| | --- |
| | license: mit |
| | library_name: transformers |
| | base_model: Qwen/Qwen3-Reranker-0.6B |
| | pipeline_tag: text-generation |
| | tags: |
| | - code |
| | - context-pruning |
| | --- |
| | |
| | # SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents |
| |
|
| | SWE-Pruner is a self-adaptive context pruning framework specifically designed for coding agents. It addresses the challenges of long interaction contexts, such as high API costs and latency, by performing task-aware adaptive pruning. |
| |
|
| | - **Paper:** [SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents](https://huggingface.co/papers/2601.16746) |
| | - **Repository:** [https://github.com/Ayanami1314/swe-pruner](https://github.com/Ayanami1314/swe-pruner) |
| |
|
| | ## Description |
| | Inspired by how human programmers selectively skim code, SWE-Pruner enables agents to formulate explicit goals (e.g., "focus on error handling") which guide a lightweight neural skimmer (0.6B parameters). This skimmer dynamically selects relevant lines from the surrounding context, preserving critical implementation details while significantly reducing token usage. |
| |
|
| | Evaluations across benchmarks show that SWE-Pruner achieves 23-54% token reduction on agent tasks like SWE-Bench Verified and up to 14.84x compression on single-turn tasks like LongCodeQA with minimal performance impact. |
| |
|
| | ## Citation |
| | If you find SWE-Pruner useful in your research, please cite: |
| | ```bibtex |
| | @misc{wang2026sweprunerselfadaptivecontextpruning, |
| | title={SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents}, |
| | author={Yuhang Wang and Yuling Shi and Mo Yang and Rongrui Zhang and Shilin He and Heng Lian and Yuting Chen and Siyu Ye and Kai Cai and Xiaodong Gu}, |
| | year={2026}, |
| | eprint={2601.16746}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.SE}, |
| | url={https://arxiv.org/abs/2601.16746}, |
| | } |
| | ``` |
