diff --git a/examples/models/qwen3/qwen3_32b_decode_scope3.py b/examples/models/qwen3/qwen3_32b_decode_scope3.py
index ed8249d..dfa3b0f 100644
--- a/examples/models/qwen3/qwen3_32b_decode_scope3.py
+++ b/examples/models/qwen3/qwen3_32b_decode_scope3.py
@@ -63,14 +63,7 @@ def scope3(
             for b0 in pl.range(0, BATCH_CFG, BATCH_TILE):
                 resid1_tile = pl.create_tensor([BATCH_TILE, HIDDEN_CFG], dtype=pl.FP32)
 
-                # Stage 0: Initialize resid1_tile accumulator in parallel.
-                with pl.auto_incore():
-                    for ob in pl.parallel(0, Q_OUT_BLOCKS, chunk=8):
-                        o0 = ob * Q_OUT_CHUNK
-                        zero_resid1 = pl.full([BATCH_TILE, Q_OUT_CHUNK], dtype=pl.FP32, value=0.0)
-                        resid1_tile = pl.assemble(resid1_tile, zero_resid1, [0, o0])
-
-                # Stage 1: Output projection: attn_out × wo, tiled by Q_OUT_CHUNK.
+                # Stage 0: Output projection: attn_out × wo, tiled by Q_OUT_CHUNK.
                 for ob in pl.range(Q_OUT_BLOCKS):
                     o0 = ob * Q_OUT_CHUNK
 
@@ -84,7 +77,7 @@ def scope3(
                             w_chunk = pl.slice(wo, [K_CHUNK, Q_OUT_CHUNK], [k0, o0])
                             o_acc = pl.matmul_acc(o_acc, a_chunk, w_chunk)
 
-                    # Stage 2: Residual addition with hidden_states
+                    # Stage 1: Residual addition with hidden_states
                     with pl.incore():
                         resid = pl.cast(
                             pl.slice(hidden_states, [BATCH_TILE, Q_OUT_CHUNK], [b0, o0]),
@@ -93,7 +86,7 @@ def scope3(
                         resid_sum = pl.add(o_acc, resid)
                         resid1_tile = pl.assemble(resid1_tile, resid_sum, [0, o0])
 
-                # Stage 3: Post-attention RMSNorm
+                # Stage 2: Post-attention RMSNorm
                 post_norm_tile = pl.create_tensor([BATCH_TILE, HIDDEN_CFG], dtype=pl.BF16)
                 with pl.incore():
                     sq_sum = pl.full([1, BATCH_TILE], dtype=pl.FP32, value=0.0)
@@ -111,18 +104,8 @@ def scope3(
                         normed_bf16 = pl.cast(normed, target_type=pl.BF16)
                         post_norm_tile = pl.assemble(post_norm_tile, normed_bf16, [0, k0])
 
-                # Stage 4:  Initialize mlp_tile
+                # Stage 3 & 4 & 5: MLP: gate/up projections + SiLU.
                 mlp_tile = pl.create_tensor([BATCH_TILE, INTER_CFG], dtype=pl.BF16)
-                with pl.auto_incore():
-                    for ob in pl.parallel(0, MLP_OUT_BLOCKS, chunk=8):
-                        o0 = ob * MLP_OUT_CHUNK
-                        zero_mlp = pl.cast(
-                            pl.full([BATCH_TILE, MLP_OUT_CHUNK], dtype=pl.FP32, value=0.0),
-                            target_type=pl.BF16,
-                        )
-                        mlp_tile = pl.assemble(mlp_tile, zero_mlp, [0, o0])
-
-                # Stage 5 & 6 & 7: MLP: gate/up projections + SiLU.
                 for ob in pl.range(MLP_OUT_BLOCKS):
                     o0 = ob * MLP_OUT_CHUNK
                     with pl.incore():
@@ -151,7 +134,7 @@ def scope3(
                         mlp_chunk_bf16 = pl.cast(mlp_chunk, target_type=pl.BF16)
                         mlp_tile = pl.assemble(mlp_tile, mlp_chunk_bf16, [0, o0])
 
-                # Stage 8 & 9: Down projection + final residual writeback.
+                # Stage 6 & 7: Down projection + final residual writeback.
                 for dob in pl.range(HIDDEN_BLOCKS):
                     d0 = dob * K_CHUNK
                     with pl.incore():