diff --git a/tests/fmha_v3_stage_c_example7.py b/tests/fmha_v3_stage_c_example7.py
index 22a0d4b9..7f5bb9c5 100644
--- a/tests/fmha_v3_stage_c_example7.py
+++ b/tests/fmha_v3_stage_c_example7.py
@@ -289,7 +289,7 @@ class FmhaV3StageCMulti:
             # Per-tile softmax loop.
             # Online softmax row_max/row_sum tracking is maintained, but the
             # in-place TMEM O rescale (which would multiply existing O by
-            # O rescale + final normalize setup: single set of correction_rescale tensors
+            # O corr setup: DISABLED to debug n=128 regression
             corr_tile_size = 16
             cO_corr = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
             tOcO_corr = pv_thr.partition_C(cO_corr)
@@ -311,6 +311,16 @@ class FmhaV3StageCMulti:
             tTMrO = cute.make_rmem_tensor(
                 (tTMEM_LOAD_OcO.shape, 128 // corr_tile_size), self.acc_dtype)
 
+            # DISABLED: O rescale (kt > 0)
+            # if kt > 0:
+            #     for ci in range(HEAD_DIM // corr_tile_size):
+            #         ...
+
+            # DISABLED: Final O normalize (1/row_sum)
+            # inv_row_sum = Float32(1.0) / row_sum
+            # for i in range(HEAD_DIM // corr_tile_size):
+            #     ...
+
             row_max = -Float32.inf
             row_sum = Float32(0.0)
             scale_log2 = Float32(self.scale_softmax_log2)
@@ -344,25 +354,7 @@ class FmhaV3StageCMulti:
                     acc_scale = Float32(0.0)
                 row_sum *= acc_scale
 
-                # O rescale: multiply existing O by acc_scale = exp2(old_max - new_max)
-                if kt > 0:
-                    for ci in range(HEAD_DIM // corr_tile_size):
-                        tTMrO_i_ = tTMrO[None, ci]
-                        tTMrO_i_layout = cute.composition(
-                            tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0])
-                        )
-                        tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
-                        tTMEM_LOAD_OtO_i = cute.make_tensor(
-                            tTMEM_LOAD_OtO.iterator + ci * corr_tile_size, tTMEM_LOAD_OtO.layout
-                        )
-                        tTMEM_STORE_OtO_i = cute.make_tensor(
-                            tTMEM_STORE_OtO.iterator + ci * corr_tile_size, tTMEM_STORE_OtO.layout
-                        )
-                        cute.copy(tiled_tmem_load_o, tTMEM_LOAD_OtO_i, tTMrO_i)
-                        for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
-                            tTMrO_i[j] = tTMrO_i[j] * acc_scale
-                        cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STORE_OtO_i)
-                    cute.arch.fence_view_async_tmem_store()
+                # O rescale: DISABLED (debugging n=128 regression)
 
                 # Pass 2: P = exp2((S - new_max) * log2), accumulate row_sum,
                 # store BF16 P through the FP32-backed register bridge.
@@ -404,29 +396,7 @@ class FmhaV3StageCMulti:
             # Wait for MMA's PV[N-1] to commit before reading O.
             final_o_bar.arrive_and_wait()
 
-            # === Final O normalization: 1/row_sum ===
-            # Reuses the same corr_tile_size + tiled_tmem_load_o/store_o from above.
-            inv_row_sum = Float32(1.0) / row_sum
-
-            for i in range(HEAD_DIM // corr_tile_size):
-                tTMrO_i_ = tTMrO[None, i]
-                tTMrO_i_layout = cute.composition(
-                    tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0])
-                )
-                tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
-                tTMEM_LOAD_OtO_i = cute.make_tensor(
-                    tTMEM_LOAD_OtO.iterator + i * corr_tile_size, tTMEM_LOAD_OtO.layout
-                )
-                tTMEM_STORE_OtO_i = cute.make_tensor(
-                    tTMEM_STORE_OtO.iterator + i * corr_tile_size, tTMEM_STORE_OtO.layout
-                )
-
-                cute.copy(tiled_tmem_load_o, tTMEM_LOAD_OtO_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(tiled_tmem_store_o, tTMrO_i, tTMEM_STORE_OtO_i)
-
-            cute.arch.fence_view_async_tmem_store()
+            # === Final O normalization: DISABLED (debugging) ===
 
             # Standard epilogue: TMEM → SMEM → GMEM via TMA store.
             # O in TMEM is now scaled by 1/row_sum.