RepoNav: From Snippet Retrieval to File-Centered Repository Navigation for Code Agents

2026-09-08

AI Summary

为解决代码代理在大型代码库中定位相关文件和函数的问题，RepoNav通过重组检索片段提供以文件为中心的导航框架，提高函数级定位精度。

Abstract

Solving repository-level code tasks requires LLM-based agents to use code search tools to navigate large codebases and identify a small set of relevant files and functions. However, current retrieval tools typically return flat lists of isolated code snippets: such lists can surface relevant files, but provide insufficient structure for agents to distinguish the target function from semantically similar alternatives in the same file. We introduce RepoNav, a lightweight post-retrieval interface that reorganizes retrieved snippets into a file-centered navigation scaffold. By presenting compact structural cues and candidate targets, this scaffold guides on-demand file-structure browsing, helping agents compare sibling symbols before selecting a target function. Across diverse models on LocBench, RepoNav improves function-level localization and narrows the file-to-function gap. Controlled ablations demonstrate that these gains come from structured evidence organization rather than simply exposing additional file structure, and the approach also improves performance on a repository-level question-answering benchmark.

Problem

• code search
• LLM-based agents
• file-centered navigation
• code snippets
• repository-level tasks

Innovation

• file-centered navigation
• post-retrieval interface
• structural cues
• code snippets reorganization
• function localization