| base_model: | |
| - Qwen/Qwen3-8B | |
| # EvoDS | |
| ## Model Description | |
| This model is the backbone language model used in the paper **"EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management"**. It is initialized from **Qwen3-8B** and further trained to support autonomous data science tasks through multi-agent supervised fine-tuning and reinforcement learning. | |
| ## Base Model | |
| * Base Model: Qwen3-8B | |
| ## About EvoDS | |
| EvoDS is a self-evolving autonomous data science agent that continuously improves its capabilities over time. The framework introduces two key mechanisms: | |
| ### Autonomous Skill Acquisition (ASA) | |
| ASA enables the agent to autonomously synthesize, validate, cache, and reuse executable skills, allowing the agent's action space to expand over time. | |
| ### Adaptive Context Compression (ACC) | |
| ACC enables efficient long-horizon reasoning by dynamically compressing interaction history and preserving critical information under limited context budgets. | |
| ## Resources | |
| * Paper: [EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management](https://arxiv.org/abs/2606.03841v1) | |
| * Code Repository: https://github.com/usail-hkust/EvoDS | |
| ## Citation | |
| ```bibtex | |
| @article{yang2026evods, | |
| title={EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management}, | |
| author={Yang, Zherui and Liu, Fan and Ning, Yansong and Liu, Hao}, | |
| journal={arXiv preprint arXiv:2606.03841}, | |
| year={2026} | |
| } | |
| ``` |