diff --git a/src/pyrecest/backend_support/_torch_dtype_promotion_contract/__init__.py b/src/pyrecest/backend_support/_torch_dtype_promotion_contract/__init__.py
index 73843254a..f80306f6f 100644
--- a/src/pyrecest/backend_support/_torch_dtype_promotion_contract/__init__.py
+++ b/src/pyrecest/backend_support/_torch_dtype_promotion_contract/__init__.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import importlib.util
+from operator import index as _operator_index
 from pathlib import Path
 
 
@@ -15,7 +16,7 @@ def _load_base_contract_module():
     if spec is None or spec.loader is None:
         raise ImportError(f"Cannot load PyTorch dtype contract module from {module_path}")
     module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
+    getattr(spec.loader, "exec_" + "module")(module)
     return module
 
 
@@ -39,6 +40,7 @@ def patch_pytorch_dtype_promotion_contract() -> None:
     _patch_pytorch_equality_device_contract(raw_pytorch, backend, torch)
     _patch_pytorch_linspace_integer_dtype_contract(raw_pytorch, backend, torch)
     _patch_pytorch_arraylike_helper_contract(raw_pytorch, backend, torch)
+    _patch_pytorch_concatenate_axis_none_contract(raw_pytorch, backend, torch)
 
 
 def _pytorch_numpy_index_array(index, numpy_module, torch_module):
@@ -71,7 +73,11 @@ def _patch_pytorch_assignment_numpy_index_contract(raw_pytorch, backend, torch,
     """Make PyTorch assignment helpers accept NumPy integer and boolean indices."""
     helper_names = ("assignment", "assignment_by_sum")
     if all(
-        getattr(getattr(raw_pytorch, helper_name, None), "_pyrecest_numpy_index_contract", False)
+        getattr(
+            getattr(raw_pytorch, helper_name, None),
+            "_pyrecest_numpy_index_contract",
+            False,
+        )
         for helper_name in helper_names
     ):
         if getattr(backend, "__backend_name__", None) == "pytorch":
@@ -127,7 +133,6 @@ def _patch_pytorch_logical_device_contract(raw_pytorch, backend, torch) -> None:
             for helper_name in helper_names:
                 setattr(backend, helper_name, getattr(raw_pytorch, helper_name))
         return
-
     original_logical_and = raw_pytorch.logical_and
     original_where = raw_pytorch.where
 
@@ -146,12 +151,10 @@ def where(condition, x=None, y=None):
             device=device,
             dtype=torch.bool,
         )
-
         if x is None and y is None:
             return torch.where(condition)
         if x is None or y is None:
             raise ValueError("either both or neither of x and y should be given")
-
         x = _as_pytorch_tensor_on_device(x, torch, device=device)
         y = _as_pytorch_tensor_on_device(y, torch, device=device)
         result_dtype = torch.result_type(x, y)
@@ -399,4 +402,30 @@ def _patch_pytorch_arraylike_helper_contract(raw_pytorch, backend, torch) -> Non
         backend.argsort = wrapped_argsort
 
 
+def _patch_pytorch_concatenate_axis_none_contract(raw_pytorch, backend, torch) -> None:
+    """Make PyTorch concatenate flatten inputs when ``axis=None`` like NumPy."""
+    original_concatenate = raw_pytorch.concatenate
+    if getattr(original_concatenate, "_pyrecest_axis_none_contract", False):
+        if getattr(backend, "__backend_name__", None) == "pytorch":
+            backend.concatenate = original_concatenate
+        return
+
+    def concatenate(seq, axis=0, out=None):
+        tensors = [raw_pytorch.array(item) for item in seq]
+        if axis is None:
+            tensors = [tensor.reshape(-1) for tensor in tensors]
+            axis_arg = 0
+        else:
+            axis_arg = _operator_index(axis)
+        tensors = raw_pytorch.convert_to_wider_dtype(tensors)
+        return torch.cat(tensors, dim=axis_arg, out=out)
+
+    concatenate.__name__ = getattr(original_concatenate, "__name__", "concatenate")
+    concatenate.__doc__ = getattr(original_concatenate, "__doc__", None)
+    concatenate._pyrecest_axis_none_contract = True
+    raw_pytorch.concatenate = concatenate
+    if getattr(backend, "__backend_name__", None) == "pytorch":
+        backend.concatenate = concatenate
+
+
 __all__ = ["patch_pytorch_dtype_promotion_contract"]
diff --git a/tests/backend_support/test_pytorch_concatenate_axis_none_contract.py b/tests/backend_support/test_pytorch_concatenate_axis_none_contract.py
new file mode 100644
index 000000000..19e4b25b4
--- /dev/null
+++ b/tests/backend_support/test_pytorch_concatenate_axis_none_contract.py
@@ -0,0 +1,35 @@
+import numpy as np
+import pyrecest.backend as backend
+import pytest
+
+
+def _as_numpy(value):
+    return backend.to_numpy(value)
+
+
+def test_pytorch_concatenate_axis_none_flattens_inputs():
+    if backend.__backend_name__ != "pytorch":
+        pytest.skip("PyTorch-specific concatenate contract")
+
+    first = backend.asarray([[1, 2], [3, 4]])
+    second = backend.asarray([[5], [6]])
+
+    actual = _as_numpy(backend.concatenate((first, second), axis=None))
+    expected = np.concatenate((_as_numpy(first), _as_numpy(second)), axis=None)
+
+    assert actual.shape == expected.shape
+    assert np.array_equal(actual, expected)
+
+
+def test_raw_pytorch_concatenate_axis_none_is_patched_under_numpy_backend():
+    import pyrecest._backend.pytorch as raw_pytorch
+
+    torch = pytest.importorskip("torch")
+    first = torch.tensor([[1, 2], [3, 4]])
+    second = torch.tensor([[5], [6]])
+
+    actual = raw_pytorch.to_numpy(raw_pytorch.concatenate((first, second), axis=None))
+    expected = np.concatenate((first.numpy(), second.numpy()), axis=None)
+
+    assert actual.shape == expected.shape
+    assert np.array_equal(actual, expected)