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Improving Code Localization with Repository Memory

ICLR 2026 · Multi-agent systems and games › LLM agents · approach: Software engineering agent benchmarks · 0 citations (OpenAlex)

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Augments the LocAgent code-localization agent with two commit-history-derived memories (episodic commit search and LLM-generated semantic summaries of frequently edited files), improving file-level localization on SWE-bench-verified and SWE-bench-live.

Problem

Code localization agents treat each repository issue from scratch, ignoring accumulated repository knowledge that human developers build over time; the paper asks how to give agents long-term repository memory for localizing bugs to files.

Approach

Build a non-parametric repository memory from commit history: a BM25-searchable store of past commits and linked issues (episodic), plus LLM-written functionality summaries of the most frequently edited files (semantic), exposed to the LocAgent ReAct loop as extra tools.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: SWE-bench-verified SWE-bench-live repository commit history (7,000 commits prior to each issue)

Benchmarks: SWE-bench-verified SWE-bench-live

Metrics: Accuracy@1 Accuracy@3 Accuracy@5 resolve rate API cost

Baselines: CodeRankEmbed Agentless LocAgent

Builds on: LocAgent ReAct Agentless

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