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arXiv · 2607.01916

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

Chiwang Luk·Matin Mohammad Najafi·Zhifeng Jia·Wei Yang·Xiuchang Li·Jinwei Zhu·Yang Ren·Lei Chen·Gao Cong
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Abstract

Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long terminal outputs where useful evidence is mixed with irrelevant code and logs. This paper presents ContextSniper, AntTrail's code-repair module for precision evidence selection in repository-level program repair, as part of AntTrail's broader agent-memory engine. AntTrail is available at https://gitcode.com/datagallery/AntTrail. ContextSniper indexes code and action memory at three levels of abstraction, retrieves candidates with a hybrid ranker, filters long tool output through an intention-aware context gate, and returns compact evidence packets while keeping full source recoverable on demand. In matched 50-task-per-condition comparisons, ContextSniper reduces total token use by 51.5% and logged cost by 36.4% for OpenClaw on SWE-bench Lite, and reduces total token use by 40.0% and average interaction rounds by 28.1% for OpenCode on SWE-bench Pro. Submitted-resolution rates differ by one task out of 50 in each host-agent setting. In a separate five-task comparison, ContextSniper outperforms existing memory- and retrieval-augmented generation (RAG)-style integrations in token efficiency. These results suggest that ContextSniper can substantially reduce token and interaction overhead for repository-level repair agents without a measurable loss in repair quality. The evaluation harness is available at https://gitcode.com/lukchiwang/ContextSniper.

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Chiwang Luk, Matin Mohammad Najafi, Zhifeng Jia, Wei Yang, Xiuchang Li, Jinwei Zhu, Yang Ren, Lei Chen, Gao Cong. 2026-09-14. ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair. https://arxiv.org/abs/2607.01916

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