arXiv Machine Learning
CuraLoc: Repository-Level Fault Localization via Planned Code Inspection
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Cost-Effective Repository Exploration for Agentic Issue Localization
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SHERLOC: Structured Diagnostic Localization for Code Repair Agents
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PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization
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Hugging Face Trending Papers
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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.