Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner.

Key Features

Omni and Novel Architecture: We propose Thinker-Talker architecture, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. We propose a novel position embedding, named TMRoPE (Time-aligned Multimodal RoPE), to synchronize the timestamps of video inputs with audio.
Real-Time Voice and Video Chat: Architecture designed for fully real-time interactions, supporting chunked input and immediate output.
Natural and Robust Speech Generation: Surpassing many existing streaming and non-streaming alternatives, demonstrating superior robustness and naturalness in speech generation.
Strong Performance Across Modalities: Exhibiting exceptional performance across all modalities when benchmarked against similarly sized single-modality models. Qwen2.5-Omni outperforms the similarly sized Qwen2-Audio in audio capabilities and achieves comparable performance to Qwen2.5-VL-7B.
Excellent End-to-End Speech Instruction Following: Qwen2.5-Omni shows performance in end-to-end speech instruction following that rivals its effectiveness with text inputs, evidenced by benchmarks such as MMLU and GSM8K.

Model Architecture

Performance

We conducted a comprehensive evaluation of Qwen2.5-Omni, which demonstrates strong performance across all modalities when compared to similarly sized single-modality models and closed-source models like Qwen2.5-VL-7B, Qwen2-Audio, and Gemini-1.5-pro. In tasks requiring the integration of multiple modalities, such as OmniBench, Qwen2.5-Omni achieves state-of-the-art performance. Furthermore, in single-modality tasks, it excels in areas including speech recognition (Common Voice), translation (CoVoST2), audio understanding (MMAU), image reasoning (MMMU, MMStar), video understanding (MVBench), and speech generation (Seed-tts-eval and subjective naturalness).

Multimodality -> Text

Datasets	Model	Performance
OmniBench Speech \| Sound Event \| Music \| Avg	Gemini-1.5-Pro	42.67%\|42.26%\|46.23%\|42.91%
MIO-Instruct	36.96%\|33.58%\|11.32%\|33.80%
AnyGPT (7B)	17.77%\|20.75%\|13.21%\|18.04%
video-SALMONN	34.11%\|31.70%\|56.60%\|35.64%
UnifiedIO2-xlarge	39.56%\|36.98%\|29.25%\|38.00%
UnifiedIO2-xxlarge	34.24%\|36.98%\|24.53%\|33.98%
MiniCPM-o	-\|-\|-\|40.50%
Baichuan-Omni-1.5	-\|-\|-\|42.90%
Qwen2.5-Omni-3B	52.14%\|52.08%\|52.83%\|52.19%
Qwen2.5-Omni-7B	55.25%\|60.00%\|52.83%\|56.13%

Audio -> Text

Datasets	Model	Performance
ASR
Librispeech dev-clean \| dev other \| test-clean \| test-other	SALMONN	-\|-\|2.1\|4.9
SpeechVerse	-\|-\|2.1\|4.4
Whisper-large-v3	-\|-\|1.8\|3.6
Llama-3-8B	-\|-\|-\|3.4
Llama-3-70B	-\|-\|-\|3.1
Seed-ASR-Multilingual	-\|-\|1.6\|2.8
MiniCPM-o	-\|-\|1.7\|-
MinMo	-\|-\|1.7\|3.9
Qwen-Audio	1.8\|4.0\|2.0\|4.2
Qwen2-Audio	1.3\|3.4\|1.6\|3.6
Qwen2.5-Omni-3B	2.0\|4.1\|2.2\|4.5
Qwen2.5-Omni-7B	1.6\|3.5\|1.8\|3.4
Common Voice 15 en \| zh \| yue \| fr	Whisper-large-v3	9.3\|12.8\|10.9\|10.8
MinMo	7.9\|6.3\|6.4\|8.5
Qwen2-Audio	8.6\|6.9\|5.9\|9.6
Qwen2.5-Omni-3B	9.1\|6.0\|11.6\|9.6
Qwen2.5-Omni-7B	7.6\|5.2\|7.3\|7.5
Fleurs zh \| en	Whisper-large-v3	7.7\|4.1
Seed-ASR-Multilingual	-\|3.4
Megrez-3B-Omni	10.8\|-
MiniCPM-o	4.4\|-
MinMo	3.0\|3.8
Qwen2-Audio	7.5\|-
Qwen2.5-Omni-3B	3.2\|5.4
Qwen2.5-Omni-7B	3.0\|4.1
Wenetspeech test-net \| test-meeting	Seed-ASR-Chinese	4.7\|5.7
Megrez-3B-Omni	-\|16.4
MiniCPM-o	6.9\|-
MinMo	6.8\|7.4
Qwen2.5-Omni-3B	6.3\|8.1
Qwen2.5-Omni-7B	5.9\|7.7
Voxpopuli-V1.0-en	Llama-3-8B	6.2
Llama-3-70B	5.7
Qwen2.5-Omni-3B	6.6
Qwen2.5-Omni-7B	5.8
S2TT
CoVoST2 en-de \| de-en \| en-zh \| zh-en	SALMONN	18.6\|-\|33.1\|-
SpeechLLaMA	-\|27.1\|-\|12.3
BLSP	14.1\|-\|-\|-
MiniCPM-o	-\|-\|48.2\|27.2
MinMo	-\|39.9\|46.7\|26.0
Qwen-Audio	25.1\|33.9\|41.5\|15.7
Qwen2-Audio	29.9\|35.2\|45.2\|24.4
Qwen2.5-Omni-3B	28.3\|38.1\|41.4\|26.6
Qwen2.5-Omni-7B	30.2\|37.7\|41.4\|29.4
SER
Meld	WavLM-large	0.542
MiniCPM-o	0.524
Qwen-Audio	0.557
Qwen2-Audio	0.553
Qwen2.5-Omni-3B	0.558
Qwen2.5-Omni-7B	0.570
VSC
VocalSound	CLAP	0.495
Pengi	0.604
Qwen-Audio	0.929
Qwen2-Audio	0.939
Qwen2.5-Omni-3B	0.936
Qwen2.5-Omni-7B	0.939
Music
GiantSteps Tempo	Llark-7B	0.86
Qwen2.5-Omni-3B	0.88
Qwen2.5-Omni-7B	0.88
MusicCaps	LP-MusicCaps	0.291\|0.149\|0.089\|0.061\|0.129\|0.130
Qwen2.5-Omni-3B	0.325\|0.163\|0.093\|0.057\|0.132\|0.229
Qwen2.5-Omni-7B	0.328\|0.162\|0.090\|0.055\|0.127\|0.225
Audio Reasoning
MMAU Sound \| Music \| Speech \| Avg	Gemini-Pro-V1.5	56.75\|49.40\|58.55\|54.90
Qwen2-Audio	54.95\|50.98\|42.04\|49.20
Qwen2.5-Omni-3B	70.27\|60.48\|59.16\|63.30
Qwen2.5-Omni-7B	67.87\|69.16\|59.76\|65.60
Voice Chatting
VoiceBench AlpacaEval \| CommonEval \| SD-QA \| MMSU	Ultravox-v0.4.1-LLaMA-3.1-8B	4.55\|3.90\|53.35\|47.17
MERaLiON	4.50\|3.77\|55.06\|34.95
Megrez-3B-Omni	3.50\|2.95\|25.95\|27.03
Lyra-Base	3.85\|3.50\|38.25\|49.74
MiniCPM-o	4.42\|4.15\|50.72\|54.78
Baichuan-Omni-1.5	4.50\|4.05\|43.40\|57.25
Qwen2-Audio	3.74\|3.43\|35.71\|35.72
Qwen2.5-Omni-3B	4.32\|4.00\|49.37\|50.23
Qwen2.5-Omni-7B	4.49\|3.93\|55.71\|61.32
VoiceBench OpenBookQA \| IFEval \| AdvBench \| Avg	Ultravox-v0.4.1-LLaMA-3.1-8B	65.27\|66.88\|98.46\|71.45
MERaLiON	27.23\|62.93\|94.81\|62.91
Megrez-3B-Omni	28.35\|25.71\|87.69\|46.25
Lyra-Base	72.75\|36.28\|59.62\|57.66
MiniCPM-o	78.02\|49.25\|97.69\|71.69
Baichuan-Omni-1.5	74.51\|54.54\|97.31\|71.14
Qwen2-Audio	49.45\|26.33\|96.73\|55.35
Qwen2.5-Omni-3B	74.73\|42.10\|98.85\|68.81
Qwen2.5-Omni-7B	81.10\|52.87\|99.42\|74.12

Image -> Text

Dataset	Qwen2.5-Omni-7B	Qwen2.5-Omni-3B	Other Best	Qwen2.5-VL-7B	GPT-4o-mini
MMMU_val	59.2	53.1	53.9	58.6	60.0
MMMU-Pro_overall	36.6	29.7	-	38.3	37.6
MathVista_testmini	67.9	59.4	71.9	68.2	52.5
MathVision_full	25.0	20.8	23.1	25.1	-
MMBench-V1.1-EN_test	81.8	77.8	80.5	82.6	76.0
MMVet_turbo	66.8	62.1	67.5	67.1	66.9
MMStar	64.0	55.7	64.0	63.9	54.8
MME_sum	2340	2117	2372	2347	2003
MuirBench	59.2	48.0	-	59.2	-
CRPE_relation	76.5	73.7	-	76.4	-
RealWorldQA_avg	70.3	62.6	71.9	68.5	-
MME-RealWorld_en	61.6	55.6	-	57.4	-
MM-MT-Bench	6.0	5.0	-	6.3	-
AI2D	83.2	79.5	85.8	83.9	-
TextVQA_val	84.4	79.8	83.2	84.9	-
DocVQA_test	95.2	93.3	93.5	95.7	-
ChartQA_{test Avg}	85.3	82.8	84.9	87.3	-
OCRBench_V2_en	57.8	51.7	-	56.3	-

Dataset	Qwen2.5-Omni-7B	Qwen2.5-Omni-3B	Qwen2.5-VL-7B	Grounding DINO	Gemini 1.5 Pro
Refcoco_val	90.5	88.7	90.0	90.6	73.2
Refcoco_textA	93.5	91.8	92.5	93.2	72.9
Refcoco_textB	86.6	84.0	85.4	88.2	74.6
Refcoco+_val	85.4	81.1	84.2	88.2	62.5
Refcoco+_textA	91.0	87.5	89.1	89.0	63.9
Refcoco+_textB	79.3	73.2	76.9	75.9	65.0
Refcocog+_val	87.4	85.0	87.2	86.1	75.2
Refcocog+_test	87.9	85.1	87.2	87.0	76.2
ODinW	42.4	39.2	37.3	55.0	36.7
PointGrounding	66.5	46.2	67.3	-	-

Video(without audio) -> Text

Dataset	Qwen2.5-Omni-7B	Qwen2.5-Omni-3B	Other Best	Qwen2.5-VL-7B	GPT-4o-mini
Video-MME_{w/o sub}	64.3	62.0	63.9	65.1	64.8
Video-MME_{w sub}	72.4	68.6	67.9	71.6	-
MVBench	70.3	68.7	67.2	69.6	-
EgoSchema_test	68.6	61.4	63.2	65.0	-

Zero-shot Speech Generation

Datasets	Model	Performance
Content Consistency
SEED test-zh \| test-en \| test-hard	Seed-TTS_ICL	1.11 \| 2.24 \| 7.58
Seed-TTS_RL	1.00 \| 1.94 \| 6.42
MaskGCT	2.27 \| 2.62 \| 10.27
E2_TTS	1.97 \| 2.19 \| -
F5-TTS	1.56 \| 1.83 \| 8.67
CosyVoice 2	1.45 \| 2.57 \| 6.83
CosyVoice 2-S	1.45 \| 2.38 \| 8.08
Qwen2.5-Omni-3B_ICL	1.95 \| 2.87 \| 9.92
Qwen2.5-Omni-3B_RL	1.58 \| 2.51 \| 7.86
Qwen2.5-Omni-7B_ICL	1.70 \| 2.72 \| 7.97
Qwen2.5-Omni-7B_RL	1.42 \| 2.32 \| 6.54
Speaker Similarity
SEED test-zh \| test-en \| test-hard	Seed-TTS_ICL	0.796 \| 0.762 \| 0.776
Seed-TTS_RL	0.801 \| 0.766 \| 0.782
MaskGCT	0.774 \| 0.714 \| 0.748
E2_TTS	0.730 \| 0.710 \| -
F5-TTS	0.741 \| 0.647 \| 0.713
CosyVoice 2	0.748 \| 0.652 \| 0.724
CosyVoice 2-S	0.753 \| 0.654 \| 0.732
Qwen2.5-Omni-3B_ICL	0.741 \| 0.635 \| 0.748
Qwen2.5-Omni-3B_RL	0.744 \| 0.635 \| 0.746
Qwen2.5-Omni-7B_ICL	0.752 \| 0.632 \| 0.747
Qwen2.5-Omni-7B_RL	0.754 \| 0.641 \| 0.752

Text -> Text

Dataset	Qwen2.5-Omni-7B	Qwen2.5-Omni-3B	Qwen2.5-7B	Qwen2.5-3B	Qwen2-7B	Llama3.1-8B	Gemma2-9B
MMLU-Pro	47.0	40.4	56.3	43.7	44.1	48.3	52.1
MMLU-redux	71.0	60.9	75.4	64.4	67.3	67.2	72.8
LiveBench₀₈₃₁	29.6	22.3	35.9	26.8	29.2	26.7	30.6
GPQA	30.8	34.3	36.4	30.3	34.3	32.8	32.8
MATH	71.5	63.6	75.5	65.9	52.9	51.9	44.3
GSM8K	88.7	82.6	91.6	86.7	85.7	84.5	76.7
HumanEval	78.7	70.7	84.8	74.4	79.9	72.6	68.9
MBPP	73.2	70.4	79.2	72.7	67.2	69.6	74.9
MultiPL-E	65.8	57.6	70.4	60.2	59.1	50.7	53.4
LiveCodeBench_2305-2409	24.6	16.5	28.7	19.9	23.9	8.3	18.9

Quickstart

Below, we provide simple examples to show how to use Qwen2.5-Omni with 🤗 Transformers. The codes of Qwen2.5-Omni has been in the latest Hugging face transformers and we advise you to build from source with command:

pip uninstall transformers
pip install git+https://github.com/huggingface/transformers@v4.51.3-Qwen2.5-Omni-preview
pip install accelerate

or you might encounter the following error:

KeyError: 'qwen2_5_omni'

We offer a toolkit to help you handle various types of audio and visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved audio, images and videos. You can install it using the following command and make sure your system has ffmpeg installed:

bash

# It's highly recommended to use `[decord]` feature for faster video loading.
pip install qwen-omni-utils[decord] -U

If you are not using Linux, you might not be able to install decord from PyPI. In that case, you can use pip install qwen-omni-utils -U which will fall back to using torchvision for video processing. However, you can still install decord from source to get decord used when loading video.

🤗 Transformers Usage

Here we show a code snippet to show you how to use the chat model with transformers and qwen_omni_utils:

python

import soundfile as sf

from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
from qwen_omni_utils import process_mm_info

# default: Load the model on the available device(s)
model = Qwen2_5OmniForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B", torch_dtype="auto", device_map="auto")

# We recommend enabling flash_attention_2 for better acceleration and memory saving.
# model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen2.5-Omni-7B",
#     torch_dtype="auto",
#     device_map="auto",
#     attn_implementation="flash_attention_2",
# )

processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")

conversation = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-Omni/draw.mp4"},
        ],
    },
]

# set use audio in video
USE_AUDIO_IN_VIDEO = True

# Preparation for inference
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios, images, videos = process_mm_info(conversation, use_audio_in_video=USE_AUDIO_IN_VIDEO)
inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=USE_AUDIO_IN_VIDEO)
inputs = inputs.to(model.device).to(model.dtype)

# Inference: Generation of the output text and audio
text_ids, audio = model.generate(**inputs, use_audio_in_video=USE_AUDIO_IN_VIDEO)

text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(text)
sf.write(
    "output.wav",
    audio.reshape(-1).detach().cpu().numpy(),
    samplerate=24000,
)

Minimum GPU memory requirements

Model	Precision	15(s) Video	30(s) Video	60(s) Video
Qwen-Omni-3B	FP32	89.10 GB	Not Recommend	Not Recommend
Qwen-Omni-3B	BF16	18.38 GB	22.43 GB	28.22 GB
Qwen-Omni-7B	FP32	93.56 GB	Not Recommend	Not Recommend
Qwen-Omni-7B	BF16	31.11 GB	41.85 GB	60.19 GB

Note: The table above presents the theoretical minimum memory requirements for inference with transformers and BF16 is test with attn_implementation="flash_attention_2"; however, in practice, the actual memory usage is typically at least 1.2 times higher. For more information, see the linked resource here.

Video URL resource usage

Video URL compatibility largely depends on the third-party library version. The details are in the table below. Change the backend by FORCE_QWENVL_VIDEO_READER=torchvision or FORCE_QWENVL_VIDEO_READER=decord if you prefer not to use the default one.

Backend	HTTP	HTTPS
torchvision >= 0.19.0	✅	✅
torchvision < 0.19.0	❌	❌
decord	✅	❌

Batch inference

The model can batch inputs composed of mixed samples of various types such as text, images, audio and videos as input when return_audio=False is set. Here is an example.

python

# Sample messages for batch inference

# Conversation with video only
conversation1 = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "/path/to/video.mp4"},
        ]
    }
]

# Conversation with audio only
conversation2 = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "audio", "audio": "/path/to/audio.wav"},
        ]
    }
]

# Conversation with pure text
conversation3 = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
        ],
    },
    {
        "role": "user",
        "content": "who are you?"
    }
]


# Conversation with mixed media
conversation4 = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "/path/to/image.jpg"},
            {"type": "video", "video": "/path/to/video.mp4"},
            {"type": "audio", "audio": "/path/to/audio.wav"},
            {"type": "text", "text": "What are the elements can you see and hear in these medias?"},
        ],
    }
]

# Combine messages for batch processing
conversations = [conversation1, conversation2, conversation3, conversation4]

# set use audio in video
USE_AUDIO_IN_VIDEO = True

# Preparation for batch inference
text = processor.apply_chat_template(conversations, add_generation_prompt=True, tokenize=False)
audios, images, videos = process_mm_info(conversations, use_audio_in_video=USE_AUDIO_IN_VIDEO)

inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=USE_AUDIO_IN_VIDEO)
inputs = inputs.to(model.device).to(model.dtype)

# Batch Inference
text_ids = model.generate(**inputs, use_audio_in_video=USE_AUDIO_IN_VIDEO, return_audio=False)
text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(text)

Usage Tips

Prompt for audio output

If users need audio output, the system prompt must be set as "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.", otherwise the audio output may not work as expected.

{
    "role": "system",
    "content": [
        {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
    ],
}

Use audio in video

In the process of multimodal interaction, the videos provided by users are often accompanied by audio (such as questions about the content in the video, or sounds generated by certain events in the video). This information is conducive to the model providing a better interactive experience. So we provide the following options for users to decide whether to use audio in video.

python

# first place, in data preprocessing
audios, images, videos = process_mm_info(conversations, use_audio_in_video=True)

python

# second place, in model processor
inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", 
                   padding=True, use_audio_in_video=True)

python

#  third place, in model inference
text_ids, audio = model.generate(**inputs, use_audio_in_video=True)

It is worth noting that during a multi-round conversation, the use_audio_in_video parameter in these places must be set to the same, otherwise unexpected results will occur.

Use audio output or not

The model supports both text and audio outputs, if users do not need audio outputs, they can call model.disable_talker() after init the model. This option will save about ~2GB of GPU memory but the return_audio option for generate function will only allow to be set at False.

python

model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-Omni-7B",
    torch_dtype="auto",
    device_map="auto"
)
model.disable_talker()

In order to obtain a flexible experience, we recommend that users can decide whether to return audio when generate function is called. If return_audio is set to False, the model will only return text outputs to get text responses faster.

python

model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-Omni-7B",
    torch_dtype="auto",
    device_map="auto"
)
...
text_ids = model.generate(**inputs, return_audio=False)

Change voice type of output audio

Qwen2.5-Omni supports the ability to change the voice of the output audio. The "Qwen/Qwen2.5-Omni-7B" checkpoint support two voice types as follow:

Voice Type	Gender	Description
Chelsie	Female	A honeyed, velvety voice that carries a gentle warmth and luminous clarity.
Ethan	Male	A bright, upbeat voice with infectious energy and a warm, approachable vibe.

Users can use the speaker parameter of generate function to specify the voice type. By default, if speaker is not specified, the default voice type is Chelsie.

text_ids, audio = model.generate(**inputs, speaker="Chelsie")

text_ids, audio = model.generate(**inputs, speaker="Ethan")

Flash-Attention 2 to speed up generation

First, make sure to install the latest version of Flash Attention 2:

pip install -U flash-attn --no-build-isolation

Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the flash attention repository. FlashAttention-2 can only be used when a model is loaded in torch.float16 or torch.bfloat16.

To load and run a model using FlashAttention-2, add attn_implementation="flash_attention_2" when loading the model:

python

from transformers import Qwen2_5OmniForConditionalGeneration

model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-Omni-7B",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)

Citation

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BibTeX

@article{Qwen2.5-Omni,
  title={Qwen2.5-Omni Technical Report},
  author={Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, Junyang Lin},
  journal={arXiv preprint arXiv:2503.20215},
  year={2025}
}