Instructions to use BAAI/AREX-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BAAI/AREX-2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="BAAI/AREX-2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("BAAI/AREX-2")
model = AutoModelForMultimodalLM.from_pretrained("BAAI/AREX-2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BAAI/AREX-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BAAI/AREX-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AREX-2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/BAAI/AREX-2

SGLang

How to use BAAI/AREX-2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BAAI/AREX-2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AREX-2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BAAI/AREX-2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AREX-2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use BAAI/AREX-2 with Docker Model Runner:
```
docker model run hf.co/BAAI/AREX-2
```

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@@ -17,7 +17,7 @@ tags:
 <div align="center">
   <img src="assets/arex-logo.png" width="40%" alt="AREX" style="display:block;margin:0 auto -2px;">
   <strong>AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks</strong><br>
-  <a href="https://huggingface.co/BAAI/AREX-2/resolve/main/arex2_paper.pdf"><img src="https://img.shields.io/badge/-Paper-B31B1B?style=for-the-badge&amp;logo=arxiv&amp;logoColor=white" alt="Paper"></a>
   <a href="https://github.com/VectorSpaceLab/AREX-2"><img src="https://img.shields.io/badge/-Homepage-24292F?style=for-the-badge&amp;logo=github&amp;logoColor=white" alt="Homepage"></a>
 </div>
@@ -188,18 +188,11 @@ and notices for the Qwen base model and any downstream data or tools.
 ## Citation
 ```bibtex
-@misc{baai2026arex,
-  title={AREX: Towards a Recursively Self-Improving Agent for Deep Research},
-  author={Lu, Shuqi and Li, Chaofan and Luo, Kun and Zhang, Zhang and Wang, Hui
-          and Xiao, Hongwang and Xiong, Lei and Wang, Jiahao and Wang, Sen
-          and Jiang, Xiyan and Li, Wanli and Hu, Yuyang and Qian, Hongjin
-          and Yan, Bingyu and Xia, Ziyi and Shao, Yingxia and Liu, Kang
-          and Dou, Zhicheng and He, Di and Li, Chaozhuo and Ye, Qiwei
-          and Wang, Zhongyuan and Liu, Zheng},
-  year={2026},
-  eprint={2607.21461},
-  archivePrefix={arXiv},
-  primaryClass={cs.AI},
-  url={https://arxiv.org/abs/2607.21461}
 }
 ```

 <div align="center">
   <img src="assets/arex-logo.png" width="40%" alt="AREX" style="display:block;margin:0 auto -2px;">
   <strong>AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks</strong><br>
+  <a href="https://arxiv.org/abs/2609.38288"><img src="https://img.shields.io/badge/-Paper-B31B1B?style=for-the-badge&amp;logo=arxiv&amp;logoColor=white" alt="Paper"></a>
   <a href="https://github.com/VectorSpaceLab/AREX-2"><img src="https://img.shields.io/badge/-Homepage-24292F?style=for-the-badge&amp;logo=github&amp;logoColor=white" alt="Homepage"></a>
 </div>
 ## Citation
 ```bibtex
+@article{2026arex2,
+  title   = {AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks},
+  author  = {Qian, Hongjin and Li, Chaofan and Luo, Kun and Wei, Wenqing and Chen, Jianlyu and Lu, Shuqi and Hu, Yuyang and Xiao, Hongwang and Wang, Hui and Li, Chaozhuo and Ye, Qiwei and Dou, Zhicheng and Lian, Defu and Liu, Zheng},
+  journal = {arXiv preprint arXiv:2609.38288},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2609.38288}
 }
 ```