diff --git a/tests/test_fmha_v3_softmax.py b/tests/test_fmha_v3_softmax.py
index cbff5a44..166dc904 100644
--- a/tests/test_fmha_v3_softmax.py
+++ b/tests/test_fmha_v3_softmax.py
@@ -63,6 +63,7 @@ class FmhaV3Softmax:
         q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
         self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
         self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
+        self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
 
     @cute.jit
     def __call__(self, q, k, v, c, stream):
@@ -348,16 +349,34 @@ class FmhaV3Softmax:
 
                 row_sum = row_sum + tile_sum
 
-            # --- C9: Final normalization + epilogue TMA store ---
+            # --- C9: Final normalization via O TMEM rescale ---
+            # After all KV tiles, O = sum(P_i @ V_i) but unnormalized.
+            # Load O, multiply by 1/row_sum, store O. Then use identity epilogue.
+            inv_row_sum = cutlass.Float32(1.0) / row_sum
+
+            tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
+            for i in range(o_col_tiles):
+                tTMrO_i_ = tTMrO_final[None, i]
+                tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
+                tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
+                tTMEM_LOADtO_i = cute.make_tensor(
+                    tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
+                tTMEM_STOREtO_i = cute.make_tensor(
+                    tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
+                cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
+                for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
+                    tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
+                cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
+            cute.arch.fence_view_async_tmem_store()
+
+            # Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
             tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
             acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
             c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
             c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
-            # C9: Normalize by 1/row_sum
-            inv_row_sum = cutlass.Float32(1.0) / row_sum
             acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
                 self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
-                const_expr(lambda x, s=inv_row_sum: x * s),
+                const_expr(lambda x: x),
                 (0,0,0), acc_cons_st, acc_pipe, c_pipe)
             c_pipe.producer_tail()
             tmem.relinquish_alloc_permit()