diff --git a/test/dynamo_expected_failures/TestAutograd.test_gradcheck_check_no_differentiable_outputs b/test/dynamo_expected_failures/TestAutograd.test_gradcheck_check_no_differentiable_outputs
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/test/dynamo_expected_failures/TestExamplesCorrectnessCPU.test_maml_regression_mechanism_functional_call_cpu b/test/dynamo_expected_failures/TestExamplesCorrectnessCPU.test_maml_regression_mechanism_functional_call_cpu
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/test/dynamo_expected_failures/TestExamplesCorrectnessCPU.test_maml_regression_mechanism_make_functional_cpu b/test/dynamo_expected_failures/TestExamplesCorrectnessCPU.test_maml_regression_mechanism_make_functional_cpu
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/test/dynamo_expected_failures/TestQuantizePT2E.test_derived_qspec b/test/dynamo_expected_failures/TestQuantizePT2E.test_derived_qspec
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/test/dynamo_expected_failures/TestQuantizePT2EQAT_ConvBn1d.test_qat_conv_bn_bias_derived_qspec b/test/dynamo_expected_failures/TestQuantizePT2EQAT_ConvBn1d.test_qat_conv_bn_bias_derived_qspec
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/test/dynamo_expected_failures/TestQuantizePT2EQAT_ConvBn2d.test_qat_conv_bn_bias_derived_qspec b/test/dynamo_expected_failures/TestQuantizePT2EQAT_ConvBn2d.test_qat_conv_bn_bias_derived_qspec
deleted file mode 100644
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diff --git a/test/dynamo_expected_failures/TestTensorCreationCPU.test_cartesian_prod_cpu b/test/dynamo_expected_failures/TestTensorCreationCPU.test_cartesian_prod_cpu
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/torch/_refs/__init__.py b/torch/_refs/__init__.py
index 818c195fceb..fa571489bd7 100644
--- a/torch/_refs/__init__.py
+++ b/torch/_refs/__init__.py
@@ -6332,21 +6332,15 @@ def _infer_scalar_type(obj):
 
 # Analogous to recursive_store
 # xref: recursive_store in torch/csrc/utils/tensor_new.cpp
-def _recursive_build(sizes, dim, scalarType, obj):
-    ndim = len(sizes)
-    assert dim <= ndim
-    if dim == ndim:
+def _recursive_build(scalarType: torch.dtype, obj: TensorOrNumberLikeType):
+    if isinstance(obj, Tensor) and obj.ndim <= 1:
+        obj = obj.item()
+        # fall through into next case
+    if isinstance(obj, Number):
         return torch.scalar_tensor(obj, dtype=scalarType)
-    n = sizes[dim]
+
     seq = obj
-    seq_size = len(seq)
-    if seq_size != n:
-        raise ValueError(
-            f"expected sequence of length {n} at dim {dim} (got {seq_size})"
-        )
-    return torch.stack(
-        [_recursive_build(sizes, dim + 1, scalarType, item) for item in seq]
-    )
+    return torch.stack([_recursive_build(scalarType, item) for item in seq])
 
 
 # xref: internal_new_from_data in torch/csrc/utils/tensor_new.cpp
@@ -6383,7 +6377,6 @@ def _internal_new_from_data(
     # TODO: test for numpy input with PyArray_Check
 
     device = device_opt if device_opt is not None else options["device"]
-    sizes = _compute_sizes(data, scalar_type)
     inferred_scalar_type = _infer_scalar_type(data) if type_inference else scalar_type
 
     # NB: Don't need to avoid tracing, as we aren't going to do any manual
@@ -6398,7 +6391,7 @@ def _internal_new_from_data(
         # of a freshly allocated CPU tensor.  Here, we're going to do an
         # alternate, heinously slow implementation: turn each individual
         # scalar into a tensor, and then repeatedly cat them together
-        tensor = _recursive_build(sizes, 0, inferred_scalar_type, data)
+        tensor = _recursive_build(inferred_scalar_type, data)
 
         tensor = tensor.to(device, inferred_scalar_type, non_blocking=False, copy=False)