diff --git a/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/patches/sglang-kimi-image-sm120-flashinfer-mxfp4.patch b/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/patches/sglang-kimi-image-sm120-flashinfer-mxfp4.patch
index 4ed6129..a7adada 100644
--- a/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/patches/sglang-kimi-image-sm120-flashinfer-mxfp4.patch
+++ b/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/patches/sglang-kimi-image-sm120-flashinfer-mxfp4.patch
@@ -95,7 +95,7 @@
          if self.use_flashinfer:
              # Per-expert buffers are local (create_weights uses num_local_experts);
              # the global self.num_experts here breaks EP>1. Mirrors the SM90 path.
-@@ -1130,7 +1135,135 @@
+@@ -1130,7 +1135,136 @@
          layer.w2_weight_bias = Parameter(w2_bias_padded, requires_grad=False)
  
          torch.cuda.empty_cache()
@@ -192,7 +192,8 @@
 +
 +        activation = layer.moe_runner_config.activation
 +        alpha = layer.moe_runner_config.gemm1_alpha
-+        beta = layer.moe_runner_config.gemm1_beta
++        # Older Kimi image baselines predate the generic gemm1_beta field.
++        beta = getattr(layer.moe_runner_config, "gemm1_beta", None)
 +        limit = layer.moe_runner_config.gemm1_clamp_limit
 +        if activation == "situ":
 +            alpha = 4.0 if alpha is None else alpha
@@ -231,7 +232,7 @@
      def create_moe_runner(
          self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
      ):
-@@ -1154,10 +1287,23 @@
+@@ -1154,10 +1288,23 @@
              or moe_runner_backend.is_deep_gemm()
          ):
              self.runner = MoeRunner(moe_runner_backend, moe_runner_config)
@@ -258,7 +259,7 @@
              # Register the fused func at runner construction so the FusedOpPool
              # lookup at `MoeRunner.__init__` finds it.
              import sglang.srt.layers.moe.moe_runner.flashinfer_cutlass  # noqa: F401
-@@ -1215,8 +1361,33 @@
+@@ -1215,8 +1362,33 @@
          quant_info = build_marlin_moe_quant_info(layer)
          return self.runner.run(
              dispatch_output._replace(hidden_states=x_padded), quant_info
@@ -292,7 +293,7 @@
      def apply(
          self,
          layer: torch.nn.Module,
-@@ -1290,6 +1461,8 @@
+@@ -1290,6 +1462,8 @@
  
          if self._fi_kernel == "cutlass_sm90":
              return self._apply_sm90_cutlass(layer, dispatch_output)