diff --git a/nanovllm/kvcache/offload_engine.py b/nanovllm/kvcache/offload_engine.py
index cd3ad01..e34a211 100644
--- a/nanovllm/kvcache/offload_engine.py
+++ b/nanovllm/kvcache/offload_engine.py
@@ -118,6 +118,24 @@ class OffloadEngine:
             dtype=dtype, device="cuda"
         )
 
+        # ========== Per-layer decode buffer ==========
+        # During decode, all layers share decode_slot (no layer dimension in GPU cache).
+        # This causes accumulated tokens to be overwritten by each layer.
+        # Solution: Maintain separate per-layer buffers for decode tokens.
+        # Shape: [num_layers, block_size, kv_heads, head_dim]
+        # Memory: num_layers * block_size * kv_heads * head_dim * dtype_size
+        #         e.g., 28 * 1024 * 8 * 128 * 2 = 58.7 MB (acceptable)
+        self.decode_k_buffer = torch.zeros(
+            num_layers, block_size, num_kv_heads, head_dim,
+            dtype=dtype, device="cuda"
+        )
+        self.decode_v_buffer = torch.zeros(
+            num_layers, block_size, num_kv_heads, head_dim,
+            dtype=dtype, device="cuda"
+        )
+        decode_buf_mb = 2 * num_layers * block_size * num_kv_heads * head_dim * dtype.itemsize / (1024 * 1024)
+        logger.info(f"  Per-layer decode buffer: {decode_buf_mb:.1f} MB")
+
         # ========== Fixed-address CPU KV cache (pinned memory) ==========
         self.k_cache_cpu = torch.zeros(
             num_layers, num_cpu_blocks, block_size, num_kv_heads, head_dim,
diff --git a/nanovllm/layers/attention.py b/nanovllm/layers/attention.py
index 88dfce1..de7dbff 100644
--- a/nanovllm/layers/attention.py
+++ b/nanovllm/layers/attention.py
@@ -87,6 +87,15 @@ class Attention(nn.Module):
         else:    # decode
             if context.is_chunked_prefill:
                 # Chunked decode: need to load all KV from CPU+GPU
+                # Store current decode token to per-layer decode buffer
+                # This is needed because GPU cache has no layer dimension,
+                # so all layers would overwrite each other in decode_slot.
+                kvcache_manager = context.kvcache_manager
+                offload_engine = kvcache_manager.offload_engine
+                pos_in_block = context.decode_pos_in_block
+                # k, v shape: [1, kv_heads, head_dim]
+                offload_engine.decode_k_buffer[self.layer_id, pos_in_block].copy_(k.squeeze(0))
+                offload_engine.decode_v_buffer[self.layer_id, pos_in_block].copy_(v.squeeze(0))
                 o = self._chunked_decode_attention(q, k, v, context)
             else:
                 o = flash_attn_with_kvcache(q.unsqueeze(1), k_cache, v_cache,
@@ -390,25 +399,17 @@ class Attention(nn.Module):
         context,
     ) -> torch.Tensor:
         """
-        Compute decode attention with double-buffering using decode_load_slots.
+        Compute decode attention using ring buffer pipeline (same as prefill).
 
-        Decode uses:
-        - decode_slot (slot[0]): writes new token's KV
-        - decode_load_slots (slots[1:]): load previous chunks from CPU
+        Uses the same loading mechanism as _chunked_prefill_attention:
+        - Load one block at a time from CPU to GPU slot
+        - Compute attention for each block
+        - Merge results using online softmax
+        - Finally merge with decode buffer (accumulated decode tokens)
 
-        Pipeline design:
-        - First half of decode_load_slots: 'compute' buffer
-        - Second half: 'prefetch' buffer
-        - Double-buffer between them for async overlap
-
-        Timeline:
-        ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
-        │Load C0→buf0 │ │Load C1→buf1 │ │Load C2→buf0 │ ...
-        └─────────────┘ └─────────────┘ └─────────────┘
-                      ↘               ↘               ↘
-                       ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
-                       │ Attn(C0)    │ │ Attn(C1)    │ │ Attn(C2)    │
-                       └─────────────┘ └─────────────┘ └─────────────┘
+        This approach is simpler and proven correct (prefill tests pass).
+        The only difference from prefill is the additional decode buffer
+        that stores new tokens generated during decode.
         """
         from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
 
@@ -419,7 +420,6 @@ class Attention(nn.Module):
         seq = context.chunked_seq
 
         # Get only PREFILLED CPU blocks (exclude the current decode block)
-        # The decode block's KV is still in GPU decode_slot, not yet offloaded to CPU
         cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
         if self.layer_id == 0:
             logger.debug(f"Decode attention: cpu_block_table={cpu_block_table}, seq.block_table={list(seq.block_table)}")
@@ -427,12 +427,12 @@ class Attention(nn.Module):
             raise RuntimeError("Chunked decode attention failed: no prefilled CPU blocks available")
 
         # Calculate valid tokens in the last block
-        # prefill_len = total prefilled tokens (current decode token not yet in CPU)
+        # Note: For chunked prefill, each block is exactly block_size tokens
+        # The cpu_block_table only contains full prefill blocks
         block_size = kvcache_manager.block_size
-        prefill_len = len(seq) - 1  # Exclude current decode token
-        last_block_valid_tokens = prefill_len % block_size
-        if last_block_valid_tokens == 0 and prefill_len > 0:
-            last_block_valid_tokens = block_size  # Last block is full
+        num_prefill_blocks = len(cpu_block_table)
+        # All prefill blocks are full (block_size tokens each)
+        last_block_valid_tokens = block_size
 
         # Apply sparse policy if enabled
         if kvcache_manager.sparse_policy is not None:
@@ -440,7 +440,7 @@ class Attention(nn.Module):
                 query_chunk_idx=0,
                 num_query_chunks=1,
                 layer_id=self.layer_id,
-                query=q_batched,  # Decode provides query for query-aware selection
+                query=q_batched,
                 is_prefill=False,
                 block_size=kvcache_manager.block_size,
                 total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
@@ -450,104 +450,28 @@ class Attention(nn.Module):
             )
 
         offload_engine = kvcache_manager.offload_engine
-        compute_stream = offload_engine.compute_stream
+        load_slots = offload_engine.decode_load_slots  # Available slots for loading
 
-        # Chunk size = capacity of each double buffer region (compute/prefetch)
-        # Each region uses half of decode_load_slots
-        chunk_size = max(1, len(offload_engine.decode_load_slots) // 2)
-        num_chunks = (len(cpu_block_table) + chunk_size - 1) // chunk_size
+        # Use ring buffer pipeline (same as prefill) to load prefilled blocks
+        o_acc, lse_acc = self._decode_ring_buffer_pipeline(
+            q_batched, cpu_block_table, load_slots, offload_engine,
+            block_size, last_block_valid_tokens
+        )
 
-        # Check if double buffering is possible (need at least 2 separate regions)
-        # With only 1 load slot, compute and prefetch regions overlap -> can't double buffer
-        can_double_buffer = len(offload_engine.decode_load_slots) >= 2
-
-        o_acc = None
-        lse_acc = None
-
-        # Double buffering state: True = use Compute region, False = use Prefetch region
-        use_compute = True
-
-        # Pre-load first chunk to Compute region (async)
-        first_chunk_ids = cpu_block_table[:min(chunk_size, len(cpu_block_table))]
-        offload_engine.load_to_compute_layer(self.layer_id, first_chunk_ids)
-
-        for chunk_idx in range(num_chunks):
-            start = chunk_idx * chunk_size
-            end = min(start + chunk_size, len(cpu_block_table))
-            num_blocks_in_chunk = end - start
-
-            # Wait for current buffer to be ready on compute_stream
-            # The load runs on transfer_stream_main, compute runs on compute_stream
-            compute_stream.wait_stream(offload_engine.transfer_stream_main)
-
-            # All computation on explicit compute_stream
-            with torch.cuda.stream(compute_stream):
-                # Get KV from current buffer FIRST, before prefetching overwrites it
-                if use_compute:
-                    k_chunk, v_chunk = offload_engine.get_kv_for_compute(num_blocks_in_chunk)
-                else:
-                    k_chunk, v_chunk = offload_engine.get_kv_for_prefetch(num_blocks_in_chunk)
-
-                # Handle partial last block: slice to only include valid tokens
-                # This is critical because the rest of the block contains stale data
-                is_last_chunk = (end == len(cpu_block_table))
-                if is_last_chunk and last_block_valid_tokens < block_size:
-                    # Calculate total valid tokens in this chunk
-                    # All blocks except the last are full, last block has last_block_valid_tokens
-                    full_blocks = num_blocks_in_chunk - 1
-                    valid_tokens = full_blocks * block_size + last_block_valid_tokens
-                    # Slice KV: [batch, seqlen, heads, dim] -> [batch, valid_tokens, heads, dim]
-                    k_chunk = k_chunk[:, :valid_tokens, :, :]
-                    v_chunk = v_chunk[:, :valid_tokens, :, :]
-
-                # Compute attention for this chunk
-                o_chunk, lse_chunk = flash_attn_with_lse(
-                    q_batched, k_chunk, v_chunk,
-                    softmax_scale=self.scale,
-                    causal=False,
-                )
-
-                # Merge with accumulated
-                if o_acc is None:
-                    o_acc, lse_acc = o_chunk, lse_chunk
-                else:
-                    o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, o_chunk, lse_chunk)
-
-            # Trigger async prefetch/load of next chunk to the OTHER buffer
-            # This happens AFTER attention completes, so the data is no longer needed
-            if chunk_idx + 1 < num_chunks:
-                next_start = end
-                next_end = min(next_start + chunk_size, len(cpu_block_table))
-                next_chunk_ids = cpu_block_table[next_start:next_end]
-                if can_double_buffer:
-                    if use_compute:
-                        # Current in Compute, prefetch next to Prefetch region
-                        offload_engine.load_to_prefetch_layer(self.layer_id, next_chunk_ids)
-                    else:
-                        # Current in Prefetch, prefetch next to Compute region
-                        offload_engine.load_to_compute_layer(self.layer_id, next_chunk_ids)
-                else:
-                    # Sync fallback: load next chunk to same slot (always compute region)
-                    offload_engine.load_to_compute_layer(self.layer_id, next_chunk_ids)
-
-            # Swap buffers for next iteration (only matters if can_double_buffer)
-            use_compute = not use_compute
-
-        # Now attend to Decode region (contains accumulated decode tokens)
+        # Now attend to accumulated decode tokens from per-layer decode buffer
         pos_in_block = context.decode_pos_in_block
         start_pos = context.decode_start_pos_in_block
         num_accumulated = pos_in_block - start_pos + 1
 
-        # IMPORTANT: Sync compute_stream with default stream before reading decode_slot
-        # store_kvcache writes to decode_slot on default stream (before entering this function)
-        # We need to ensure that write is complete before reading on compute_stream
+        # Sync compute_stream with default stream before reading decode_buffer
+        compute_stream = offload_engine.compute_stream
         compute_stream.wait_stream(torch.cuda.default_stream())
 
         with torch.cuda.stream(compute_stream):
             if num_accumulated > 0:
-                # GPU cache has no layer dimension
-                decode_k = offload_engine.k_cache_gpu[offload_engine.decode_slot, start_pos:pos_in_block+1]
-                decode_v = offload_engine.v_cache_gpu[offload_engine.decode_slot, start_pos:pos_in_block+1]
+                # Read from per-layer decode buffer
+                decode_k = offload_engine.decode_k_buffer[self.layer_id, start_pos:pos_in_block+1]
+                decode_v = offload_engine.decode_v_buffer[self.layer_id, start_pos:pos_in_block+1]
                 decode_k = decode_k.unsqueeze(0)
                 decode_v = decode_v.unsqueeze(0)
 
@@ -566,7 +490,82 @@ class Attention(nn.Module):
             raise RuntimeError("Chunked decode attention failed: no KV available")
 
         # Sync back to default stream before returning
-        # Caller expects result to be ready on default stream
         torch.cuda.default_stream().wait_stream(compute_stream)
 
         return o_acc
+
+    def _decode_ring_buffer_pipeline(
+        self,
+        q_batched: torch.Tensor,
+        cpu_block_table: list,
+        load_slots: list,
+        offload_engine,
+        block_size: int,
+        last_block_valid_tokens: int,
+    ):
+        """
+        Ring buffer pipeline for decode prefill loading (same mechanism as prefill).
+
+        Loads one block at a time, computes attention, and merges results.
+        Uses the same load_to_slot_layer / wait_slot_layer / get_kv_for_slot
+        methods as prefill for proven correctness.
+        """
+        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
+
+        num_blocks = len(cpu_block_table)
+        if num_blocks == 0:
+            return None, None
+
+        if not load_slots:
+            return None, None
+
+        o_acc, lse_acc = None, None
+        num_slots = len(load_slots)
+        compute_stream = offload_engine.compute_stream
+
+        # Phase 1: Pre-load up to num_slots blocks
+        num_preload = min(num_slots, num_blocks)
+        for i in range(num_preload):
+            offload_engine.load_to_slot_layer(load_slots[i], self.layer_id, cpu_block_table[i])
+
+        # Phase 2: Process blocks with pipeline
+        for block_idx in range(num_blocks):
+            current_slot = load_slots[block_idx % num_slots]
+            cpu_block_id = cpu_block_table[block_idx]
+
+            # Wait for current slot's transfer to complete
+            offload_engine.wait_slot_layer(current_slot)
+
+            with torch.cuda.stream(compute_stream):
+                # Get KV from slot
+                prev_k, prev_v = offload_engine.get_kv_for_slot(current_slot)
+
+                # Handle partial last block
+                is_last_block = (block_idx == num_blocks - 1)
+                if is_last_block and last_block_valid_tokens < block_size:
+                    prev_k = prev_k[:, :last_block_valid_tokens, :, :]
+                    prev_v = prev_v[:, :last_block_valid_tokens, :, :]
+
+                # Compute attention
+                prev_o, prev_lse = flash_attn_with_lse(
+                    q_batched, prev_k, prev_v,
+                    softmax_scale=self.scale,
+                    causal=False,
+                )
+
+                # Record compute done for slot reuse
+                offload_engine.record_slot_compute_done(current_slot)
+
+            # Start loading next block (pipeline)
+            next_block_idx = block_idx + num_slots
+            if next_block_idx < num_blocks:
+                offload_engine.load_to_slot_layer(current_slot, self.layer_id, cpu_block_table[next_block_idx])
+
+            # Merge with accumulated
+            with torch.cuda.stream(compute_stream):
+                if o_acc is None:
+                    o_acc, lse_acc = prev_o, prev_lse
+                else:
+                    o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
+
+        return o_acc, lse_acc
diff --git a/tests/test_chunked_decode_hook.py b/tests/test_chunked_decode_hook.py
index 4113381..e1dcfec 100644
--- a/tests/test_chunked_decode_hook.py
+++ b/tests/test_chunked_decode_hook.py
@@ -92,13 +92,14 @@ def compute_decode_reference(layer_id: int, decode_step: int, scale: float,
     q = decode_cap['q'].cuda()  # [1, num_heads, head_dim]
     q_batched = q.unsqueeze(0)  # [1, 1, num_heads, head_dim]
 
-    # Collect all K, V: prefill chunks from CPU cache + decode tokens from captures
+    # Collect all K, V: prefill chunks from captures + decode tokens from captures
+    # NOTE: We use prefill captures directly instead of CPU cache because
+    # the CPU block ID may not equal the chunk index.
     all_k = []
     all_v = []
 
-    # 1. Prefill chunks from CPU cache
+    # 1. Prefill chunks from captures (use captured K/V, not CPU cache)
     for cidx in range(num_prefill_chunks):
-        # Get prefill capture to know the sequence length for this chunk
         prefill_cap = None
         for c in prefill_captures:
             if c['layer_id'] == layer_id and c['chunk_idx'] == cidx:
@@ -106,11 +107,9 @@ def compute_decode_reference(layer_id: int, decode_step: int, scale: float,
                 break
 
         if prefill_cap is not None:
-            seq_len = prefill_cap['q'].shape[0]
-            k = k_cache_cpu[layer_id, cidx, :seq_len].cuda()
-            v = v_cache_cpu[layer_id, cidx, :seq_len].cuda()
-            all_k.append(k)
-            all_v.append(v)
+            # Use captured K/V directly (guaranteed to be correct layer data)
+            all_k.append(prefill_cap['k'].cuda())
+            all_v.append(prefill_cap['v'].cuda())
 
     # 2. Decode tokens from captures (up to and including current step)
     for step in range(decode_step + 1):
@@ -184,6 +183,184 @@ v_cache_cpu = offload_engine.v_cache_cpu.clone()
 # Calculate number of prefill chunks
 num_prefill_chunks = INPUT_LEN // BLOCK_SIZE
 
+# Debug: Compare decode_buffer with captured K/V
+print("\n=== DEBUG: Comparing decode_buffer with captured K/V ===")
+decode_k_buffer = offload_engine.decode_k_buffer.clone().cpu()
+for step in range(NUM_DECODE_TOKENS):
+    for layer_id in [0, 17, 35]:  # Sample a few layers
+        # Find captured K for this step and layer
+        for c in decode_captures:
+            if c['layer_id'] == layer_id and c['decode_step'] == step:
+                captured_k = c['k'].squeeze(0)  # [kv_heads, head_dim]
+                buffer_k = decode_k_buffer[layer_id, step]  # [kv_heads, head_dim]
+                diff = (captured_k - buffer_k).abs().max().item()
+                print(f"Step {step}, Layer {layer_id}: captured vs buffer max_diff={diff:.6f}")
+                break
+
+# Debug: Verify that decode_buffer slices match concatenated captures
+print("\n=== DEBUG: Verifying decode_buffer slices ===")
+for layer_id in [0]:
+    for decode_step in [1, 2]:  # Check steps that use multiple tokens
+        # Build expected slice from captures
+        expected_k_list = []
+        for step in range(decode_step + 1):
+            for c in decode_captures:
+                if c['layer_id'] == layer_id and c['decode_step'] == step:
+                    expected_k_list.append(c['k'].squeeze(0))  # [kv_heads, head_dim]
+                    break
+        if expected_k_list:
+            expected_k = torch.stack(expected_k_list, dim=0)  # [num_tokens, kv_heads, head_dim]
+            buffer_slice = decode_k_buffer[layer_id, 0:decode_step+1]
+            diff = (expected_k - buffer_slice).abs().max().item()
+            print(f"Decode step {decode_step}, Layer {layer_id}: buffer slice vs expected max_diff={diff:.6f}")
+            # Print first values
+            print(f"  Buffer[0,0,0]={buffer_slice[0,0,0].item():.6f}, Expected[0,0,0]={expected_k[0,0,0].item():.6f}")
+            if decode_step >= 1:
+                print(f"  Buffer[1,0,0]={buffer_slice[1,0,0].item():.6f}, Expected[1,0,0]={expected_k[1,0,0].item():.6f}")
+
+# Debug: Print expected K value for block 0, layer 0 (to compare with actual loading)
+print("\n=== DEBUG: Expected K values for block 0, layer 0 ===")
+for c in prefill_captures:
+    if c['layer_id'] == 0 and c['chunk_idx'] == 0:
+        print(f"Captured K[0,0,0] for layer 0, chunk 0: {c['k'][0,0,0].item():.6f}")
+        break
+print(f"CPU cache K[0,0,0,0,0] for layer 0, block 0: {k_cache_cpu[0,0,0,0,0].item():.6f}")
+
+# Debug: Compare CPU cache with captured prefill K/V
+print("\n=== DEBUG: Comparing CPU cache with captured prefill K/V ===")
+for chunk_idx in [0, 7, 15]:  # Sample a few chunks
+    for layer_id in [0, 17, 35]:  # Sample a few layers
+        # Find captured K for this chunk and layer
+        for c in prefill_captures:
+            if c['layer_id'] == layer_id and c['chunk_idx'] == chunk_idx:
+                captured_k = c['k']  # [seq_len, kv_heads, head_dim]
+                cpu_cache_k = k_cache_cpu[layer_id, chunk_idx, :captured_k.shape[0]]
+                diff = (captured_k - cpu_cache_k).abs().max().item()
+                print(f"Chunk {chunk_idx}, Layer {layer_id}: captured vs CPU cache max_diff={diff:.6f}")
+                break
+
+# Debug: Get cpu_block_table to check order
+kvcache_manager = llm.model_runner.kvcache_manager
+# Find the sequence (it should still exist)
+from nanovllm.engine.sequence import Sequence
+for attr_name in ['sequences', '_sequences', 'active_sequences']:
+    if hasattr(kvcache_manager, attr_name):
+        print(f"Found {attr_name}")
+        break
+
+# Try to get cpu_block_table through a different way
+print(f"\n=== DEBUG: CPU block order ===")
+# For each prefill capture, check which CPU block it ended up in
+for chunk_idx in range(num_prefill_chunks):
+    for c in prefill_captures:
+        if c['layer_id'] == 0 and c['chunk_idx'] == chunk_idx:
+            # Check if this chunk's K matches any CPU block
+            captured_k_first = c['k'][0, 0, 0].item()
+            for block_id in range(num_prefill_chunks):
+                cpu_k_first = k_cache_cpu[0, block_id, 0, 0, 0].item()
+                if abs(captured_k_first - cpu_k_first) < 1e-6:
+                    print(f"Chunk {chunk_idx} -> CPU block {block_id}")
+                    break
+            break
+
+# Debug: Check reference vs actual for decode steps 0 and 1
+# Also compute partial references (prefill only, decode only) to isolate the bug
+from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
+for decode_step in [0, 1]:
+    print(f"\n=== DEBUG: Reference vs Actual for layer 0, decode {decode_step} ===")
+    layer_id = 0
+    # Find the capture
+    for c in decode_captures:
+        if c['layer_id'] == layer_id and c['decode_step'] == decode_step:
+            q = c['q'].cuda()  # [1, num_heads, head_dim]
+            q_batched = q.unsqueeze(0)  # [1, 1, num_heads, head_dim]
+
+            # Build prefill K/V per-block for block-by-block reference
+            prefill_k_blocks = []
+            prefill_v_blocks = []
+            for cidx in range(num_prefill_chunks):
+                for pc in prefill_captures:
+                    if pc['layer_id'] == layer_id and pc['chunk_idx'] == cidx:
+                        prefill_k_blocks.append(pc['k'].cuda().unsqueeze(0))  # [1, block_size, kv_heads, head_dim]
+                        prefill_v_blocks.append(pc['v'].cuda().unsqueeze(0))
+                        break
+
+            # Build decode K/V
+            decode_k_list = []
+            decode_v_list = []
+            for step in range(decode_step + 1):
+                for dc in decode_captures:
+                    if dc['layer_id'] == layer_id and dc['decode_step'] == step:
+                        decode_k_list.append(dc['k'].cuda())
+                        decode_v_list.append(dc['v'].cuda())
+                        break
+
+            full_prefill_k = torch.cat([kb.squeeze(0) for kb in prefill_k_blocks], dim=0).unsqueeze(0)
+            full_prefill_v = torch.cat([vb.squeeze(0) for vb in prefill_v_blocks], dim=0).unsqueeze(0)
+            full_decode_k = torch.cat(decode_k_list, dim=0).unsqueeze(0)
+            full_decode_v = torch.cat(decode_v_list, dim=0).unsqueeze(0)
+
+            full_k = torch.cat([full_prefill_k, full_decode_k], dim=1)
+            full_v = torch.cat([full_prefill_v, full_decode_v], dim=1)
+
+            print(f"Q shape: {q_batched.shape}")
+            print(f"Prefill K shape: {full_prefill_k.shape}")
+            print(f"Decode K shape: {full_decode_k.shape}")
+            print(f"Full K shape: {full_k.shape}")
+            print(f"Total tokens: prefill={num_prefill_chunks * BLOCK_SIZE}, decode={decode_step + 1}")
+
+            # Reference output (single attention over all)
+            ref_output = flash_attn_func(
+                q_batched, full_k, full_v,
+                softmax_scale=scale,
+                causal=False,
+            )
+
+            # Chunked reference: prefill attention + decode attention + merge
+            prefill_o, prefill_lse = flash_attn_with_lse(
+                q_batched, full_prefill_k, full_prefill_v,
+                softmax_scale=scale,
+                causal=False,
+            )
+            decode_o, decode_lse = flash_attn_with_lse(
+                q_batched, full_decode_k, full_decode_v,
+                softmax_scale=scale,
+                causal=False,
+            )
+            chunked_output, _ = merge_attention_outputs(prefill_o, prefill_lse, decode_o, decode_lse)
+
+            # Block-by-block reference (simulating ring buffer pipeline)
+            block_o_acc, block_lse_acc = None, None
+            for bidx, (kb, vb) in enumerate(zip(prefill_k_blocks, prefill_v_blocks)):
+                o_blk, lse_blk = flash_attn_with_lse(q_batched, kb, vb, softmax_scale=scale, causal=False)
+                if block_o_acc is None:
+                    block_o_acc, block_lse_acc = o_blk, lse_blk
+                else:
+                    block_o_acc, block_lse_acc = merge_attention_outputs(block_o_acc, block_lse_acc, o_blk, lse_blk)
+
+            # Compare block-by-block vs single
+            block_vs_single_diff = (block_o_acc - prefill_o).abs().max().item()
+            print(f"Block-by-block vs Single max_diff: {block_vs_single_diff:.6f}")
+
+            # Compare full reference vs chunked reference
+            ref_vs_chunked_diff = (ref_output - chunked_output).abs().max().item()
+            print(f"Reference vs Chunked-reference max_diff: {ref_vs_chunked_diff:.6f}")
+
+            ref_output = ref_output.squeeze(0).squeeze(0).cpu()
+            chunked_output_cpu = chunked_output.squeeze(0).squeeze(0).cpu()
+
+            # Actual output
+            actual_output = c['output'].squeeze(0)
+            if actual_output.dim() == 3:
+                actual_output = actual_output.squeeze(0)
+
+            diff_ref = (actual_output - ref_output).abs()
+            diff_chunked = (actual_output - chunked_output_cpu).abs()
+            print(f"Actual vs Reference max_diff: {diff_ref.max().item():.6f}")
+            print(f"Actual vs Chunked-ref max_diff: {diff_chunked.max().item():.6f}")
+            break
+print()
+
 # Verify decode outputs
 all_passed = True
 
@@ -208,7 +385,7 @@ for c in decode_captures:
     passed = max_diff < 1e-1
     all_passed = all_passed and passed
 
-    # if not passed:
-    print(f"[FAIL] Layer {layer_id}, Decode {decode_step}: max_diff={max_diff:.6f}")
+    if not passed:
+        print(f"[FAIL] Layer {layer_id}, Decode {decode_step}: max_diff={max_diff:.6f}")
 
 print(f"test_chunked_decode_hook: {'PASSED' if all_passed else 'FAILED'}")