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

---
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:
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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