3 papers
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…
cs.PL2026
Defusing Logic Bombs in Symbolic Execution with LLM-Generated Ghost Code
Dimitrios Stamatios Bouras, Sergey Mechtaev
Symbolic execution is a powerful program analysis technique, but its effectiveness is fundamentally limited by solver-hostile program fragments, complex numerical reasoning, and un…