---
tags:
- agent
pipeline_tag: text-generation
library_name: transformers
---

# SWE-AGILE

## 📣 News

[2026/02/23] SWE-AGILE has been accepted to the ACL 2026 Findings.

<font size=4><div align='center' > [[📖 Paper](https://huggingface.co/papers/2604.11716)] [[🤗 Checkpoints](https://huggingface.co/KDEGroup)] [[🤗 Daily Paper](https://huggingface.co/papers/2604.11716)] [[🚀 Github](https://github.com/KDEGroup/SWE-AGILE)]</div></font>

## 🔥 Overview

Prior approaches typically lack the **explicit System-2 reasoning** required for deep analysis. While recent reasoning models demonstrate the potential of extended Chain-of-Thought (CoT), applying them to multi-turn tasks creates a dilemma: retaining full history leads to **context explosion**, while discarding it causes **redundant re-reasoning**.

We propose SWE-AGILE, a novel software agent framework designed to bridge the gap between reasoning depth, efficiency, and context constraints. SWE-AGILE introduces a Dynamic Reasoning Context strategy, maintaining a “sliding window” of detailed reasoning for immediate continuity to prevent redundant re-analyzing, while compressing historical reasoning content into concise Reasoning Digests via **Backfilling Data Synthesis**, **Trajectory Snapshot Training** and **Compression-Aware Optimization**.



While our current paradigm implicitly reduces redundant state reconstruction, a highly promising direction to strictly enforce this efficiency is to quantitatively monitor the reasoning content. By calculating the embedding similarity between consecutive reasoning steps or employing an LLM-as-a-Judge, future iterations can explicitly filter out repetitive SFT trajectories or design targeted RLVR penalties, pushing the boundary of cognitive efficiency even further.

![overview](https://cdn-uploads.huggingface.co/production/uploads/669f53549a21428ccda89fab/YQmyhamGa29mmAWAh1Qoe.png)

![swe-bench-verified](https://cdn-uploads.huggingface.co/production/uploads/669f53549a21428ccda89fab/YGIov1OkGhAcE4wsPBCs2.png)

## ⭐️ Citation

If you find this project useful, please cite our work:

```bibtex
@misc{lian2026sweagilesoftwareagentframework,
      title={SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context}, 
      author={Shuquan Lian and Juncheng Liu and Yazhe Chen and Yuhong Chen and Hui Li},
      year={2026},
      eprint={2604.11716},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.11716}, 
}
```

## 🤝 Acknowledgements

We sincerely thank the projects [R2E-Gym/R2E-Gym](https://github.com/R2E-Gym/R2E-Gym) and [rllm-org/rllm](https://github.com/rllm-org/rllm) for providing their open-source resources.