Exploration Structure in LLM Agents for Multi-File Change Localization
📰 ArXiv cs.AI
Learn how to improve multi-file change localization in LLM agents using non-linear exploration structures, enhancing software engineering tools' efficiency
Action Steps
- Implement linear sequential exploration in an LLM agent using SWE Bench Pro as a benchmark
- Design a non-linear, domain-scoped parallel agentic exploration structure for the LLM agent
- Compare the performance of linear and non-linear exploration structures on a multi-file change localization task
- Apply the non-linear exploration structure to a real-world software engineering project
- Evaluate the effectiveness of the non-linear exploration structure in reducing localization time and improving accuracy
Who Needs to Know This
Software engineers and AI researchers can benefit from this knowledge to develop more efficient LLM agents for multi-file change localization, improving overall software development productivity
Key Insight
💡 Non-linear, domain-scoped parallel agentic exploration can outperform linear sequential exploration in multi-file change localization tasks
Share This
🤖 Improve multi-file change localization in LLM agents with non-linear exploration structures! 🚀
Key Takeaways
Learn how to improve multi-file change localization in LLM agents using non-linear exploration structures, enhancing software engineering tools' efficiency
Full Article
Title: Exploration Structure in LLM Agents for Multi-File Change Localization
Abstract:
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. Most AI agents explore repositories linearly, that is, visiting one directory or file per step. We postulate that this is a structural mismatch for changes that span several subsystems. We compare linear sequential exploration against non-linear, domain-scoped parallel agentic exploration. Using SWE Bench Pro as initial benchm
Abstract:
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. Most AI agents explore repositories linearly, that is, visiting one directory or file per step. We postulate that this is a structural mismatch for changes that span several subsystems. We compare linear sequential exploration against non-linear, domain-scoped parallel agentic exploration. Using SWE Bench Pro as initial benchm
DeepCamp AI