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Cost-Effective Repository Exploration for Agentic Issue Localization

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository files are likely to matter. We study whether this stage can be delegated to lower-cost models while retaining useful localization quality. Using our IssueLoc-Bench, we evaluate five explorer models under the same read-only interactive interface on 499 SWE-bench Verified-derived tasks and 500 tasks from 153 additional repositories. We measure early candidate discovery, top-three gold-file coverage, strict file-set recovery, agent time, and token usa

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-30T09:16:33.000Z

First collected: 2026-09-21T07:31:56.984Z. This is not the publication date.