diff --git a/vllm_omni/entrypoints/openai/api_server.py b/vllm_omni/entrypoints/openai/api_server.py
index b6298c037..6404e56d9 100644
--- a/vllm_omni/entrypoints/openai/api_server.py
+++ b/vllm_omni/entrypoints/openai/api_server.py
@@ -67,7 +67,7 @@ from vllm.entrypoints.openai.speech_to_text.serving import (
 )
 from vllm.entrypoints.openai.utils import validate_json_request
 from vllm.entrypoints.pooling.classify.serving import ServingClassification
-from vllm.entrypoints.pooling.embed.serving import ServingEmbedding
+from vllm.entrypoints.pooling.embed.serving import OpenAIServingEmbedding
 from vllm.entrypoints.pooling.pooling.serving import OpenAIServingPooling
 from vllm.entrypoints.pooling.score.serving import ServingScores
 from vllm.entrypoints.serve.disagg.serving import ServingTokens
@@ -536,7 +536,13 @@ async def omni_init_app_state(
                             else vllm_config.model_config
                         )
                         io_processor_plugin = model_config.io_processor_plugin
-                        engine_client.io_processor = get_io_processor(vllm_config, io_processor_plugin)
+                        renderer = getattr(engine_client, "renderer", None)
+                        if renderer is None:
+                            from vllm.renderers import renderer_from_config
+
+                            renderer = renderer_from_config(vllm_config)
+                            engine_client.renderer = renderer
+                        engine_client.io_processor = get_io_processor(vllm_config, renderer, io_processor_plugin)
                         logger.info("Initialized io_processor for AsyncOmni")
                 else:
                     logger.warning("Cannot initialize processors: tokenizer is None. OpenAIServingModels may fail.")
@@ -627,7 +633,7 @@ async def omni_init_app_state(
         else None
     )
     state.openai_serving_embedding = (
-        ServingEmbedding(
+        OpenAIServingEmbedding(
             engine_client,
             state.openai_serving_models,
             request_logger=request_logger,
diff --git a/vllm_omni/model_executor/models/mimo_audio/mimo_audio_llm.py b/vllm_omni/model_executor/models/mimo_audio/mimo_audio_llm.py
index 1424ca775..c1b71e076 100644
--- a/vllm_omni/model_executor/models/mimo_audio/mimo_audio_llm.py
+++ b/vllm_omni/model_executor/models/mimo_audio/mimo_audio_llm.py
@@ -773,6 +773,7 @@ class MiMoAudioLLMForConditionalGeneration(nn.Module, SupportsMultiModal, Suppor
         multimodal_embeddings: MultiModalEmbeddings | None = None,
         *,
         is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
     ) -> torch.Tensor:
         # This is to satisfy the type checker for each overload
         if multimodal_embeddings is None or is_multimodal is None:
@@ -782,6 +783,7 @@ class MiMoAudioLLMForConditionalGeneration(nn.Module, SupportsMultiModal, Suppor
             input_ids,
             multimodal_embeddings=multimodal_embeddings,
             is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
         )
 
     def base_local_forward(
diff --git a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py
index 8c348a9c6..210d262f1 100644
--- a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py
+++ b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py
@@ -109,6 +109,7 @@ class Qwen2_5OmniTalkerForConditionalGeneration(
         multimodal_embeddings: MultiModalEmbeddings | None = None,
         *,
         is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
     ) -> torch.Tensor:
         # This is to satisfy the type checker for each overload
         if multimodal_embeddings is None or is_multimodal is None:
@@ -118,6 +119,7 @@ class Qwen2_5OmniTalkerForConditionalGeneration(
             input_ids,
             multimodal_embeddings=multimodal_embeddings,
             is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
         )
 
     def forward(
@@ -230,7 +232,7 @@ class Qwen2_5OmniTalkerForConditionalGeneration(
             return []
 
         # The result multimodal_embeddings is tuple of tensors, with each
-        # tensor correspoending to a multimodal data item (image or video).
+        # tensor corresponding to a multimodal data item (image or video).
         multimodal_embeddings: tuple[torch.Tensor, ...] = ()
 
         # NOTE: It is important to iterate over the keys in this dictionary
diff --git a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
index c32b121c2..4560ede39 100644
--- a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
+++ b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
@@ -58,6 +58,10 @@ from vllm.multimodal.inputs import (
     MultiModalKwargsItems,
 )
 from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.processing import (
+    ProcessorInputs,
+    TimingContext,
+)
 from vllm.multimodal.processing.processor import (
     MultiModalPromptUpdates,
     PlaceholderFeaturesInfo,
@@ -76,6 +80,16 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
 ):
     """Override to fix use_audio_in_video detection when mm cache returns None."""
 
+    def _cached_apply_hf_processor(
+        self,
+        inputs: ProcessorInputs,
+        timing_ctx: TimingContext,
+    ):
+        mm_processor_kwargs = inputs.hf_processor_mm_kwargs
+        if mm_processor_kwargs.get("use_audio_in_video", False):
+            return self._apply_hf_processor(inputs, timing_ctx)
+        return super()._cached_apply_hf_processor(inputs, timing_ctx)
+
     def _maybe_apply_prompt_updates(
         self,
         mm_items: MultiModalDataItems,
@@ -586,6 +600,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
         multimodal_embeddings: MultiModalEmbeddings | None = None,
         *,
         is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
     ) -> torch.Tensor:
         if multimodal_embeddings is None or is_multimodal is None:
             return super().embed_input_ids(input_ids)
@@ -594,6 +609,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
             input_ids,
             self.get_language_model().embed_input_ids,
             is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
         )
 
         if len(multimodal_embeddings) == 0:
@@ -616,6 +632,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
                 input_ids,
                 self.get_language_model().embed_input_ids,
                 is_multimodal=is_multimodal,
+                handle_oov_mm_token=handle_oov_mm_token,
             )
             return merge_interleaved_embeddings(
                 inputs_embeds,
@@ -632,6 +649,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
             input_ids,
             multimodal_embeddings=multimodal_embeddings,
             is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
         )
 
     def forward(
diff --git a/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py b/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py
index 9f5c20cf7..7b3d0b017 100644
--- a/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py
+++ b/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py
@@ -341,7 +341,7 @@ class Qwen3OmniMoeTalkerForConditionalGeneration(
         )
 
         # The result multimodal_embeddings is tuple of tensors, with each
-        # tensor correspoending to a multimodal data item (image or video).
+        # tensor corresponding to a multimodal data item (image or video).
         dummy_multimodal_embeddings: tuple[torch.Tensor, ...] = ()
 
         # NOTE: It is important to iterate over the keys in this dictionary
diff --git a/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py b/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py
index a0baefca7..ca7bb58c7 100644
--- a/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py
+++ b/vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py
@@ -966,11 +966,13 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
         multimodal_embeddings: MultiModalEmbeddings | None = None,
         *,
         is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
     ) -> torch.Tensor:
         inputs_embeds = self._embed_text_input_ids(
             input_ids,
             self.language_model.embed_input_ids,
             is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
         )
 
         if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
@@ -1062,6 +1064,7 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
             input_ids,
             multimodal_embeddings=multimodal_embeddings,
             is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
         )
 
     def forward(
diff --git a/vllm_omni/worker/gpu_ar_model_runner.py b/vllm_omni/worker/gpu_ar_model_runner.py
index 9dce9778d..d7d45031a 100644
--- a/vllm_omni/worker/gpu_ar_model_runner.py
+++ b/vllm_omni/worker/gpu_ar_model_runner.py
@@ -141,7 +141,7 @@ class GPUARModelRunner(OmniGPUModelRunner):
             # Update persistent batch states.
             self._update_states(scheduler_output)
 
-            if has_ec_transfer() and not get_ec_transfer().is_consumer:
+            if has_ec_transfer() and get_ec_transfer().is_producer:
                 with self.maybe_get_ec_connector_output(
                     scheduler_output,
                     encoder_cache=self.encoder_cache,
@@ -276,9 +276,9 @@ class GPUARModelRunner(OmniGPUModelRunner):
 
         # Run the model.
         # Use persistent buffers for CUDA graphs.
-        # When spec decode is enabled, defer connector finalization
-        # (wait_for_save + clear metadata) until after draft model runs.
-        defer_kv_connector_finalize = self.speculative_config is not None
+        # When spec decode is enabled, delay clearing connector metadata
+        # until after draft model runs in sample_tokens.
+        clear_kv_metadata = self.speculative_config is None
         with (
             set_forward_context(
                 attn_metadata,
@@ -292,8 +292,7 @@ class GPUARModelRunner(OmniGPUModelRunner):
             ),
             record_function_or_nullcontext("gpu_model_runner: forward"),
             self.maybe_get_kv_connector_output(
-                scheduler_output,
-                defer_finalize=defer_kv_connector_finalize,
+                scheduler_output, clear_metadata=clear_kv_metadata
             ) as kv_connector_output,
         ):
             model_output = self._model_forward(
@@ -534,11 +533,11 @@ class GPUARModelRunner(OmniGPUModelRunner):
             # tokens on the CPU, so they are run after bookkeeping.
             propose_draft_token_ids(valid_sampled_token_ids)
 
-        # Finalize KV connector (wait_for_save + clear metadata) after
-        # draft model runs. Deferred from target model forward to allow
-        # draft model to also save its KV cache.
+        # Clear KV connector metadata after draft model runs (if spec decode).
+        # This was deferred from target model forward to allow draft model
+        # to also save its KV cache.
         if self.speculative_config is not None:
-            self.finalize_kv_connector()
+            self.clear_kv_connector_metadata()
 
         with record_function_or_nullcontext("gpu_model_runner: eplb"):
             self.eplb_step()
diff --git a/vllm_omni/worker/gpu_model_runner.py b/vllm_omni/worker/gpu_model_runner.py
index 5a3d2f9ca..1b1d3d9cf 100644
--- a/vllm_omni/worker/gpu_model_runner.py
+++ b/vllm_omni/worker/gpu_model_runner.py
@@ -238,7 +238,8 @@ class OmniGPUModelRunner(GPUModelRunner):
             self.requests.pop(req_id, None)
             self.model_intermediate_buffer.pop(req_id, None)
             self.num_prompt_logprobs.pop(req_id, None)
-        self.late_interaction_runner.on_requests_finished(scheduler_output.finished_req_ids)
+        if hasattr(self, "late_interaction_runner"):
+            self.late_interaction_runner.on_requests_finished(scheduler_output.finished_req_ids)
         # Remove the finished requests from the persistent batch.
         # NOTE(woosuk): There could be an edge case where finished_req_ids and
         # scheduled_req_ids overlap. This happens when a request is aborted and
@@ -250,7 +251,7 @@ class OmniGPUModelRunner(GPUModelRunner):
 
         # Zero GPU memory for freshly allocated cache blocks to prevent
         # stale NaN/data from corrupting attention or SSM computation.
-        if scheduler_output.new_block_ids_to_zero:
+        if hasattr(scheduler_output, "new_block_ids_to_zero") and scheduler_output.new_block_ids_to_zero:
             self._zero_block_ids(scheduler_output.new_block_ids_to_zero)
 
         # Free the cached encoder outputs.
@@ -320,7 +321,8 @@ class OmniGPUModelRunner(GPUModelRunner):
                 lora_request=new_req_data.lora_request,
             )
             self.requests[req_id] = req_state
-            self.late_interaction_runner.register_request(req_id, pooling_params)
+            if hasattr(self, "late_interaction_runner"):
+                self.late_interaction_runner.register_request(req_id, pooling_params)
 
             # If prompt embeddings are provided, decode and attach to inter_data
             try: