diff --git a/tests/unit/test_kv_merge_debug.py b/tests/unit/test_kv_merge_debug.py
deleted file mode 100644
index 71f977d0..00000000
--- a/tests/unit/test_kv_merge_debug.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""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!")