diff --git a/vllm/model_executor/models/qwen3_vl_moe.py b/vllm/model_executor/models/qwen3_vl_moe.py
index 8df1458e39fe..f65e95279b4a 100644
--- a/vllm/model_executor/models/qwen3_vl_moe.py
+++ b/vllm/model_executor/models/qwen3_vl_moe.py
@@ -110,9 +110,14 @@ def forward(
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
+
+        aux_hidden_states = []
         for layer_idx, layer in islice(
             enumerate(self.layers), self.start_layer, self.end_layer
         ):
+            if layer_idx in self.aux_hidden_state_layers:
+                aux_hidden_states.append(hidden_states + residual)
+
             hidden_states, residual = layer(
                 positions,
                 hidden_states,
@@ -132,6 +137,9 @@ def forward(
                 {"hidden_states": hidden_states, "residual": residual}
             )
         hidden_states, _ = self.norm(hidden_states, residual)
+
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
         return hidden_states
 
     def load_fused_expert_weights(
diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py
index 8ce53a2933de..1ae058c2eac1 100644
--- a/vllm/v1/spec_decode/eagle.py
+++ b/vllm/v1/spec_decode/eagle.py
@@ -112,7 +112,9 @@ def __init__(
         self.input_ids = torch.zeros(
             self.max_num_tokens, dtype=torch.int32, device=device
         )
-        self.uses_mrope = self.vllm_config.model_config.uses_mrope
+        # Use draft model's M-RoPE setting, not target model's
+        # Draft models may be text-only even if target is multimodal
+        self.uses_mrope = self.draft_model_config.uses_mrope
         if self.uses_mrope:
             # NOTE: `mrope_positions` is implemented with one additional dummy
             # position on purpose to make it non-contiguous so that it can work
@@ -221,6 +223,11 @@ def _set_positions(self, num_tokens: int, positions: torch.Tensor):
         if self.uses_mrope:
             self.mrope_positions[:, :num_tokens] = positions
         else:
+            # Convert M-RoPE positions if target model uses M-RoPE
+            # but draft doesn't, For text inputs, all M-RoPE
+            # dimensions are identical
+            if self.vllm_config.model_config.uses_mrope:
+                positions = positions[0]
             self.positions[:num_tokens] = positions
 
     def initialize_cudagraph_keys(self, cudagraph_mode: CUDAGraphMode) -> None:
@@ -1080,6 +1087,7 @@ def load_model(self, target_model: nn.Module) -> None:
             if self.get_model_name(target_model) in [
                 "Qwen2_5_VLForConditionalGeneration",
                 "Qwen3VLForConditionalGeneration",
+                "Qwen3VLMoeForConditionalGeneration",
             ]:
                 self.model.config.image_token_index = target_model.config.image_token_id
             elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":