diff --git a/tests/fmha_v3_real_softmax.py b/tests/fmha_v3_real_softmax.py
index 228df9e2..de4a430d 100644
--- a/tests/fmha_v3_real_softmax.py
+++ b/tests/fmha_v3_real_softmax.py
@@ -226,23 +226,7 @@ class FmhaV3RealSoftmax:
             tScP = cute.make_tensor(tScS.iterator, tScP_layout)
             tTMEM_STOREcP = thr_store.partition_S(tScP)
 
-            # O normalize setup
-            cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
-            tOcO = pv_thr.partition_C(cO)
-            corr_tile_size = 16
-            tOtO_i_layout = cute.composition(tOtO.layout, cute.make_layout((128, corr_tile_size)))
-            tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
-            tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
-            tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
-            tmem_load_o_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.acc_dtype)
-            tmem_store_o_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.acc_dtype)
-            tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
-            tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
-            thr_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
-            thr_store_o = tiled_tmem_store_o.get_slice(sfw_idx)
-            tTMEM_LOAD_OtO = thr_load_o.partition_S(tOtO_i)
-            tTMEM_LOAD_OcO = thr_load_o.partition_D(tOcO_i)
-            tTMEM_STORE_OtO = thr_store_o.partition_D(tOtO_i)
+            # TODO: O normalize setup (add when O rescale is ready)
 
             row_max = -Float32.inf
             row_sum = Float32(0.0)
@@ -276,16 +260,7 @@ class FmhaV3RealSoftmax:
                     acc_scale = Float32(0.0)
                 row_sum *= acc_scale
 
-                # O rescale in TMEM (only for kt > 0)
-                if kt > 0:
-                    # Read O, multiply by acc_scale, write back
-                    tTMEM_LOAD_OrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
-                    cute.copy(tiled_tmem_load_o, tTMEM_LOAD_OtO, tTMEM_LOAD_OrO)
-                    cute.arch.fence_view_async_tmem_load()
-                    for j in range(cute.size(tTMEM_LOAD_OrO)):
-                        tTMEM_LOAD_OrO[None, j] = tTMEM_LOAD_OrO[None, j] * acc_scale
-                    cute.copy(tiled_tmem_store_o, tTMEM_LOAD_OrO, tTMEM_STORE_OtO)
-                    cute.arch.fence_view_async_tmem_store()
+                # TODO: O rescale in TMEM (skip for now, test softmax + P only)
 
                 # Pass 2: P = exp2(S * scale_log2 - row_max), accumulate row_sum
                 rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
@@ -306,15 +281,10 @@ class FmhaV3RealSoftmax:
                 softmax_done_bar.arrive()
 
             # Final O normalization: O = O / row_sum
-            if row_sum != Float32(0.0):
-                inv_row_sum = Float32(1.0) / row_sum
-                tTMEM_LOAD_OrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
-                cute.copy(tiled_tmem_load_o, tTMEM_LOAD_OtO, tTMEM_LOAD_OrO)
-                cute.arch.fence_view_async_tmem_load()
-                for j in range(cute.size(tTMEM_LOAD_OrO)):
-                    tTMEM_LOAD_OrO[None, j] = tTMEM_LOAD_OrO[None, j] * inv_row_sum
-                cute.copy(tiled_tmem_store_o, tTMEM_LOAD_OrO, tTMEM_STORE_OtO)
-                cute.arch.fence_view_async_tmem_store()
+            # TODO: enable after basic softmax works
+            # if row_sum != Float32(0.0):
+            #     inv_row_sum = Float32(1.0) / row_sum
+            #     ...
 
             # Epilogue: TMEM -> SMEM -> GMEM via TMA store
             tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)