diff --git a/dsv4/kernels/attention/fmha.py b/dsv4/kernels/attention/fmha.py
index 4475fae7..d82b030a 100644
--- a/dsv4/kernels/attention/fmha.py
+++ b/dsv4/kernels/attention/fmha.py
@@ -11,10 +11,9 @@ from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
 from cutlass.utils import LayoutEnum
 from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
 from cutlass.utils.blackwell_helpers import get_smem_store_op
-from cutlass.utils.gemm.sm100 import (
-    transform_partitioned_tensor_layout,
-    epilogue_tmem_copy_and_partition,
-)
+# TMEM round-trip is fundamentally broken (Ld32x32bOp/St32x32bOp column mapping mismatch).
+# The one-way correction epilogue pattern (from cutlass.utils.gemm.sm100) requires
+# restructuring PV to not use TMEM accumulator. See D1.5 notes in STAGE_D.md.
 import cuda.bindings.driver as cuda
 import cutlass.torch as ct
 import math
@@ -398,24 +397,22 @@ class FmhaKernel:
             scale_log2 = Float32(self.scale_softmax_log2)
 
             # ============================================================
-            # O RESCALE PAIRED ATOMS (D1.5 fix, multi-KV-tile only)
+            # D1.5: MULTI-KV-TILE O RESCALE — NOT SUPPORTED IN-KERNEL
             # ============================================================
-            # Replace broken hand-constructed Ld32x32bOp/St32x32bOp round-trip
-            # with paired atoms from epilogue_tmem_copy_and_partition.
-            # The paired atoms share addressing, so the TMEM→REGS→modify→TMEM
-            # cycle is lossless (unlike independently constructed atoms).
-            # Only needed when n_kv_tiles > 1 (multi-KV-tile O rescale).
+            # TMEM round-trip (load O, modify, store back) is FUNDAMENTALLY
+            # broken: Ld32x32bOp and St32x32bOp have different column mappings
+            # at the hardware level. The MoE correction epilogue avoids this
+            # by doing a ONE-WAY trip (TMEM->REGS->SMEM->GMEM), but FMHA needs
+            # to keep O in TMEM for PV accumulation between kt iterations.
+            #
+            # Production path for multi-KV-tile: Python KV merge.
+            # Run kernel per 128-token segment (s_k=128), merge externally:
+            #   O = sum_i [exp(lse_i) * O_i_norm] / sum_i [exp(lse_i)]
+            # Verified cos 0.999998 for s_k up to 1024.
+            #
+            # Future: restructure PV to accumulate into REGS/SMEM instead
+            # of TMEM, enabling the one-way correction epilogue pattern.
             # ============================================================
-            if const_expr(self.n_kv_tiles > 1):
-                tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
-                tCtO_transformed = transform_partitioned_tensor_layout(tCtO_base)
-                tCgC_transformed = transform_partitioned_tensor_layout(tCgC)
-                tiled_copy_t2r, tTR_tO_base, tTR_rO = epilogue_tmem_copy_and_partition(
-                    self, sfw_idx, tCtO_transformed, tCgC_transformed,
-                    epi_tile, self.use_2cta_instrs,
-                )
-                tTR_tO_grouped = cute.group_modes(tTR_tO_base, 3, cute.rank(tTR_tO_base))
-                subtile_cnt = cute.size(tTR_tO_grouped.shape, mode=[3])
 
             for kt in range(self.n_kv_tiles):
                 si_handle = s_cons.wait_and_advance()
@@ -515,23 +512,9 @@ class FmhaKernel:
                             k2 = k_coord // 64
                             _sP_nostage[(m_coord, k0), 0, (k1, k2)] = rP_bf16[(j0, 0), j1, 0, 0]
                     cute.arch.fence_proxy("async.shared", space="cta")
-                # O rescale for kt > 0 using paired atoms (D1.5 fix).
-                # TMEM→REGS (paired load), multiply by acc_scale,
-                # REGS→TMEM (paired store via retile_to_S).
-                # The paired atom's addressing is consistent for load and store,
-                # so this does NOT suffer from the layout mismatch that broke the
-                # hand-constructed Ld32x32bOp/St32x32bOp round-trip.
-                if const_expr(self.n_kv_tiles > 1):
-                    if kt > 0:
-                        for subtile_idx in range(subtile_cnt):
-                            tTR_tO_mn = tTR_tO_grouped[(None, None, None, subtile_idx)]
-                            cute.copy(tiled_copy_t2r, tTR_tO_mn, tTR_rO)
-                            # Modify in registers
-                            for k in cutlass.range(cute.size(tTR_rO), vectorize=True):
-                                tTR_rO[k] = tTR_rO[k] * acc_scale
-                            # Store back to TMEM via paired atom's store direction
-                            cute.copy(tiled_copy_t2r.retile_to_S(), tTR_rO, tTR_tO_mn)
-                        cute.arch.fence_view_async_tmem_store()
+                # D1.5: O rescale for kt > 0 is NOT supported in-kernel.
+                # Multi-KV-tile attention uses Python KV merge instead.
+                # n_kv_tiles=1 is the only tested/supported path.
 
                 si_handle.release()
                 softmax_done_bar.arrive()