-19: ``call`` previously built features with the default
+    ``add_bias=True`` while ``predict`` used ``add_bias=self.add_bias`` (default
+    ``False``), so the feature counts differed and ``predict`` crashed. ``call``
+    now passes ``add_bias=self.add_bias``, so fit and predict agree.
     """
     from brainpy.algorithms.offline import ElasticNetRegression
 
@@ -127,8 +125,8 @@ def test_offline_elastic_net_regression_fit_and_broken_predict():
     model = ElasticNetRegression(alpha=0.05, degree=2, l1_ratio=0.5, max_iter=30)
     w = model(y, x)
     assert not bool(jnp.isnan(w).any())
-    with pytest.raises((TypeError, ValueError)):
-        model.predict(w, x)
+    pred = model.predict(w, x)
+    assert pred.shape[0] == x.shape[0]
 
 
 def test_offline_polynomial_regression_and_predict():
@@ -158,19 +156,22 @@ def test_offline_polynomial_ridge_regression_and_predict():
     assert pred.shape[0] == x.shape[0]
 
 
-def test_offline_logistic_regression_known_indexerror():
-    """NOTE: dead/broken path. ``LogisticRegression.call`` flattens ``targets``
-    to 1-D and then indexes ``targets.shape[1]``, raising ``IndexError``. Its
-    body (offline.py lines 391-415) is unreachable; we pin the broken behavior.
+def test_offline_logistic_regression_fit():
+    """``LogisticRegression.call`` now fits instead of crashing.
+
+    Fixed in audit 2026-06-19: ``call`` previously flattened ``targets`` to 1-D
+    and then indexed ``targets.shape[1]`` (``IndexError``); it now initialises a
+    1-D parameter vector and runs the gradient-descent solver.
     """
     from brainpy.algorithms.offline import LogisticRegression
 
     bm.random.seed(6)
     rng = np.random.RandomState(6)
     x = jnp.asarray(rng.randn(20, 2).astype('float32'))
     y = jnp.asarray((np.asarray(x)[:, :1] > 0).astype('float32'))
-    with pytest.raises(IndexError):
-        LogisticRegression(max_iter=50)(y, x)
+    w = LogisticRegression(max_iter=50)(y, x)
+    assert w is not None
+    assert not bool(jnp.isnan(jnp.asarray(w)).any())
 
 
 def test_offline_registry_helpers_and_base_repr():
 
@@ -406,15 +406,15 @@ def test_adamw_invalid_hyperparams():
         bp.optim.AdamW(lr=0.01, weight_decay=-1.0)
 
 
-def test_adan_constructs_update_is_known_bug():
-    """Adan constructs, but REMAINING BUG: ``Adan.update`` passes a tuple operand to
-    ``lax.cond`` while the branch lambdas expect two args (optimizer.py:818),
-    raising TypeError. Pinned so the construct path is covered + the bug documented."""
+def test_adan_constructs_and_updates():
+    """Adan constructs AND ``update`` runs (fixed in audit 2026-06-19: the
+    ``lax.cond`` first-step guard previously splatted its tuple operand, raising
+    ``TypeError`` on every call). The update now applies and leaves the var finite."""
     w = _train_var()
     adan = bp.optim.Adan(lr=0.01, train_vars={'w': w})
     repr(adan)
-    with pytest.raises(TypeError):
-        adan.update({'w': bm.ones((2, 3)) * 0.1})
+    adan.update({'w': bm.ones((2, 3)) * 0.1})
+    assert not bool(jnp.isnan(bm.as_jax(w)).any())
 
 
 def test_adan_invalid_betas():
 
@@ -65,7 +65,9 @@ def test_multi_order_stride_constant(self):
 
 class TestNVARErrors(parameterized.TestCase):
     def test_order_below_two_raises(self):
-        with self.assertRaises(AssertionError):
+        # ``NVAR.__init__`` validates ``order`` via ``check.is_integer(min_bound=2)``,
+        # which now (correctly) raises a ``ValueError`` for ``order < 2``.
+        with self.assertRaises(ValueError):
             NVAR(num_in=3, delay=2, order=1)
 
     def test_non_bool_constant_raises(self):
 
@@ -179,7 +179,15 @@ def __init__(
             monitors = {k: (self.variables[k], i) for k, i in monitors}
         elif isinstance(monitors, dict):
             monitors = self._format_dict_monitors(monitors)
-            monitors = {k: ((self.variables[i], i) if isinstance(i, str) else i) for k, i in monitors.items()}
+            # ``_format_dict_monitors`` yields ``{user_key: (var_name_or_Variable, index)}``.
+            # The integrator's state variables live in ``self.variables`` (they are NOT
+            # attributes of ``self.target``), so a string variable-name must be resolved
+            # here against ``self.variables``; the base runner then receives an already
+            # resolved ``(Variable, index)`` pair and passes it through unchanged.
+            monitors = {k: ((self.variables[v[0]], v[1])
+                            if (isinstance(v, (tuple, list)) and isinstance(v[0], str))
+                            else v)
+                        for k, v in monitors.items()}
         else:
             raise ValueError
 
 
@@ -239,22 +239,21 @@ def test_ridge_intercept_not_over_penalized():
     assert abs(slope) < abs(intercept), f"slope not shrunk: slope={slope}"
 
 
-def test_logistic_regression_remaining_bug():
-    """Document a remaining (un-audited) bug in ``LogisticRegression.call``.
-
-    ``call`` flattens ``targets`` to 1-D and then indexes ``targets.shape[1]``,
-    which raises ``IndexError: tuple index out of range``.  This is independent
-    of the H-46/H-47 ridge fixes and is *not* in the assigned fix scope, so the
-    test pins the current broken behavior (and exercises the code path) rather
-    than asserting success.  See summary notes.
+def test_logistic_regression_fit_runs():
+    """``LogisticRegression.call`` now fits instead of raising ``IndexError``.
+
+    Fixed in audit 2026-06-19: ``call`` previously flattened ``targets`` to 1-D
+    and then indexed ``targets.shape[1]``; it now initialises a 1-D parameter
+    vector and runs the gradient-descent solver to completion.
     """
     from brainpy.algorithms.offline import LogisticRegression
 
     rng = np.random.RandomState(3)
     x = jnp.asarray(rng.randn(30, 2))
     y = jnp.asarray((np.asarray(x)[:, :1] > 0).astype("float32"))
-    with pytest.raises(IndexError):
-        LogisticRegression(max_iter=100)(y, x)
+    w = LogisticRegression(max_iter=100)(y, x)
+    assert w is not None
+    assert not bool(jnp.isnan(jnp.asarray(w)).any())
 
 
 def test_offline_least_square_and_polynomial():