arXiv AI
Cost-Effective Repository Exploration for Agentic Issue Localization
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Jun 11
Exploration Structure in LLM Agents for Multi-File Change Localization
arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.
Hugging Face Trending Papers
Jun 10
Exploration Structure in LLM Agents for Multi-File Change Localization
Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step.
arXiv Computation and Language
Oct 8
Large-scale Repository Engineering via Agent-Native Reusable Code Primitives
arXiv:2610.09079v1 Announce Type: cross Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete r...
arXiv AI
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Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?
arXiv:2602.11988v3 Announce Type: replace-cross Abstract: A widespread practice in software development is to tailor coding agents to repositories using context files, such as AGENTS.md. Although thi...
arXiv Machine Learning
2d ago
CuraLoc: Repository-Level Fault Localization via Planned Code Inspection
arXiv:2610.31809v1 Announce Type: new Abstract: A bug report describes what went wrong, but rarely where the code should change. Existing repository-level localization methods often let language mode...
arXiv AI
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PROBE: Frontier Coding Agents Find Different Bugs Than Maintainers Fix
arXiv:2610.35987v1 Announce Type: cross Abstract: Proactive bug discovery is an important software-engineering problem. Even though coding agents are already widely deployed in this domain, their cap...