M2-Miner

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[ICLR 2026] M2-Miner: Multi-Agent Enhanced MCTS for Mobile GUI Agent Data Mining

M2-Miner 최신버젼 다운로드

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<h1 align="center">
  <img src="./assets/sparkui_logo.png" width="30" style="vertical-align: middle; margin-right: 3px;" />
  M<sup>2</sup>-Miner: Multi-Agent Enhanced Monte Carlo Tree Search for Mobile GUI Agent Data Mining
</h1>

<div align="center">
  <img src="./assets/M2_Miner.png" alt="M2-Miner Framework" width="80%" />
  <p><em>M<sup>2</sup>-Miner: Multi-Agent Enhanced Monte Carlo Tree Search for Mobile GUI Agent Data Mining</em></p>
</div>

---

# Overview

* [News](#news)
* [Motivation](#motivation)
* [Highlights](#highlights)
* [Experimental Results](#experimental-results)
* [Citation](#citation)

---

# 🎉 News
- **2026-04-18:** We released the inference code.
- **2026-02-05:** We released our paper, *M<sup>2</sup>-Miner: Multi-Agent Enhanced Monte Carlo Tree Search for Mobile GUI Agent Data Mining*.

# 🚀 Motivation

<div align="center">
  <img src="./assets/multi_intent_tree.png" alt="Multi intent tree" width="80%" />
  <p><em>Comparison of the different GUI data structure between prior methods.</em></p>
</div>

Compared to existing methods, our algorithm produces a more diverse set of intents from the same initial intent.

---

# ✨ Highlights

* 🤖 **Innovative MCTS-based GUI Agent Mining**: We introduce **M<sup>2</sup>-Miner**, the first automated MCTS-driven framework for GUI agent data mining, featuring a collaborative multi-agent system—**InferAgent, OrchestraAgent,** and **JudgeAgent**—that jointly boost mining efficiency and data quality.

* 🔄 **Intent Recycling Strategy**: We propose a novel intent recycling mechanism that extracts a wealth of valuable interaction trajectories, significantly enriching intent diversity and further enhancing mining efficiency.

* 🧠 **Progressive Model-in-the-Loop Training**: Our progressive model-in-the-loop training strategy greatly improves the mining success rate and enables strong generalization to unseen environments, supporting robust startup even in novel scenarios.

* 👑 **Superior Mining Quality and SOTA Agent Performance**: Extensive experiments confirm that our method excels in mining high-quality, diverse intent trajectories, and GUI agents trained with our data consistently achieve state-of-the-art results on standard benchmarks.

---

# 📊 Experimental Results

![Experimental results](https://raw.githubusercontent.com/ant-research/M2-Miner/main/assets/gui_benchmark.png)

*Table 1. Performance comparison on GUI agent benchmarks. Bold and underline indicate the best and second-best results.*

---

# 🤝 Acknowledgements

We thank the teams behind [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) and [AndroidControl](https://github.com/google-research/google-research/tree/master/android_control) for their foundational work, and [ms-swift](https://github.com/modelscope/ms-swift) for the efficient training and inference framework. We also thank [AgentCPM-GUI](https://github.com/OpenBMB/AgentCPM-GUI) for providing evaluation code.

---

# 📄 License

* Code in this repository is released under the [Apache-2.0](./LICENSE) license.

---

# 📄 Citation

If you use works related to **M<sup>2</sup>-Miner**, please cite our work:

```bibtex
@misc{lv2026m2miner,
      title={M2-Miner: Multi-Agent Enhanced MCTS for Mobile GUI Agent Data Mining}, 
      author={Rui Lv, Juncheng Mo, Tianyi Chu, Chen Rao, Hongyi Jing, Jiajie Teng, Jiafu Chen, Shiqi Zhang, Liangzi Ding, Shuo Fang, Huaizhong Lin, Ziqiang Dang, Chenguang Ma, Lei Zhao},
      journal={arXiv preprint arXiv:2602.05429},
      year={2026},
      doi={10.48550/arXiv.2602.05429},
      url={https://doi.org/10.48550/arXiv.2602.05429},
      note={Accepted by ICLR 2026}
}

```
If you are interested in our method or it helps your research, please give us a star 🌟 on GitHub.

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