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
swe-bench-verified
⭐️ Citation
If you find this project useful, please cite our work:
bibtex
1@misc{lian2026sweagilesoftwareagentframework,
2 title={SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context},
3 author={Shuquan Lian and Juncheng Liu and Yazhe Chen and Yuhong Chen and Hui Li},
4 year={2026},
5 eprint={2604.11716},
6 archivePrefix={arXiv},
7 primaryClass={cs.AI},
8 url={https://arxiv.org/abs/2604.11716},
9}
