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--- |
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license: mit |
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library_name: transformers |
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base_model: Qwen/Qwen3-Reranker-0.6B |
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pipeline_tag: token-classification |
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tags: |
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- code |
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- context-pruning |
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- agent |
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datasets: |
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- nick007x/github-code-2025 |
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metrics: |
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- f1 |
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- mse |
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--- |
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# SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents |
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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. |
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- **Paper:** [SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents](https://huggingface.co/papers/2601.16746) |
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- **Repository:** [https://github.com/Ayanami1314/swe-pruner](https://github.com/Ayanami1314/swe-pruner) |
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## Description |
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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. |
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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. |
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## Model Usage |
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Given that we have made significant modifications to the model, its dual-head architecture and the complex compression head service code will be rather complex. |
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Therefore, we recommend that you use the version we have released on [GitHub](https://github.com/Ayanami1314/swe-pruner) instead of attempting to use the original model on your own. |
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## Citation |
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If you find SWE-Pruner useful in your research, please cite: |
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```bibtex |
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@misc{wang2026sweprunerselfadaptivecontextpruning, |
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title={SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents}, |
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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}, |
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year={2026}, |
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eprint={2601.16746}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.SE}, |
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url={https://arxiv.org/abs/2601.16746}, |
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} |
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``` |
