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cs.SE2026
Compressing Code Context for LLM-based Issue Resolution
Haoxiang Jia, Earl T. Barr, Sergey Mechtaev
Large Language Models (LLMs) are now capable of resolving real-world GitHub issues. However, current approaches overapproximate the code context and suffer from two compounding pro…
cs.SE2026
Reducing Hallucinations in LLM-Generated Code via Semantic Triangulation
Yihan Dai, Sijie Liang, Haotian Xu +2
Large language models (LLMs) can generate executable code from natural language descriptions, but the resulting programs frequently contain bugs due to hallucinations. In the absen…