agent/papers/items/2026-2607-01916-contextsniper-anttrail-s-token-efficient-code-memory-for-repository-level-progra.md

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

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type: paper title: "ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair" authors: Chiwang Luk, Matin Mohammad Najafi, Zhifeng Jia, Wei Yang, Xiuchang Li, Jinwei Zhu, Yang Ren, Lei Chen, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.01916 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-02 updated_at: 2026-07-06 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• memory
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: agent-memory, coding-agent, rag-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agent-memory, coding-agent, rag-agent
• inferred topics: agent-evaluation, coding-agent, memory, rag, tool-use
• arXiv categories: cs.AI
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.01916