diff --git a/algebra/versor.py b/algebra/versor.py
index 51f92df3..98f31035 100644
--- a/algebra/versor.py
+++ b/algebra/versor.py
@@ -14,7 +14,6 @@ _CONSTRUCTION_RESIDUE_TOLERANCE = 1e-2
 _NEAR_ZERO_TOLERANCE = 1e-12
 _DENSE_SEED_MIN_COMPONENTS = 8
 _SEED_BIVECTORS = (6, 7, 8, 10, 11, 13)
-_NULL_SCALAR_TOLERANCE = 1e-9
 
 
 def _array_dtype(v: np.ndarray) -> np.dtype:
@@ -95,22 +94,15 @@ def construction_seed_versor(v: np.ndarray) -> np.ndarray:
     return _seed_to_rotor(v, _array_dtype(v))
 
 
-
-def _is_null_vector(v: np.ndarray) -> bool:
-    product = geometric_product(v, v).astype(np.float64)
-    return float(np.linalg.norm(product)) < _NULL_SCALAR_TOLERANCE
-
-
 def _close_applied_versor(v: np.ndarray, dtype: np.dtype) -> np.ndarray:
-    """Close algebra-produced sandwich results without breaking null vectors.
+    """Close algebra-produced sandwich results for runtime field states.
 
-    CGA sandwiching must preserve null vectors as null vectors. Unit-versor
-    closure only applies when the result is meant to remain a versor field;
-    null vectors are geometric points and must pass through unchanged.
+    ``versor_apply`` is the field-propagation API: callers expect the returned
+    multivector to satisfy ``versor_condition(result)``.  CGA point/null-vector
+    preservation belongs behind an explicit geometry API, not this runtime
+    manifold boundary.
     """
     arr = np.asarray(v, dtype=dtype)
-    if _is_null_vector(arr):
-        return arr.astype(dtype)
     try:
         return unitize_versor(arr).astype(dtype)
     except ValueError:
diff --git a/tests/test_versor_closure.py b/tests/test_versor_closure.py
index 10077775..423db1a4 100644
--- a/tests/test_versor_closure.py
+++ b/tests/test_versor_closure.py
@@ -103,6 +103,18 @@ def test_composition_closed():
     assert versor_condition(F3) < 1e-4
 
 
+def test_versor_apply_closes_null_like_field_results_for_runtime_contract():
+    identity = np.zeros(32, dtype=np.float32)
+    identity[0] = 1.0
+    null_like = np.zeros(32, dtype=np.float32)
+    null_like[1] = 1.0
+    null_like[5] = 1.0
+
+    result = versor_apply(identity, null_like)
+
+    assert versor_condition(result) < 1e-6
+
+
 def test_identity_versor():
     identity = np.zeros(32, dtype=np.float32)
     identity[0] = 1.0