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  "full_text": "LLM-based Agents for Automated Bug Fixing\n\nAnalyzes 7 leading LLM-based bug fixing systems on the SWE-bench Lite benchmark, finding MarsCode Agent (developed by ByteDance) achieved the highest success rate at 39.33%.\n\nReveals that for error localization line-level fault localization accuracy is more critical than file-level accuracy, and bug reproduction capabilities significantly impact fixing success.\n\nShows that 24/168 resolved issues could only be solved using reproduction techniques, though reproduction sometimes misled LLMs when issue descriptions were already clear.\n\nConcludes that improvements are needed in both LLM reasoning capabilities and Agent workflow design to enhance automated bug fixing effectiveness.\n\nThis paper highlights the challenging nature of some domains, like code, and the opportunities to innovate further in agentic workflow design.",
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