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cs.SE2025
RefleXGen:The unexamined code is not worth using
Bin Wang, Hui Li, AoFan Liu +7
Security in code generation remains a pivotal challenge when applying large language models (LLMs). This paper introduces RefleXGen, an innovative method that significantly enhance…
cs.SE2025
RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution
Aofan Liu, Haoxuan Li, Bin Wang +2
Code generation models based on large language models (LLMs) have gained wide adoption, but challenges remain in ensuring safety, accuracy, and controllability, especially for comp…