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

Libraries

How to use cfli/AREX-2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="cfli/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("cfli/AREX-2")
model = AutoModelForMultimodalLM.from_pretrained("cfli/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 cfli/AREX-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cfli/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": "cfli/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/cfli/AREX-2

SGLang

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

AREX-2 / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3.8-27B
	tags:
	- qwen3_5
	- agent
	- deep-research
	- reasoning
	- tool-use
	- long-context
	- self-improvement
	- safetensors
	---

	<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/2607.21461"><img src="https://img.shields.io/badge/-Paper-B31B1B?style=for-the-badge&logo=arxiv&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&logo=github&logoColor=white" alt="Homepage"></a>
	</div>

	## Introduction

	AREX-2 is a 27B-parameter long-horizon agent model from the Beijing Academy of
	Artificial Intelligence (BAAI). It learns to improve a solution over multiple
	test-time rounds: propose, measure, reflect, and revise.

	AREX-2 is trained on machine-learning and algorithmic-programming tasks with
	verifiable feedback, together with the existing AREX deep-research data. The
	learned self-improvement behavior transfers to deep research without adding new
	search trajectories.

	- Architecture: Dense Qwen3.8-compatible multimodal model
	- Parameters: 27B
	- Context length: 262,144 tokens

	## Key features

	- Long-horizon self-improvement: turns extra test-time rounds into useful
	solution refinement.
	- Feedback-driven reflection: reads scores, logs, errors, and timings to
	decide what to change next.
	- Cross-domain performance: training on coding and machine-learning tasks
	also improves the model's deep-research performance.
	- Long-horizon reasoning: sustains productive iteration as the task budget
	grows.

	## Evaluation

	AREX-2 is evaluated on algorithmic programming, machine-learning engineering,
	deep research, and general agentic reasoning. Results follow the protocols
	reported in the AREX-2 paper.

	### Coding and machine-learning engineering

	<table cellpadding="7" style="display:table!important;overflow:visible!important;width:auto;max-width:100%;border:0!important;border-radius:0!important;border-collapse:collapse!important;font-size:13px;table-layout:auto">
	<thead>
	<tr style="color:#0F766E;border-bottom:2px solid #0F766E">
	<th style="padding:8px;text-align:left">Model</th>
	<th style="padding:8px;text-align:center">Params</th>
	<th style="padding:8px;text-align:center">Frontier-CS</th>
	<th style="padding:8px;text-align:center">MLE-Lite</th>
	</tr>
	</thead>
	<tbody>
	<tr><td colspan="4" style="padding:8px 10px;font-weight:600;color:#0F766E;background:rgba(15,118,110,.1)">Closed-weight models</td></tr>
	<tr><td>GPT-5.6 Sol</td><td style="text-align:center">-</td><td style="text-align:center">76.4</td><td style="text-align:center">72.7</td></tr>
	<tr><td>Claude Opus 4.8</td><td style="text-align:center">-</td><td style="text-align:center">74.5</td><td style="text-align:center">63.6</td></tr>
	<tr><td>GPT-5.5</td><td style="text-align:center">-</td><td style="text-align:center">72.1</td><td style="text-align:center">68.2</td></tr>
	<tr><td>Gemini-3.1-Pro</td><td style="text-align:center">-</td><td style="text-align:center">68.9</td><td style="text-align:center">-</td></tr>
	<tr><td>Qwen3.7-Max</td><td style="text-align:center">-</td><td style="text-align:center">61.9</td><td style="text-align:center">-</td></tr>
	<tr><td colspan="4" style="padding:8px 10px;font-weight:600;color:#0F766E;background:rgba(15,118,110,.1)">Open-weight models</td></tr>
	<tr><td>Kimi-K3</td><td style="text-align:center">2.8T</td><td style="text-align:center">-</td><td style="text-align:center">72.7</td></tr>
	<tr><td>Naive-N0.5-Flash</td><td style="text-align:center">309B</td><td style="text-align:center">-</td><td style="text-align:center">73.7</td></tr>
	<tr><td>DeepSeek-V4-Pro</td><td style="text-align:center">1.6T</td><td style="text-align:center">44.7</td><td style="text-align:center">54.5</td></tr>
	<tr><td>DeepSeek-V4-Flash</td><td style="text-align:center">284B</td><td style="text-align:center">39.1</td><td style="text-align:center">51.5</td></tr>
	<tr><td>Kimi-K2.7-Code</td><td style="text-align:center">1T</td><td style="text-align:center">54.7</td><td style="text-align:center">-</td></tr>
	<tr><td>GLM-5.3-Flash</td><td style="text-align:center">320B</td><td style="text-align:center">50.4</td><td style="text-align:center">-</td></tr>
	<tr><td>Kimi-K2.6</td><td style="text-align:center">1T</td><td style="text-align:center">46.9</td><td style="text-align:center">66.7</td></tr>
	<tr><td>Frontis-MA1-35B</td><td style="text-align:center">35B</td><td style="text-align:center">-</td><td style="text-align:center">71.2</td></tr>
	<tr><td>BigBang-V1</td><td style="text-align:center">35B</td><td style="text-align:center">-</td><td style="text-align:center">59.1</td></tr>
	<tr><td>Qwen3.6-35B-A3B</td><td style="text-align:center">35B</td><td style="text-align:center">23.4</td><td style="text-align:center">39.4</td></tr>
	<tr style="background:rgba(15,118,110,.08);font-weight:600;color:#0F766E"><td style="padding:7px 10px">AREX-2</td><td style="text-align:center">27B</td><td style="text-align:center">70.7</td><td style="text-align:center">81.8</td></tr>
	</tbody>
	</table>

	<small>Frontier-CS is the 188-task Agent Track. MLE-Lite reports Any Medal
	averaged over three seeds.</small>

	### General agentic reasoning and deep research

	<table cellpadding="7" style="display:table!important;overflow:visible!important;width:auto;max-width:100%;border:0!important;border-radius:0!important;border-collapse:collapse!important;font-size:13px;table-layout:auto">
	<thead>
	<tr style="color:#0F766E;border-bottom:2px solid #0F766E">
	<th style="padding:8px;text-align:left">Model</th>
	<th style="padding:8px;text-align:center">Params</th>
	<th style="padding:8px;text-align:center">BrowseComp</th>
	<th style="padding:8px;text-align:center">HLE</th>
	<th style="padding:8px;text-align:center">GAIA</th>
	<th style="padding:8px;text-align:center">DeepSearchQA</th>
	</tr>
	</thead>
	<tbody>
	<tr><td colspan="6" style="padding:8px 10px;font-weight:600;color:#0F766E;background:rgba(15,118,110,.1)">Frontier models</td></tr>
	<tr><td>GPT-5.6 Sol</td><td style="text-align:center">-</td><td style="text-align:center">90.4</td><td style="text-align:center">58.0*</td><td style="text-align:center">-</td><td style="text-align:center">-</td></tr>
	<tr><td>GPT-5.6 Terra</td><td style="text-align:center">-</td><td style="text-align:center">87.5</td><td style="text-align:center">-</td><td style="text-align:center">-</td><td style="text-align:center">-</td></tr>
	<tr><td>GPT-5.6 Luna</td><td style="text-align:center">-</td><td style="text-align:center">83.3</td><td style="text-align:center">-</td><td style="text-align:center">-</td><td style="text-align:center">-</td></tr>
	<tr><td>Kimi-K3</td><td style="text-align:center">2.8T</td><td style="text-align:center">91.2</td><td style="text-align:center">56.0*</td><td style="text-align:center">-</td><td style="text-align:center">95.0</td></tr>
	<tr><td>Claude Fable 5</td><td style="text-align:center">-</td><td style="text-align:center">88.0</td><td style="text-align:center">64.5*</td><td style="text-align:center">-</td><td style="text-align:center">94.2</td></tr>
	<tr><td>Claude Opus 4.8</td><td style="text-align:center">-</td><td style="text-align:center">84.3</td><td style="text-align:center">57.9*</td><td style="text-align:center">-</td><td style="text-align:center">93.1</td></tr>
	<tr><td>GPT-5.5</td><td style="text-align:center">-</td><td style="text-align:center">84.4</td><td style="text-align:center">52.2*</td><td style="text-align:center">87.4</td><td style="text-align:center">-</td></tr>
	<tr><td>Gemini-3.1-Pro</td><td style="text-align:center">-</td><td style="text-align:center">85.9</td><td style="text-align:center">51.4*</td><td style="text-align:center">80.6</td><td style="text-align:center">93.3</td></tr>
	<tr><td colspan="6" style="padding:8px 10px;font-weight:600;color:#0F766E;background:rgba(15,118,110,.1)">Large models (>40B)</td></tr>
	<tr><td>GLM-5</td><td style="text-align:center">744B</td><td style="text-align:center">75.9</td><td style="text-align:center">50.4</td><td style="text-align:center">70.0</td><td style="text-align:center">-</td></tr>
	<tr><td>Kimi-K2.6</td><td style="text-align:center">1T</td><td style="text-align:center">83.2</td><td style="text-align:center">54.0*</td><td style="text-align:center">80.6</td><td style="text-align:center">92.5</td></tr>
	<tr><td>GLM-5.3-Flash</td><td style="text-align:center">320B</td><td style="text-align:center">-</td><td style="text-align:center">55.3*</td><td style="text-align:center">78.8</td><td style="text-align:center">-</td></tr>
	<tr><td>DeepSeek-V4-Flash</td><td style="text-align:center">284B</td><td style="text-align:center">73.2</td><td style="text-align:center">45.1</td><td style="text-align:center">57.5</td><td style="text-align:center">90.6</td></tr>
	<tr><td>DeepSeek-V4-Pro</td><td style="text-align:center">1.6T</td><td style="text-align:center">83.4</td><td style="text-align:center">48.2</td><td style="text-align:center">71.1</td><td style="text-align:center">88.7</td></tr>
	<tr><td>MiroThinker-1.7</td><td style="text-align:center">235B</td><td style="text-align:center">74.0</td><td style="text-align:center">42.9</td><td style="text-align:center">82.7</td><td style="text-align:center">72.1</td></tr>
	<tr><td>XYZ-Aquila-pro</td><td style="text-align:center">397B</td><td style="text-align:center">84.8</td><td style="text-align:center">53.3</td><td style="text-align:center">-</td><td style="text-align:center">92.5</td></tr>
	<tr><td>Iris-pro</td><td style="text-align:center">397B</td><td style="text-align:center">88.6</td><td style="text-align:center">56.4</td><td style="text-align:center">-</td><td style="text-align:center">92.9</td></tr>
	<tr><td>AREX-Base</td><td style="text-align:center">122B</td><td style="text-align:center">82.5</td><td style="text-align:center">52.4</td><td style="text-align:center">85.4</td><td style="text-align:center">89.9</td></tr>
	<tr><td colspan="6" style="padding:8px 10px;font-weight:600;color:#0F766E;background:rgba(15,118,110,.1)">Small models (≤40B)</td></tr>
	<tr><td>Tongyi-DeepResearch-30B</td><td style="text-align:center">30B</td><td style="text-align:center">43.4</td><td style="text-align:center">32.9</td><td style="text-align:center">70.9</td><td style="text-align:center">-</td></tr>
	<tr><td>Qwen3.5-35B</td><td style="text-align:center">35B</td><td style="text-align:center">61.0</td><td style="text-align:center">47.4</td><td style="text-align:center">80.0</td><td style="text-align:center">68.5</td></tr>
	<tr><td>XYZ-Aquila-mini</td><td style="text-align:center">35B</td><td style="text-align:center">78.8</td><td style="text-align:center">51.1</td><td style="text-align:center">97.1</td><td style="text-align:center">89.5</td></tr>
	<tr><td>BigBang-V1</td><td style="text-align:center">35B</td><td style="text-align:center">76.5</td><td style="text-align:center">50.3</td><td style="text-align:center">-</td><td style="text-align:center">-</td></tr>
	<tr><td>Quest-35B</td><td style="text-align:center">35B</td><td style="text-align:center">64.6</td><td style="text-align:center">37.2</td><td style="text-align:center">80.8</td><td style="text-align:center">-</td></tr>
	<tr><td>Apodex-1.0-mini</td><td style="text-align:center">35B</td><td style="text-align:center">71.5</td><td style="text-align:center">46.8</td><td style="text-align:center">-</td><td style="text-align:center">82.2</td></tr>
	<tr><td>Agents-A1</td><td style="text-align:center">35B</td><td style="text-align:center">75.5</td><td style="text-align:center">47.6</td><td style="text-align:center">96.0</td><td style="text-align:center">-</td></tr>
	<tr><td>MiroThinker-1.7-mini</td><td style="text-align:center">30B</td><td style="text-align:center">67.9</td><td style="text-align:center">36.4</td><td style="text-align:center">80.3</td><td style="text-align:center">67.9</td></tr>
	<tr><td>Iris-mini</td><td style="text-align:center">35B</td><td style="text-align:center">82.2</td><td style="text-align:center">52.3</td><td style="text-align:center">-</td><td style="text-align:center">86.9</td></tr>
	<tr><td>AREX-Turbo</td><td style="text-align:center">4B</td><td style="text-align:center">70.7</td><td style="text-align:center">40.6</td><td style="text-align:center">81.6</td><td style="text-align:center">78.5</td></tr>
	<tr style="background:rgba(15,118,110,.12);font-weight:700;color:#0F766E"><td>AREX-2</td><td style="text-align:center">27B</td><td style="text-align:center">84.0</td><td style="text-align:center">52.6</td><td style="text-align:center">92.2</td><td style="text-align:center">93.8</td></tr>
	</tbody>
	</table>

	<small>HLE values marked * are from the full HLE set; unmarked values use the
	text-only subset.</small>

	## Inference

	Use a recent Transformers release with Qwen3.8 support.

	```bash
	pip install -U torch transformers accelerate
	```

	```python
	import torch
	from transformers import AutoModelForMultimodalLM, AutoProcessor

	model_id = "BAAI/AREX-2"
	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForMultimodalLM.from_pretrained(
	model_id, dtype=torch.bfloat16, device_map="auto"
	)

	messages = [{
	"role": "user",
	"content": "Propose a solution and explain how you would improve it over several rounds.",
	}]
	inputs = processor.apply_chat_template(
	messages, add_generation_prompt=True, tokenize=True,
	return_dict=True, return_tensors="pt",
	).to(model.device)

	with torch.inference_mode():
	outputs = model.generate(**inputs, max_new_tokens=1024)
	print(processor.decode(
	outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True
	))
	```

	## Intended use

	AREX-2 is intended for research on long-horizon agents, iterative problem
	solving, machine-learning engineering, algorithmic coding, and tool-augmented
	deep research.

	## License

	AREX-2 is released under the [Apache License 2.0](LICENSE). Follow the terms
	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}
	}
	```