diff --git a/tests/e2e/offline_inference/test_dots_tts_expansion.py b/tests/e2e/offline_inference/test_dots_tts_expansion.py
new file mode 100644
index 000000000..1c9362a23
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+++ b/tests/e2e/offline_inference/test_dots_tts_expansion.py
@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""E2E tests for dots.tts native AR offline inference."""
+
+from collections.abc import Mapping
+
+import pytest
+import torch
+
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import OmniRunner
+from tests.helpers.stage_config import get_deploy_config_path
+
+DOTS_TTS_MODEL = "rednote-hilab/dots.tts-soar"
+DEPLOY_CONFIG = get_deploy_config_path("dots_tts.yaml")
+SAMPLE_RATE = 48000
+
+# dots.tts ships a custom tokenizer carrying the added ``<|audio_gen_start|>``
+# token, so remote code must be explicitly enabled.
+_OMNI_RUNNER_PARAM = (DOTS_TTS_MODEL, DEPLOY_CONFIG, {"trust_remote_code": True})
+
+pytestmark = pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True)
+
+
+def _build_prompt(omni_runner: OmniRunner, text: str) -> dict:
+    """Wrap ``text`` in the prefix scaffold dots.tts prefill expects.
+
+    Unlike most TTS models here, a bare ``{"prompt": ...}`` string does not
+    work: prefill needs ``[文本]<text>[文本对应语音]<|audio_gen_start|>`` as
+    real Qwen2 token ids.
+    """
+    from transformers import AutoTokenizer
+
+    from vllm_omni.model_executor.models.dots_tts.dots_tts_prompt import (
+        build_dots_tts_prompt,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(omni_runner.model_name, trust_remote_code=True)
+    return build_dots_tts_prompt(tokenizer=tokenizer, text=text)
+
+
+def _extract_audio(multimodal_output: dict) -> torch.Tensor:
+    """Extract the final complete audio tensor from multimodal output."""
+    assert isinstance(multimodal_output, (dict, Mapping)), f"Expected dict/Mapping, got {type(multimodal_output)}"
+
+    # The talker emits delta chunks; the output processor accumulates them
+    # under "model_outputs" ("audio" kept for backwards compat).
+    audio = multimodal_output.get("model_outputs")
+    if audio is None:
+        audio = multimodal_output.get("audio")
+    assert audio is not None, f"No audio key, got {list(multimodal_output.keys())}"
+
+    if isinstance(audio, list):
+        valid = [torch.as_tensor(x).float().cpu().reshape(-1) for x in audio if x is not None]
+        assert valid, "No valid audio tensors in output list"
+        audio = torch.cat(valid, dim=0) if len(valid) > 1 else valid[0]
+
+    assert isinstance(audio, torch.Tensor), f"Expected Tensor, got {type(audio)}"
+    return audio.float().cpu().reshape(-1)
+
+
+@pytest.mark.slow
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+def test_dots_tts_zero_shot_001(omni_runner: OmniRunner) -> None:
+    """Zero-shot synthesis produces audio of a plausible duration."""
+    prompt = _build_prompt(omni_runner, "Hello, this is a test of dots TTS running on vLLM Omni.")
+
+    outputs = omni_runner.omni.generate([prompt])
+    assert len(outputs) == 1
+
+    audio = _extract_audio(outputs[0].outputs[0].multimodal_output)
+    duration_s = audio.shape[0] / SAMPLE_RATE
+    assert 0.5 < duration_s < 30.0, f"Audio duration out of range: {duration_s:.2f}s"
+    assert torch.isfinite(audio).all(), "Audio contains NaN/Inf"
+    assert audio.abs().max() > 1e-3, "Audio is silent"
+
+
+@pytest.mark.slow
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+def test_dots_tts_prefill_decode_mixed_batch_002(omni_runner: OmniRunner) -> None:
+    """Mixed prefill+decode batches must keep per-request rows aligned.
+
+    ``compute_logits`` fills ``logits[i]`` positionally from
+    ``_results_queue``, so every request in the batch has to push exactly one
+    entry per step, prefills included. When the prefill branch pushed nothing,
+    a shorter request's stop signal could be read into another request's row.
+    Regression guard for that pairing (PR #4765 review).
+    """
+    long_text = (
+        "This is a deliberately long sentence that stays in the decode phase "
+        "for many steps, so the shorter requests behind it keep entering "
+        "prefill alongside it and reproduce the mixed batch pattern."
+    )
+    short_texts = ["Hello one.", "Hello two.", "Hello three.", "Hello four."]
+    prompts = [_build_prompt(omni_runner, long_text)] + [_build_prompt(omni_runner, t) for t in short_texts]
+
+    outputs = omni_runner.omni.generate(prompts)
+    assert len(outputs) == len(prompts)
+
+    for i, out in enumerate(outputs):
+        audio = _extract_audio(out.outputs[0].multimodal_output)
+        duration_s = audio.shape[0] / SAMPLE_RATE
+        assert 0.1 < duration_s < 30.0, f"Request {i} audio duration out of range: {duration_s:.2f}s"
+
+
+@pytest.mark.slow
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+def test_dots_tts_deterministic_flow_matching_noise_003(omni_runner: OmniRunner) -> None:
+    """The same text twice must yield the same audio.
+
+    The DiT flow-matching noise is drawn from a per-request generator seeded
+    from ``blake2b(seed:request_key:noise_step)`` rather than the global CUDA
+    RNG. Drawing from the global RNG made output non-reproducible and let
+    concurrent requests perturb each other's noise streams (PR #4765 review).
+    """
+    text = "Reading aloud every evening helps children build a lasting love of language."
+
+    first = _extract_audio(
+        omni_runner.omni.generate([_build_prompt(omni_runner, text)])[0].outputs[0].multimodal_output
+    )
+    second = _extract_audio(
+        omni_runner.omni.generate([_build_prompt(omni_runner, text)])[0].outputs[0].multimodal_output
+    )
+
+    assert first.shape == second.shape, f"Length differs across runs: {first.shape} vs {second.shape}"
+    assert torch.allclose(first, second, atol=1e-4), "Audio differs across runs; flow-matching noise is not seeded"