diff --git a/giant/data/transforms.py b/giant/data/transforms.py
index 9b74ea7..d83148c 100644
--- a/giant/data/transforms.py
+++ b/giant/data/transforms.py
@@ -530,11 +530,44 @@ def build_cond_features(
     cond_cat = np.column_stack([pdg_idx, mat_idx])
 
     if cond_normalizer is not None:
-        cond_cont = cond_normalizer.transform(cond_cont)
+        cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, conditioning)
 
     return cond_cont, cond_cat
 
 
+def _cond_normalizer_transform(
+    cond_cont: np.ndarray, cond_normalizer: "Normalizer", conditioning: str
+) -> np.ndarray:
+    """Apply ``cond_normalizer``, padding a legacy narrower normalizer if needed.
+
+    Checkpoints trained before physical-property conditioning (``COND_DIM``
+    8->15, ``giant/constants.py``) saved a ``COND_DIM_BASE``-wide (8) cond
+    normalizer, fit before ``build_cond_features`` grew the extra physical
+    columns. In "embedding" mode those columns are never read by
+    ``ConditionEncoder`` (``giant/model/network.py``), so padding the missing
+    entries with mean=0/std=1 is a safe no-op that keeps such checkpoints
+    usable under the current, always-``COND_DIM``-wide contract. In
+    "physical" mode the physical columns are load-bearing, so a mismatch
+    there is a real incompatibility, not something to paper over.
+    """
+    mean, std = cond_normalizer.mean, cond_normalizer.std
+    assert mean is not None and std is not None, "Normalizer not fitted"
+    width = cond_cont.shape[-1]
+    if mean.shape[-1] < width:
+        if conditioning != "embedding":
+            raise ValueError(
+                f"cond normalizer has {mean.shape[-1]} columns, expected "
+                f"{width}, and conditioning={conditioning!r} reads the "
+                "physical columns directly — this checkpoint predates "
+                "physical-property conditioning and can't be safely padded; "
+                "retrain it under the current code."
+            )
+        pad = width - mean.shape[-1]
+        mean = np.concatenate([mean, np.zeros(pad, dtype=mean.dtype)])
+        std = np.concatenate([std, np.ones(pad, dtype=std.dtype)])
+    return ((cond_cont - mean) / std).astype(np.float32)
+
+
 def build_features(
     data: dict[str, np.ndarray],
     pdg_map: dict[int, int],
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index a7af975..c9bc7ac 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -397,3 +397,46 @@ def test_build_cond_features_mass_charge_override(fake_material_props):
         cond_cont[:, COND_DIM_BASE], log_transform(np.array([123.0, 456.0]))
     )
     np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 1], [2.0, -2.0])
+
+
+def test_build_cond_features_pads_legacy_normalizer_in_embedding_mode():
+    """A pre-physical-conditioning checkpoint's cond normalizer is COND_DIM_BASE
+    (8) wide, fit before build_cond_features grew the extra physical columns.
+    In "embedding" mode those columns are never read downstream, so a legacy
+    normalizer should be usable as-is (padded, not rejected)."""
+    data = _minimal_step_data(3)
+    pdg_map, mat_map = {11: 0}, {"PbWO4": 0}
+    legacy_norm = Normalizer()
+    legacy_norm.mean = np.zeros(COND_DIM_BASE, dtype=np.float32)
+    legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32)
+
+    cond_cont, _ = build_cond_features(
+        data, pdg_map, mat_map, cond_normalizer=legacy_norm, conditioning="embedding"
+    )
+
+    assert cond_cont.shape[-1] == COND_DIM
+    # padded physical columns are zero-filled pre-normalization and
+    # mean=0/std=1 post-normalization, so they should come out as zero
+    np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE:], 0.0)
+
+
+def test_build_cond_features_rejects_legacy_normalizer_in_physical_mode(
+    fake_material_props,
+):
+    """Unlike "embedding" mode, "physical" mode actually reads the physical
+    columns, so a legacy 8-wide normalizer can't be silently padded — that
+    would silently feed the network un-normalized physical properties."""
+    data = _minimal_step_data(3)
+    pdg_map, mat_map = {11: 0}, {"PbWO4": 0}
+    legacy_norm = Normalizer()
+    legacy_norm.mean = np.zeros(COND_DIM_BASE, dtype=np.float32)
+    legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32)
+
+    with pytest.raises(ValueError, match="predates physical-property conditioning"):
+        build_cond_features(
+            data,
+            pdg_map,
+            mat_map,
+            cond_normalizer=legacy_norm,
+            conditioning="physical",
+        )