diff --git a/mlstm_kernels/triton/chunkwise/limit_chunk/fw_kernel_recurrent.py b/mlstm_kernels/triton/chunkwise/limit_chunk/fw_kernel_recurrent.py
index 3829ece..bf0bba6 100644
--- a/mlstm_kernels/triton/chunkwise/limit_chunk/fw_kernel_recurrent.py
+++ b/mlstm_kernels/triton/chunkwise/limit_chunk/fw_kernel_recurrent.py
@@ -177,7 +177,7 @@ def mlstm_chunkwise__recurrent_fw_C_kernel(
         scaG_k_val = vecB_last_k_val
 
         # scaM_inter_k update
-        scaAmax_k_val, _ = tl.max(vecA_k_val)
+        scaAmax_k_val = tl.max(vecA_k_val)
         scaMinter_next_val = tl.maximum(scaG_k_val + scaMinter_k_val, scaAmax_k_val)
 
         # load matK_k, matV_k
diff --git a/mlstm_kernels/triton/chunkwise/limit_chunk/test_fw_kernel_recurrent.py b/mlstm_kernels/triton/chunkwise/limit_chunk/test_fw_kernel_recurrent.py
new file mode 100644
index 0000000..f066d51
--- /dev/null
+++ b/mlstm_kernels/triton/chunkwise/limit_chunk/test_fw_kernel_recurrent.py
@@ -0,0 +1,74 @@
+import unittest
+import torch
+import triton
+import triton.language as tl
+
+# Import the raw Triton kernel from your main source file.
+from fw_kernel_recurrent import mlstm_chunkwise__recurrent_fw_C_kernel
+
+# Define the Python launcher function locally in the test script.
+def launch_recurrent_fw_c_kernel(matK, matV, vecB, vecI, matC_initial, vecN_initial, scaMinter_initial, use_initial_state=False):
+    # This launcher is the same as before. It prepares arguments and launches the imported kernel.
+    B, NH, S, DHQK = matK.shape
+    _, _, _, DHHV = matV.shape
+    _, _, NC, L = vecB.shape
+    siz_b_DHQK, siz_b_DHHV = 32, 64
+    matC_states = torch.zeros(B, NH, (NC + 1) * DHQK, DHHV, device=matK.device, dtype=torch.float32)
+    vecN_states = torch.zeros(B, NH, (NC + 1) * DHQK, device=matK.device, dtype=torch.float32)
+    scaMinter_states = torch.zeros(B, NH, (NC + 1), device=matK.device, dtype=torch.float32)
+    grid = (triton.cdiv(DHQK, siz_b_DHQK), triton.cdiv(DHHV, siz_b_DHHV), B * NH)
+    TORCH_DTYPE_TO_TRITON_DTYPE = {
+        torch.float32: tl.float32, torch.float16: tl.float16, torch.bfloat16: tl.bfloat16,
+    }
+    triton_dtype = TORCH_DTYPE_TO_TRITON_DTYPE[matV.dtype]
+    mlstm_chunkwise__recurrent_fw_C_kernel[grid](
+        matK, matV, vecB, vecI, matC_initial, vecN_initial, scaMinter_initial,
+        matC_states, vecN_states, scaMinter_states,
+        matK.stride(0), matK.stride(2), matK.stride(3), matV.stride(0), matV.stride(2), matV.stride(3),
+        vecB.stride(0), vecB.stride(2), vecB.stride(3), matC_states.stride(0), matC_states.stride(2),
+        matC_states.stride(3), vecN_states.stride(0), vecN_states.stride(2), scaMinter_states.stride(0),
+        scaMinter_states.stride(2), matC_initial.stride(0), matC_initial.stride(2), matC_initial.stride(3),
+        vecN_initial.stride(0), vecN_initial.stride(2), scaMinter_initial.stride(0),
+        B=B, NH=NH, S=S, DHQK=DHQK, DHHV=DHHV, NC=NC, L=L,
+        siz_b_DHQK=siz_b_DHQK, siz_b_DHHV=siz_b_DHHV,
+        USE_INITIAL_STATE=use_initial_state, DTYPE=triton_dtype
+    )
+    return matC_states, vecN_states, scaMinter_states
+
+class TestMlstmRecurrentFwKernel(unittest.TestCase):
+
+    def test_kernel_runs_and_is_numerically_correct(self):
+        """
+        Tests that the kernel compiles and runs with the correct syntax,
+        and that the value from tl.max is numerically correct.
+        This test will PASS with the correct code and FAIL with the incorrect code.
+        """
+        # Arrange: Set up specific, known inputs
+        B, NH, S, DHQK, DHHV, NC, L = 1, 1, 16, 64, 128, 1, 16
+        device = 'cuda'
+        dtype = torch.float32
+        known_max_val = 100.0
+        vecI_input = torch.ones(B, NH, NC, L, device=device, dtype=dtype)
+        vecI_input[0, 0, 0, 7] = known_max_val # vecA_k_val will have a max of 100.0
+        vecB_input = torch.zeros(B, NH, NC, L, device=device, dtype=dtype) # Zeros so it doesn't affect vecA
+        matK = torch.zeros(B, NH, S, DHQK, device=device, dtype=dtype)
+        matV = torch.zeros(B, NH, S, DHHV, device=device, dtype=dtype)
+        matC_initial = torch.zeros(B, NH, DHQK, DHHV, device=device, dtype=dtype)
+        vecN_initial = torch.zeros(B, NH, DHQK, device=device, dtype=dtype)
+        scaMinter_initial = torch.zeros(B, NH, device=device, dtype=dtype)
+
+        # Act & Assert
+        try:
+            _, _, scaMinter_states = launch_recurrent_fw_c_kernel(
+                matK, matV, vecB_input, vecI_input, matC_initial, vecN_initial, scaMinter_initial, use_initial_state=True
+            )
+            # The logic inside the kernel ensures the final state is equal to the max value found
+            final_minter_state = scaMinter_states[0, 0, 1].item()
+            self.assertAlmostEqual(final_minter_state, known_max_val, places=4,
+                                 msg="Kernel ran, but output is numerically incorrect.")
+        except Exception as e:
+            # If any exception occurs (like the CompilationError), fail the test.
+            self.fail(f"Kernel launch failed unexpectedly with exception: {e}")
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file