BAAI/AREX-2

Name: BAAI/AREX-2
Creator: BAAI
License: apache-2.0

image-text-to-texttransformersVerified checksumsqwen3_5agentdeep-researchreasoningtool-uselong-context

License: apache-2.0
Size: 51 GB
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[Image blocked: AREX] AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks

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

Model	Params	Frontier-CS	MLE-Lite
Closed-weight models
GPT-5.6 Sol	-	76.4	72.7
Claude Opus 4.8	-	74.5	63.6
GPT-5.5	-	72.1	68.2
Gemini-3.1-Pro	-	68.9	-
Qwen3.7-Max	-	61.9	-
Open-weight models
Kimi-K3	2.8T	-	72.7
Naive-N0.5-Flash	309B	-	73.7
DeepSeek-V4-Pro	1.6T	44.7	54.5
DeepSeek-V4-Flash	284B	39.1	51.5
Kimi-K2.7-Code	1T	54.7	-
GLM-5.3-Flash	320B	50.4	-
Kimi-K2.6	1T	46.9	66.7
Frontis-MA1-35B	35B	-	71.2
BigBang-V1	35B	-	59.1
Qwen3.6-35B-A3B	35B	23.4	39.4
AREX-2	27B	70.7	81.8

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

General agentic reasoning and deep research

Model	Params	BrowseComp	HLE	GAIA	DeepSearchQA
Frontier models
GPT-5.6 Sol	-	90.4	58.0*	-	-
GPT-5.6 Terra	-	87.5	-	-	-
GPT-5.6 Luna	-	83.3	-	-	-
Kimi-K3	2.8T	91.2	56.0*	-	95.0
Claude Fable 5	-	88.0	64.5*	-	94.2
Claude Opus 4.8	-	84.3	57.9*	-	93.1
GPT-5.5	-	84.4	52.2*	87.4	-
Gemini-3.1-Pro	-	85.9	51.4*	80.6	93.3
Large models (>40B)
GLM-5	744B	75.9	50.4	70.0	-
Kimi-K2.6	1T	83.2	54.0*	80.6	92.5
GLM-5.3-Flash	320B	-	55.3*	78.8	-
DeepSeek-V4-Flash	284B	73.2	45.1	57.5	90.6
DeepSeek-V4-Pro	1.6T	83.4	48.2	71.1	88.7
MiroThinker-1.7	235B	74.0	42.9	82.7	72.1
XYZ-Aquila-pro	397B	84.8	53.3	-	92.5
Iris-pro	397B	88.6	56.4	-	92.9
AREX-Base	122B	82.5	52.4	85.4	89.9
Small models (≤40B)
Tongyi-DeepResearch-30B	30B	43.4	32.9	70.9	-
Qwen3.5-35B	35B	61.0	47.4	80.0	68.5
XYZ-Aquila-mini	35B	78.8	51.1	97.1	89.5
BigBang-V1	35B	76.5	50.3	-	-
Quest-35B	35B	64.6	37.2	80.8	-
Apodex-1.0-mini	35B	71.5	46.8	-	82.2
Agents-A1	35B	75.5	47.6	96.0	-
MiroThinker-1.7-mini	30B	67.9	36.4	80.3	67.9
Iris-mini	35B	82.2	52.3	-	86.9
AREX-Turbo	4B	70.7	40.6	81.6	78.5
AREX-2	27B	84.0	52.6	92.2	93.8

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

Inference

Use a recent Transformers release with Qwen3.8 support.

python

import torchfrom 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 [blocked]. Follow the terms and notices for the Qwen base model and any downstream data or tools.

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}}