AI RESEARCH
The Limits of Long-Context Reasoning in Automated Bug Fixing
arXiv CS.LG
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ArXi:2602.16069v2 Announce Type: replace-cross Rapidly increasing context lengths have led to the assumption that large language models (LLMs) can directly reason over entire codebases. Concurrently, recent advances in LLMs have enabled strong performance on software engineering benchmarks, particularly when paired with agentic workflows. In this work, we systematically evaluate whether current LLMs can reliably perform long-context code debugging and patch generation.