diff --git a/vllm/model_executor/models/qwen3_omni_moe_thinker.py b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
index b8b5e893d154..de8027c434cc 100755
--- a/vllm/model_executor/models/qwen3_omni_moe_thinker.py
+++ b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
@@ -750,18 +750,20 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
             # https://github.com/huggingface/transformers/pull/41473
             mm_kwargs = dict(mm_kwargs)
             tok_kwargs = dict(tok_kwargs)
+            mm_kwargs["audio_kwargs"] = dict(mm_kwargs.get("audio_kwargs") or {})
+            mm_kwargs["text_kwargs"] = dict(mm_kwargs.get("text_kwargs") or {})
             if Version(TRANSFORMERS_VERSION) < Version("4.58.0"):
                 # Extract audio_sample_rate before restructuring
                 audio_sample_rate = mm_kwargs.pop("audio_sample_rate", None)
 
                 # move truncation to audio_kwargs level to avoid conflict
                 # with tok_kwargs
-                mm_kwargs["audio_kwargs"] = {
-                    "truncation": mm_kwargs.pop("truncation", False)
-                }
-                mm_kwargs["text_kwargs"] = {
-                    "truncation": tok_kwargs.pop("truncation", False)
-                }
+                mm_kwargs["audio_kwargs"].setdefault(
+                    "truncation", mm_kwargs.pop("truncation", False)
+                )
+                mm_kwargs["text_kwargs"].setdefault(
+                    "truncation", tok_kwargs.pop("truncation", False)
+                )
 
                 # Validate and conditionally pass audio_sample_rate
                 # WhisperFeatureExtractor has a fixed sampling rate, and vLLM's