Jingyu Xiao, Qianxi Liu, Yintong Huo, Yuan Jiang, Michael R. Lyu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28612 · open-access PDF
软件工程人员通过迭代工具的使用来解决仓库级别的任务,但它们不断增长的交互痕迹会导致大量的代币消耗和推断。现有的压缩方法通常需要模型培训,辅助的LLM调用,或关于源代码等特定内容的假设。我们介绍了TraceLite,一个简单但有效的,无需培训的框架,用于压缩异质SWE代理观察。我们的初步研究表明,光学渲染在适度压缩下保留了大量的文本和语义信息,而
Machine translation (Meta M2M100); the English original follows.
Software engineering agents solve repository-level tasks through iterative tool use, but their continuously growing interaction traces incur substantial token consumption and inference overhead. Existing compression approaches often require model training, auxiliary LLM calls, or assumptions about specific content such as source code. We present TraceLite, a simple yet effective, training-free framework for compressing heterogeneous SWE-agent observations. Our preliminary study shows that optical rendering preserves substantial textual and semantic information under moderate compression, while
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