diff --git a/tests/unit/test_decode_fmha_layer.py b/tests/unit/test_decode_fmha_layer.py
index f9e8fca2..28cb1110 100644
--- a/tests/unit/test_decode_fmha_layer.py
+++ b/tests/unit/test_decode_fmha_layer.py
@@ -439,32 +439,30 @@ def main():
             continue
         o_prod = o_prod_4d.squeeze(0)  # (n_h, T, hd)
 
-        # 7. Reference: dequantize mixed KV to BF16, run SDPA WITH sink bias
+        # 7. Reference: dequantize mixed KV to BF16, run reference with sink bias
         nope_dequant = kv_nope_fp8.view(torch.float8_e4m3fn).float() * kv_nope_scale.unsqueeze(-1).float()
         kv_full = torch.cat([nope_dequant.bfloat16(), kv_rope_bf16], dim=-1)  # (N, hd)
         k_4d = kv_full.unsqueeze(0).unsqueeze(0).expand(1, 1, -1, -1)  # (1, 1, N, hd)
         v_4d = k_4d.clone()
         if sink_bias is not None:
-            # Reference with sink bias: compute attention scores manually and add sink as logit bias
-            # DSV4 sink merge = single softmax over [S_comp, S_swa + attn_sink]
-            # The sink bias is added as a logit to the LAST position (the sink position)
-            # In our SWA-only case, the sink is the last SWA entry.
-            # Formula: attn_weights = softmax(Q @ K^T * scale + sink_bias_on_last_pos)
-            # For per-head sink: sink_bias shape is (n_h,)
+            # DSV4 sink is denominator-only: O = sum(P*V) / (sum(P) + exp(sb))
+            # where P = softmax(QK*scale). The sink has NO V contribution.
+            # Reference: compute O_no_sink, then scale by correction factor.
             q_ref = q_4d.float()  # (1, H, T, hd)
             k_ref = k_4d.float().expand(1, n_h, -1, -1)  # (1, H, N, hd)
             v_ref = v_4d.float().expand(1, n_h, -1, -1)  # (1, H, N, hd)
             scores = torch.matmul(q_ref, k_ref.transpose(-2, -1)) * scale  # (1, H, T, N)
-            # Add sink bias to the last position
-            # sink_bias: (n_h,) → (1, n_h, 1, 1) broadcast to (1, n_h, T, N) on last N dim
-            # The production kernel adds sink_bias / scale_softmax to the last SWA position
-            # Actually, the sink bias is added as-is to the logit at the sink position
-            sb_4d = sink_bias.reshape(1, n_h, 1, 1)  # (1, H, 1, 1)
-            # The sink position is the LAST position in the KV sequence
-            scores[:, :, :, -1:] = scores[:, :, :, -1:] + sb_4d
-            # Softmax
-            attn_weights = F.softmax(scores, dim=-1)
-            o_ref_4d = torch.matmul(attn_weights, v_ref).bfloat16()  # (1, H, T, hd)
+            # O_no_sink = softmax(scores) @ V
+            O_no_sink = F.softmax(scores, dim=-1) @ v_ref  # (1, H, T, hd)
+            # Correction: O_with_sink = O_no_sink * Z / (Z + exp(sb))
+            # Z = sum(exp(scores - max)) per head, but more conveniently:
+            # Z / (Z + exp(sb)) = 1 / (1 + exp(sb) / Z) = 1 / (1 + exp(sb - log(Z)))
+            # log(Z) = logsumexp(scores)
+            lse = torch.logsumexp(scores, dim=-1, keepdim=True)  # (1, H, T, 1)
+            # sb shape: (n_h,) → (1, n_h, 1, 1)
+            sb_4d = sink_bias.reshape(1, n_h, 1, 1)
+            correction = 1.0 / (1.0 + torch.exp(sb_4d - lse))
+            o_ref_4d = (O_no_sink * correction).bfloat16()  # (1, H, T, hd)
         else:
             o_ref_4d = F.scaled_dot_product_attention(q_4d, k_4d, v_4d, scale=scale)  # (1, H, T, hd)
         o_ref = o_ref_4d.squeeze(0)  # (n_h, T, hd)