Parallel Domain-Scoped Exploration Improves LLM Agents' Ability to Locate Files for Software Changes
Researchers propose a non-linear, domain-scoped parallel agentic approach for localizing files to change when resolving software issues, outperforming linear single-agent baselines on most benchmarks. The study benchmarks the approach against a base LLM, a single-agent recursive baseline, and an external CLI tool (Codex 5.5 High) using SWE-Bench Pro with the Ansible repository as a case study. The findings suggest that structural exploration design—not just model size—significantly affects localization performance in AI-driven software engineering.
A new preprint from arXiv proposes that most LLM-based software engineering agents suffer from a structural mismatch when handling issues requiring changes across multiple subsystems, because they explore codebases linearly—one file or directory at a time. The authors introduce a domain-scoped parallel agent spawning system and evaluate it against several baselines, including a plain LLM without repository access, a single-agent Recursive Language Model with a Python REPL, and Codex 5.5 High via an external CLI. Using a small Haiku-class model, the domain-agent approach achieves the highest micro F1 among Haiku-class models by a large margin, and ranks second overall behind only the much larger Codex 5.5 High on an expanded benchmark covering pull requests from 2025 and 2026. On the original 2020 SWE-Bench Pro benchmark, a larger Sonnet-class plain LLM attains higher micro F1 by predicting fewer files—yielding higher precision but substantially lower recall. The study also identifies three secondary findings: documentation evolution represents an unresolved latent dependency across all approaches, naive filesystem access can degrade performance through over-prediction of test files, and forced multi-agent consultation provides no measurable benefit while significantly increasing token costs.
What's missing
The study uses Ansible as its sole repository exemplar, leaving open whether results generalize to other codebases or programming languages. The evaluation relies on file-level localization metrics (micro F1, recall) without reporting downstream task performance such as whether correctly localized files actually lead to successful patch generation or issue resolution.
What different sources said
- arXiv cs.AICenter
Exploration Structure in LLM Agents for Multi-File Change Localization
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