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

Libraries

How to use BAAI/AREX-Turbo with Transformers:

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

pipe = pipeline("text-generation", model="BAAI/AREX-Turbo")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("BAAI/AREX-Turbo")
model = AutoModelForMultimodalLM.from_pretrained("BAAI/AREX-Turbo", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BAAI/AREX-Turbo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BAAI/AREX-Turbo"
# 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-Turbo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use BAAI/AREX-Turbo 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-Turbo" \
    --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-Turbo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Turbo" \
        --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-Turbo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Towards a Recursively Self-Improving Agent for Deep Research

Introduction

AREX is a family of deep research agents developed by the Beijing Academy of Artificial Intelligence (BAAI). It is designed for long-horizon tasks in which an agent must search across sources, assemble candidate answers, verify multiple constraints, and revise its research plan when the available evidence is incomplete.

AREX follows a recursive self-improvement framework consisting of an inner research loop and an outer self-improvement loop. The inner loop conducts deep research, while the outer loop evaluates and improves the research process.

This repository contains AREX-Turbo, the compact model in the AREX family. It is a dense 4B model built on Qwen3.5-4B and follows the same recursive deep research framework as AREX-Base. AREX-Turbo targets lower-cost research-agent deployment while retaining AREX's core research, verification, and context-management behavior.

Architecture: Dense
Parameters: 4B
Context length: 262,144 tokens

Key Features

Compact research agent: provides AREX capabilities in a dense 4B model for lower-cost serving and faster iteration.
Recursive self-improvement: evaluates provisional answers and chooses whether to accept, refine, or restart the research trajectory.
Verification-guided research: converts unresolved answer constraints into focused follow-up searches.
Autonomous context updates: retains verified findings, source identifiers, rejected candidates, open constraints, and the next research plan.
Long-horizon tool use: supports multi-round search, browsing, evidence integration, and answer construction.

Model Family

Model	Backbone	Context	Positioning
AREX-Turbo	Qwen3.5-4B	256K	Compact model for lower-cost research and tool-use settings.
AREX-Base	Qwen3.5-122B-A10B	256K	Higher-capacity model with the strongest overall AREX results.

Method

AREX organizes deep research as two interacting loops supported by autonomous context management.

Inner research loop. The agent searches, reads, integrates evidence, tracks candidates, and produces a provisional answer with supporting evidence and an answer-level confidence score.
Outer self-improvement loop. The agent checks the provisional answer against the original constraints. High-confidence answers are accepted. Recoverable trajectories are refined around unresolved claims, while uninformative trajectories can be restarted.
Autonomous context update. The model invokes update_context to refresh its research state around verified findings, current and rejected candidates, unresolved constraints, source validity, and the next research plan.

This design preserves useful work across iterations and reduces repeated exploration of paths that have already been rejected.

Evaluation

AREX is evaluated through a unified long-horizon search-agent interface with search, visit, update_context, and finish tools. HLE with tools additionally provides a Python tool.

Model	Total Params	BrowseComp	GAIA	xbench- 2510	DeepSearch QA	WideSearch- en	HLE w/ tools
Frontier models
GPT-5.4	-	82.7	-	-	88.5	77.5	52.1*
Opus-4.6	-	83.7	-	-	91.3	77.5	53.0*
Gemini-3.1-Pro	-	85.9	80.6	53.0	93.3	66.4	51.4*
Open-source models
GLM-5	744B	75.9	70.0	-	-	69.8	50.4
Kimi-K2.6	1T	83.2	80.6	90.0	92.5	80.8	54.0*
DeepSeek-V4-Flash	284B	73.2	-	69.0	90.6	76.4	45.1
DeepSeek-V4-Pro	1.6T	83.4	-	80.0	88.7	78.0	48.2
Tongyi-DeepResearch	30B	43.4	70.9	55.0	-	-	32.9
Qwen3.5-35B	35B	61.0	80.0	50.3	68.5	57.1	47.4
Qwen3.5-122B	122B	63.8	81.6	-	-	60.5	47.5
Qwen3.5-397B	397B	78.6	83.5	61.0	82.1	74.0	48.3
MiroThinker-1.7-mini	30B	67.9	80.3	57.2	67.9	-	36.4
MiroThinker-1.7	235B	74.0	82.7	62.0	72.1	-	42.9
MiroThinker-H1	-	88.2	88.5	72.0	80.6	-	47.7
Quest-35B	35B	64.6	80.8	-	-	60.6	37.2
AREX models
AREX-Turbo	4B	70.7	81.6	57.0	78.5	68.5	40.6
AREX-Base	122B	82.5	85.4	71.0	89.9	82.0	52.4

* Results reported on the full HLE. Unmarked HLE results use the text-only subset.

Inference

See inference/README.md for OpenAI-compatible serving and generation examples. The folder also contains the complete, directly formattable system and user prompts used for BrowseComp.

Intended Use

AREX-Turbo is intended for research-agent applications that require long-horizon information seeking, evidence aggregation, multi-constraint verification, and tool-augmented reasoning under a smaller model footprint. It is suited to settings where serving cost, latency, or iteration speed matter.