| --- |
| 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}, |
| } |
| ``` |
