diff --git a/tests/unit/test_kv_merge_debug.py b/tests/unit/test_kv_merge_debug.py
new file mode 100644
index 00000000..71f977d0
--- /dev/null
+++ b/tests/unit/test_kv_merge_debug.py
@@ -0,0 +1,49 @@
+"""Quick test: KV merge only."""
+import torch
+import math
+import cutlass.cute as cute
+import cutlass.torch as ct
+import cuda.bindings.driver as cuda
+from dsv4.kernels.attention.fmha import FmhaKernel
+
+def reference_attention_with_lse(q, k, v, scale):
+    scores = torch.matmul(q.float(), k.float().T) * scale
+    max_s = scores.max(dim=-1, keepdim=True).values
+    exp_s = (scores - max_s).exp()
+    sum_s = exp_s.sum(dim=-1, keepdim=True)
+    p = exp_s / sum_s
+    o = torch.matmul(p, v.float())
+    lse = (scores - max_s).exp().sum(dim=-1).log() + max_s.squeeze(-1)
+    return o.to(torch.bfloat16), lse
+
+torch.manual_seed(42)
+m, s_k, hd = 128, 256, 64
+scale = 1.0 / math.sqrt(hd)
+
+q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
+v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
+
+ref_o, _ = reference_attention_with_lse(q[:, :, 0], k[:, :, 0], v, scale)
+
+# Single-segment kernel test first
+kernel = FmhaKernel(head_dim=hd, s_k=128, normalize=False)
+pv_n_tile = kernel.pv_n_tile
+stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
+
+v_tile = v[:, 0:pv_n_tile].contiguous().unsqueeze(-1)
+c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
+lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
+
+k_seg = k[:128, :, :].contiguous()
+mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
+mK = ct.from_dlpack(k_seg).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_seg))
+mV = ct.from_dlpack(v_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
+mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
+mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
+
+print(f"q shape: {q.shape}, k_seg shape: {k_seg.shape}, v_tile shape: {v_tile.shape}")
+compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
+print("Compile succeeded!")
+compiled(mQ, mK, mV, mC, stream, mLSE)
+print("Run succeeded!")