diff --git a/tests/layertest.py b/tests/layertest.py
index 37e3cfe3..53932a54 100644
--- a/tests/layertest.py
+++ b/tests/layertest.py
@@ -271,20 +271,15 @@ def main():
     
     expert_offsets = compute_expert_offsets(tokens_per_expert, len(expert_indices))
 
-    # ── BF16 L1 reference (gate+up only) ──
+    # ── BF16 L1 reference (slot-major, matching kernel output) ──
     print("\n  Running BF16 L1 reference...")
-    ref_l1 = torch.zeros(num_tokens, 6144, dtype=torch.bfloat16, device=DEVICE)
-    for t in range(num_tokens):
-        for k in range(top_k):
-            e = expert_ids[t, k].item()
-            w = expert_weights[t, k].item()
-            if e not in nvfp4_experts_bf16:
-                continue
-            x = hidden_states[t]
-            gate = x @ nvfp4_experts_bf16[e]["gate_proj"].T  # (3072,)
-            up = x @ nvfp4_experts_bf16[e]["up_proj"].T      # (3072,)
-            ref_l1[t] += w * torch.cat([gate, up])
-
+    ref_l1_parts = []
+    for e in expert_indices:
+        for t in expert_token_lists[e]:
+            gate = hidden_states[t] @ nvfp4_experts_bf16[e]["gate_proj"].T
+            up = hidden_states[t] @ nvfp4_experts_bf16[e]["up_proj"].T
+            ref_l1_parts.append(torch.cat([gate, up]))
+    ref_l1 = torch.cat(ref_l1_parts, dim=0)  # (num_slots, 6144)
     print(f"    BF16 L1 ref: amax={ref_l1.abs().max():.4f} mean={ref_l1.float().mean():.6f}")
 
     del nvfp4_experts_bf16
@@ -296,11 +291,14 @@ def main():
     print(f"    Kernel L1:   amax={kernel_l1.abs().max():.4f} mean={kernel_l1.float().mean():.6f}")
 
     # ── Compare ──
+    ref_flat = ref_l1.flatten()
+    kernel_flat = kernel_l1.flatten()
+
     cosine = torch.nn.functional.cosine_similarity(
-        kernel_l1.flatten().unsqueeze(0).float(),
-        ref_l1.flatten().unsqueeze(0).float(),
+        kernel_flat.unsqueeze(0).float(),
+        ref_flat.unsqueeze(0).float(),
     ).item()
-    mse = (kernel_l1.float() - ref_l1.float()).pow(2).mean().item()
+    mse = (kernel_flat.float() - ref_flat.float()).pow(2).mean().item()
 
     print(f"\n{'=' * 70}")
     print(f"  RESULT: cosine={cosine:.6f}  MSE={mse:.6e}")