diff --git a/paddle/phi/ops/yaml/python_api_info.yaml b/paddle/phi/ops/yaml/python_api_info.yaml
index c6619d0fb96e41..f3530e0b29c98e 100644
--- a/paddle/phi/ops/yaml/python_api_info.yaml
+++ b/paddle/phi/ops/yaml/python_api_info.yaml
@@ -461,7 +461,7 @@
   name : [paddle.poisson]
   args_alias :
     use_default_mapping : True
-
+   
 - op : nextafter
   name : [paddle.nextafter, paddle.Tensor.nextafter]
   args_alias :
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
index 119ff9d112cb4e..2eff7fd0d8380f 100644
--- a/python/paddle/tensor/math.py
+++ b/python/paddle/tensor/math.py
@@ -4249,14 +4249,26 @@ def multigammaln_(x: Tensor, p: int, name: str | None = None) -> Tensor:
     return x
 
 
-def neg(x: Tensor, name: str | None = None) -> Tensor:
+@param_one_alias(["x", "input"])
+def neg(
+    x: Tensor,
+    name: str | None = None,
+    *,
+    out: Tensor | None = None
+) -> Tensor:
     """
     This function computes the negative of the Tensor elementwisely.
 
     Args:
-        x (Tensor): Input of neg operator, an N-D Tensor, with data type bfloat16, float16, float32, float64, int8, int16, int32,
-            int64, uint8, complex64, complex128.
-        name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+        x (Tensor): Input of neg operator, an N-D Tensor, with data type bfloat16,
+            float16, float32, float64, int8, int16, int32, int64, uint8,
+            complex64, complex128.
+            Alias: ``input``.
+        name (str|None, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Keyword args:
+        out (Tensor|None, optional): The output tensor. Default: None.
 
     Returns:
         out (Tensor): The negative of input Tensor. The shape and data type are the same with input Tensor.
@@ -4265,7 +4277,6 @@ def neg(x: Tensor, name: str | None = None) -> Tensor:
         .. code-block:: pycon
 
             >>> import paddle
-
             >>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
             >>> out = paddle.neg(x)
             >>> out
@@ -4273,10 +4284,21 @@ def neg(x: Tensor, name: str | None = None) -> Tensor:
             [ 0.40000001,  0.20000000, -0.10000000, -0.30000001])
     """
 
-    return scale(
-        x, scale=-1.0, bias=0.0, bias_after_scale=True, act=None, name=name
+    ret = scale(
+        x,
+        scale=-1.0,
+        bias=0.0,
+        bias_after_scale=True,
+        act=None,
+        name=name,
     )
 
+    if out is not None:
+        paddle.assign(ret, out)
+        return out
+
+    return ret
+
 
 @inplace_apis_in_dygraph_only
 def neg_(x: Tensor, name: str | None = None) -> Tensor:
@@ -6899,4 +6921,4 @@ def cartesian_prod(x: Sequence[Tensor], name: str | None = None) -> Tensor:
         return x[0]
 
     coordinates = paddle.stack(paddle.meshgrid(x), axis=-1)
-    return paddle.reshape(coordinates, [-1, len(x)])
+    return paddle.reshape(coordinates, [-1, len(x)])
\ No newline at end of file
diff --git a/test/legacy_test/test_api_compatibility.py b/test/legacy_test/test_api_compatibility.py
index 6d25c9010f5008..f0293ddc3b2de5 100644
--- a/test/legacy_test/test_api_compatibility.py
+++ b/test/legacy_test/test_api_compatibility.py
@@ -3275,203 +3275,40 @@ def test_static_Compatibility(self):
                 np.testing.assert_allclose(fetches[i + 1], ref_out[1])
 
 
-# Test hstack compatibility
-class TestHstackAPI(unittest.TestCase):
+# Test neg compatibility
+class TestNeg(unittest.TestCase):
     def setUp(self):
         np.random.seed(2025)
-        self.shapes = [[2, 3], [2, 4]]
+        self.shape = [4]
         self.dtype = 'float32'
-        self.inputs = []
-        for shape in self.shapes:
-            self.inputs.append(np.random.rand(*shape).astype(self.dtype))
-
-    def test_dygraph_Compatibility(self):
-        paddle.disable_static()
-        tensors = [paddle.to_tensor(inp) for inp in self.inputs]
-
-        out1 = paddle.hstack(tuple(tensors))
-        out2 = paddle.hstack(tensors)
-        out3 = paddle.hstack(x=tuple(tensors))
-        out4 = paddle.hstack(x=tensors)
-        out5 = paddle.hstack(tensors=tuple(tensors))
-        out6 = paddle.hstack(tensors=tensors)
-
-        ref_out = np.hstack(tuple(inp for inp in self.inputs))
-        for out in [out1, out2, out3, out4, out5, out6]:
-            np.testing.assert_allclose(
-                ref_out, out.numpy(), rtol=1e-5, atol=1e-8
-            )
-
-        paddle.enable_static()
-
-    def test_static_Compatibility(self):
-        paddle.enable_static()
-        main = paddle.static.Program()
-        startup = paddle.static.Program()
-        with paddle.base.program_guard(main, startup):
-            static_tensors = []
-            feed_dict = {}
-            for i, (shape, inp) in enumerate(zip(self.shapes, self.inputs)):
-                static_tensor = paddle.static.data(
-                    name=f"x{i}", shape=shape, dtype=self.dtype
-                )
-                static_tensors.append(static_tensor)
-                feed_dict[f"x{i}"] = inp
-
-            out1 = paddle.hstack(tuple(static_tensors))
-            out2 = paddle.hstack(static_tensors)
-            out3 = paddle.hstack(x=tuple(static_tensors))
-            out4 = paddle.hstack(tensors=tuple(static_tensors))
-
-            exe = paddle.base.Executor(paddle.CPUPlace())
-            fetches = exe.run(
-                main, feed=feed_dict, fetch_list=[out1, out2, out3, out4]
-            )
-            ref_out = np.hstack(tuple(inp for inp in self.inputs))
-            for out in fetches:
-                np.testing.assert_allclose(out, ref_out, rtol=1e-5, atol=1e-8)
-
-
-class TestVstackAPI(unittest.TestCase):
-    def setUp(self):
-        np.random.seed(2025)
-        self.shapes = [[2, 3], [3, 3]]
-        self.dtype = 'float32'
-        self.inputs = []
-        for shape in self.shapes:
-            self.inputs.append(np.random.rand(*shape).astype(self.dtype))
-
-    def test_dygraph_Compatibility(self):
-        paddle.disable_static()
-        tensors = [paddle.to_tensor(inp) for inp in self.inputs]
-        out1 = paddle.vstack(tuple(tensors))
-        out2 = paddle.vstack(tensors)
-        out3 = paddle.vstack(x=tuple(tensors))
-        out4 = paddle.vstack(x=tensors)
-        out5 = paddle.vstack(tensors=tuple(tensors))
-        out6 = paddle.vstack(tensors=tensors)
-
-        ref_out = np.vstack(tuple(inp for inp in self.inputs))
-        for out in [out1, out2, out3, out4, out5, out6]:
-            np.testing.assert_allclose(
-                ref_out, out.numpy(), rtol=1e-5, atol=1e-8
-            )
-
-        paddle.enable_static()
-
-    def test_static_Compatibility(self):
-        paddle.enable_static()
-        main = paddle.static.Program()
-        startup = paddle.static.Program()
-        with paddle.base.program_guard(main, startup):
-            static_tensors = []
-            feed_dict = {}
-            for i, (shape, inp) in enumerate(zip(self.shapes, self.inputs)):
-                static_tensor = paddle.static.data(
-                    name=f"x{i}", shape=shape, dtype=self.dtype
-                )
-                static_tensors.append(static_tensor)
-                feed_dict[f"x{i}"] = inp
-
-            out1 = paddle.vstack(tuple(static_tensors))
-            out2 = paddle.vstack(static_tensors)
-            out3 = paddle.vstack(x=tuple(static_tensors))
-            out4 = paddle.vstack(tensors=tuple(static_tensors))
-
-            exe = paddle.base.Executor(paddle.CPUPlace())
-            fetches = exe.run(
-                main, feed=feed_dict, fetch_list=[out1, out2, out3, out4]
-            )
-
-            ref_out = np.vstack(tuple(inp for inp in self.inputs))
-            for out in fetches:
-                np.testing.assert_allclose(out, ref_out, rtol=1e-5, atol=1e-8)
-
+        self.init_data()
 
-# Test dstack compatibility
-class TestDstackAPI(unittest.TestCase):
-    def setUp(self):
-        np.random.seed(2025)
-        self.shapes = [[2, 3, 4], [2, 3, 4]]
-        self.dtype = 'float32'
-        self.inputs = []
-        for shape in self.shapes:
-            self.inputs.append(np.random.rand(*shape).astype(self.dtype))
+    def init_data(self):
+        self.np_x = np.random.rand(*self.shape).astype(self.dtype)
 
     def test_dygraph_Compatibility(self):
         paddle.disable_static()
-        tensors = [paddle.to_tensor(inp) for inp in self.inputs]
-        out1 = paddle.dstack(tuple(tensors))
-        out2 = paddle.dstack(tensors)
-        out3 = paddle.dstack(x=tuple(tensors))
-        out4 = paddle.dstack(x=tensors)
-        out5 = paddle.dstack(tensors=tuple(tensors))
-        out6 = paddle.dstack(tensors=tensors)
-
-        # Verify all outputs
-        ref_out = np.dstack(tuple(inp for inp in self.inputs))
-        for out in [out1, out2, out3, out4, out5, out6]:
-            np.testing.assert_allclose(
-                ref_out, out.numpy(), rtol=1e-5, atol=1e-8
-            )
-
-        paddle.enable_static()
-
-    def test_static_Compatibility(self):
-        paddle.enable_static()
-        main = paddle.static.Program()
-        startup = paddle.static.Program()
-        with paddle.base.program_guard(main, startup):
-            static_tensors = []
-            feed_dict = {}
-            for i, (shape, inp) in enumerate(zip(self.shapes, self.inputs)):
-                static_tensor = paddle.static.data(
-                    name=f"x{i}", shape=shape, dtype=self.dtype
-                )
-                static_tensors.append(static_tensor)
-                feed_dict[f"x{i}"] = inp
-
-            out1 = paddle.dstack(tuple(static_tensors))
-            out2 = paddle.dstack(static_tensors)
-            out3 = paddle.dstack(x=tuple(static_tensors))
-            out4 = paddle.dstack(tensors=tuple(static_tensors))
-
-            exe = paddle.base.Executor(paddle.CPUPlace())
-            fetches = exe.run(
-                main, feed=feed_dict, fetch_list=[out1, out2, out3, out4]
-            )
-
-            ref_out = np.dstack(tuple(inp for inp in self.inputs))
-            for out in fetches:
-                np.testing.assert_allclose(out, ref_out, rtol=1e-5, atol=1e-8)
-
-
-# Test column_stack compatibility
-class TestColumnStackAPI(unittest.TestCase):
-    def setUp(self):
-        np.random.seed(2025)
-        self.shapes = [[3, 2], [3, 3]]
-        self.dtype = 'float32'
-        self.inputs = []
-        for shape in self.shapes:
-            self.inputs.append(np.random.rand(*shape).astype(self.dtype))
+        x = paddle.to_tensor(self.np_x)
 
-    def test_dygraph_Compatibility(self):
-        paddle.disable_static()
-        tensors = [paddle.to_tensor(inp) for inp in self.inputs]
-        out1 = paddle.column_stack(tuple(tensors))
-        out2 = paddle.column_stack(tensors)
-        out3 = paddle.column_stack(x=tuple(tensors))
-        out4 = paddle.column_stack(x=tensors)
-        out5 = paddle.column_stack(tensors=tuple(tensors))
-        out6 = paddle.column_stack(tensors=tensors)
+        # 1. Paddle Positional arguments
+        out1 = paddle.neg(x)
+        # 2. Paddle keyword arguments
+        out2 = paddle.neg(x=x)
+        # 3. PyTorch keyword arguments (alias)
+        out3 = paddle.neg(input=x)
+        # 4. Mixed arguments
+        out4 = paddle.neg(x, name=None)
+        # 5. out parameter test
+        out5 = paddle.empty_like(x)
+        out6 = paddle.neg(x=x, out=out5)
+        # 6. Tensor method - args
+        out7 = x.neg()
+        # 7. Tensor method - kwargs
+        out8 = x.neg(name=None)
 
         # Verify all outputs
-        ref_out = np.column_stack(tuple(inp for inp in self.inputs))
-        for out in [out1, out2, out3, out4, out5, out6]:
-            np.testing.assert_allclose(
-                ref_out, out.numpy(), rtol=1e-5, atol=1e-8
-            )
+        for out in [out1, out2, out3, out4, out5, out6, out7, out8]:
+            np.testing.assert_allclose(out.numpy(), -self.np_x, rtol=1e-6)
 
         paddle.enable_static()
 
@@ -3479,91 +3316,27 @@ def test_static_Compatibility(self):
         paddle.enable_static()
         main = paddle.static.Program()
         startup = paddle.static.Program()
-        with paddle.base.program_guard(main, startup):
-            static_tensors = []
-            feed_dict = {}
-            for i, (shape, inp) in enumerate(zip(self.shapes, self.inputs)):
-                static_tensor = paddle.static.data(
-                    name=f"x{i}", shape=shape, dtype=self.dtype
-                )
-                static_tensors.append(static_tensor)
-                feed_dict[f"x{i}"] = inp
-
-            out1 = paddle.column_stack(tuple(static_tensors))
-            out2 = paddle.column_stack(static_tensors)
-            out3 = paddle.column_stack(x=tuple(static_tensors))
-            out4 = paddle.column_stack(tensors=tuple(static_tensors))
-
-            exe = paddle.base.Executor(paddle.CPUPlace())
-            fetches = exe.run(
-                main, feed=feed_dict, fetch_list=[out1, out2, out3, out4]
-            )
-
-            ref_out = np.column_stack(tuple(inp for inp in self.inputs))
-            for out in fetches:
-                np.testing.assert_allclose(out, ref_out, rtol=1e-5, atol=1e-8)
-
-
-# Test row_stack compatibility
-class TestRowStackAPI(unittest.TestCase):
-    def setUp(self):
-        np.random.seed(2025)
-        self.shapes = [[2, 3], [4, 3]]
-        self.dtype = 'float32'
-        self.inputs = []
-        for shape in self.shapes:
-            self.inputs.append(np.random.rand(*shape).astype(self.dtype))
-
-    def test_dygraph_Compatibility(self):
-        paddle.disable_static()
-        tensors = [paddle.to_tensor(inp) for inp in self.inputs]
-        out1 = paddle.row_stack(tuple(tensors))
-        out2 = paddle.row_stack(tensors)
-        out3 = paddle.row_stack(x=tuple(tensors))
-
-        out4 = paddle.row_stack(x=tensors)
-
-        out5 = paddle.row_stack(tensors=tuple(tensors))
-
-        out6 = paddle.row_stack(tensors=tensors)
-
-        # Verify all outputs
-        ref_out = np.vstack(tuple(inp for inp in self.inputs))
-        for out in [out1, out2, out3, out4, out5, out6]:
-            np.testing.assert_allclose(
-                ref_out, out.numpy(), rtol=1e-5, atol=1e-8
-            )
+        with paddle.static.program_guard(main, startup):
+            x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
 
-        paddle.enable_static()
+            # 1. Paddle Positional arguments
+            out1 = paddle.neg(x)
+            # 2. Paddle keyword arguments
+            out2 = paddle.neg(x=x)
+            # 3. PyTorch keyword arguments (alias)
+            out3 = paddle.neg(input=x)
 
-    def test_static_Compatibility(self):
-        paddle.enable_static()
-        main = paddle.static.Program()
-        startup = paddle.static.Program()
-        with paddle.base.program_guard(main, startup):
-            static_tensors = []
-            feed_dict = {}
-            for i, (shape, inp) in enumerate(zip(self.shapes, self.inputs)):
-                static_tensor = paddle.static.data(
-                    name=f"x{i}", shape=shape, dtype=self.dtype
-                )
-                static_tensors.append(static_tensor)
-                feed_dict[f"x{i}"] = inp
-
-            out1 = paddle.row_stack(tuple(static_tensors))
-            out2 = paddle.row_stack(static_tensors)
-            out3 = paddle.row_stack(x=tuple(static_tensors))
-            out4 = paddle.row_stack(tensors=tuple(static_tensors))
-
-            exe = paddle.base.Executor(paddle.CPUPlace())
+            exe = paddle.static.Executor()
             fetches = exe.run(
-                main, feed=feed_dict, fetch_list=[out1, out2, out3, out4]
+                main,
+                feed={"x": self.np_x},
+                fetch_list=[out1, out2, out3],
             )
 
-            ref_out = np.vstack(tuple(inp for inp in self.inputs))
+            # Verify all outputs
             for out in fetches:
-                np.testing.assert_allclose(out, ref_out, rtol=1e-5, atol=1e-8)
+                np.testing.assert_allclose(out, -self.np_x, rtol=1e-6)
 
 
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()
\ No newline at end of file