diff --git a/tests/unit/test_fmha_v3_stage_c_full.py b/tests/unit/test_fmha_v3_stage_c_full.py
index d1d356b9..d84c773e 100644
--- a/tests/unit/test_fmha_v3_stage_c_full.py
+++ b/tests/unit/test_fmha_v3_stage_c_full.py
@@ -327,35 +327,37 @@ class FmhaV3StageC:
 def test():
     torch.manual_seed(42)
     for n in [128]:
-        m, hd = 128, HEAD_DIM
-        q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
-        k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
-        v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda')
-        v_kernel = v.unsqueeze(-1)
-        c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
-        # Reference: softmax(Q @ K^T / sqrt(d)) @ V
-        qf = q[:,:,0].float(); kf = k[:,:,0].float()
-        scale = 1.0 / math.sqrt(hd)
-        attn = qf @ kf.T * scale
-        attn = torch.softmax(attn, dim=-1)
-        ref = attn @ v.float()
-        mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
-        mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
-        mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
-        mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
-        stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
-        kernel = FmhaV3StageC()
-        print(f'n={n}: Compiling...', flush=True)
-        compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
-        print(f'n={n}: tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
-        print(f'n={n}: Running...', flush=True)
-        compiled(mQ, mK, mV, mC, stream)
-        torch.cuda.synchronize()
-        out = c[:,:,0].float()
-        cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
-        print(f'FMHA Stage-C n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
-        if cos < 0.99:
-            print(f'  out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
+        for seed in [42, 123, 999]:
+            torch.manual_seed(seed)
+            m, hd = 128, HEAD_DIM
+            q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+            k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
+            v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda')
+            v_kernel = v.unsqueeze(-1)
+            c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+            qf = q[:,:,0].float(); kf = k[:,:,0].float()
+            scale = 1.0 / math.sqrt(hd)
+            attn = qf @ kf.T * scale
+            attn = torch.softmax(attn, dim=-1)
+            ref = attn @ v.float()
+            mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
+            mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
+            mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
+            mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
+            stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
+            kernel = FmhaV3StageC()
+            if seed == 42:
+                print(f'seed={seed}: Compiling...', flush=True)
+            compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
+            if seed == 42:
+                print(f'tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
+            compiled(mQ, mK, mV, mC, stream)
+            torch.cuda.synchronize()
+            out = c[:,:,0].float()
+            cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
+            print(f'FMHA Stage-C n={n} seed={seed}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
+            if cos < 0.99:
+                print(f'  out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
 
 if __name__ == '__main__':
     test()