README.md

---
base_model:
- Qwen/Qwen3.5-4B
license: mit
---

# Distillation-Learner-Qwen3.5-4B

[🌐 Project Page](https://recursivemas.github.io) | [💻 Code](https://github.com/RecursiveMAS/RecursiveMAS) | [📄 Paper](https://arxiv.org/abs/2604.25917)

We introduce RecursiveMAS, a multi-agent framework that scales agent collaboration through latent-space recursion.
RecursiveMAS treats a multi-agent system as a unified recursive computation, where heterogeneous agents iteratively exchange, refine, and evolve their latent states across recursion rounds. In the Distillation-Style setting, the Learner Agent receives guidance from the Expert Agent and performs task solving through recursive latent-space collaboration.

## Model Details

| Item | Description |
|---|---|
| Model | Distillation-Learner-Qwen3.5-4B |
| Collaboration Style | Distillation-Style |
| Agent Role | Learner Agent |
| Base Model | Qwen3.5-4B |

⚠️ **Note:** This checkpoint is a **role-specific agent** in [**RecursiveMAS**](https://arxiv.org/abs/2604.25917), rather than a standalone model intended for plain-text generation.  
For detailed usage instructions, please refer to our [GitHub repository](https://github.com/RecursiveMAS/RecursiveMAS).

## Model Collections for RecursiveMAS

| Style | Model Collection |
|---|---|
| Sequential-Style | [🤗 HuggingFace](https://huggingface.co/collections/RecursiveMAS/sequential-style-recursivemas) |
| Mixture-Style | [🤗 HuggingFace](https://huggingface.co/collections/RecursiveMAS/mixture-style-recursivemas) |
| Distillation-Style | [🤗 HuggingFace](https://huggingface.co/collections/RecursiveMAS/distillation-style-recursivemas) |
| Deliberation-Style | [🤗 HuggingFace](https://huggingface.co/collections/RecursiveMAS/deliberation-style-recursivemas) |

## Experiment Results

<p align="center">
  <img src="https://raw.githubusercontent.com/RecursiveMAS/RecursiveMAS/main/assets/hero_fig.png" width="95%" alt="RecursiveMAS Experiment Results">
</p>

## Citation

```bibtex
@misc{recursivemas,
      title={Recursive Multi-Agent Systems}, 
      author={Xiyuan Yang and Jiaru Zou and Rui Pan and Ruizhong Qiu and Pan Lu and Shizhe Diao and Jindong Jiang and Hanghang Tong and Tong Zhang and Markus J. Buehler and Jingrui He and James Zou},
      year={2026},
      eprint={2604.25917},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.25917}, 
}