diff --git a/tests/test_vllm_018_compat.py b/tests/test_vllm_018_compat.py
new file mode 100644
index 0000000..921ffba
--- /dev/null
+++ b/tests/test_vllm_018_compat.py
@@ -0,0 +1,47 @@
+"""Regression tests for vLLM 0.18 compatibility in the BART processor."""
+
+import torch
+
+
+def test_text_data_parser_handles_v018_empty_inputs():
+    from vllm_bart_plugin.bart import TextDataParser
+
+    parser = TextDataParser()
+
+    assert parser._parse_text_data("") is None
+    assert parser._parse_text_data([]) is None
+
+
+def test_create_encoder_prompt_uses_placeholder_token():
+    from vllm_bart_plugin.bart import BartMultiModalProcessor
+
+    processor = BartMultiModalProcessor.__new__(BartMultiModalProcessor)
+
+    assert processor.create_encoder_prompt("<s>decoder text", {"texts": ["encoder text"]}) == [0]
+
+
+def test_call_hf_processor_accepts_pretokenized_decoder_prompt():
+    from vllm_bart_plugin.bart import BartMultiModalProcessor
+
+    class FakeTokenizer:
+        def __call__(self, text, return_tensors="pt", **kwargs):
+            if text == "encoder text":
+                return {"input_ids": torch.tensor([[11, 12, 13]])}
+            return {"input_ids": torch.tensor([[21, 22]])}
+
+    class FakeInfo:
+        def get_tokenizer(self):
+            return FakeTokenizer()
+
+    processor = BartMultiModalProcessor.__new__(BartMultiModalProcessor)
+    processor.info = FakeInfo()
+
+    out = processor._call_hf_processor(
+        [7, 8, 9],
+        {"texts": ["encoder text"]},
+        {},
+        {},
+    )
+
+    assert torch.equal(out["encoder_input_ids"], torch.tensor([[11, 12, 13]]))
+    assert torch.equal(out["input_ids"], torch.tensor([[7, 8, 9]]))
diff --git a/vllm_bart_plugin/bart.py b/vllm_bart_plugin/bart.py
index fcced7d..62cca02 100644
--- a/vllm_bart_plugin/bart.py
+++ b/vllm_bart_plugin/bart.py
@@ -996,7 +996,10 @@ def _parse_text_data(
         if data is None:
             return TextProcessorItems(None)
 
-        if self._is_empty(data):
+        # _is_empty was removed in vLLM >=0.18; handle emptiness inline
+        if isinstance(data, str) and not data:
+            return None
+        if isinstance(data, list) and len(data) == 0:
             return None
 
         # Text data should be a string or list of strings
@@ -1030,15 +1033,11 @@ def create_encoder_prompt(
         prompt: str | list[int],
         mm_data: MultiModalDataDict,
     ) -> str | list[int]:
-        if not prompt:
-            return [0]
-        tokenizer = self.info.get_tokenizer()
-        tokens = tokenizer(
-            prompt,
-            add_special_tokens=False,
-            return_tensors="pt",
-        )["input_ids"].flatten()
-        return tokens.tolist()
+        # In vLLM >=0.18, `prompt` here is the DECODER prompt text, not the
+        # encoder text. The encoder content lives in mm_data ("text" key).
+        # Always return [0] as a single placeholder token; _get_prompt_updates
+        # will replace it with the correct number of encoder token slots.
+        return [0]
 
     def create_decoder_prompt(
         self,
@@ -1079,14 +1078,19 @@ def _call_hf_processor(
             )
             result["encoder_input_ids"] = encoder_tokenized["input_ids"]
 
-        # Always tokenize the prompt (for decoder or as dummy)
-        # This will be popped by the base class
-        prompt_tokenized = tokenizer(
-            prompt if prompt else "",
-            return_tensors="pt",
-            **tok_kwargs,
-        )
-        result["input_ids"] = prompt_tokenized["input_ids"]
+        # Always produce input_ids for the decoder prompt.
+        # In vLLM >=0.18 the rendering pipeline may call _call_hf_processor
+        # with an already-tokenized prompt (a list of ints) instead of a str.
+        # Handle both cases.
+        if isinstance(prompt, (list, tuple)) and len(prompt) > 0 and isinstance(prompt[0], int):
+            result["input_ids"] = torch.tensor([prompt])
+        else:
+            prompt_tokenized = tokenizer(
+                prompt if prompt else "",
+                return_tensors="pt",
+                **tok_kwargs,
+            )
+            result["input_ids"] = prompt_tokenized["input_ids"]
 
         return BatchFeature(result)