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cs.AR2026
Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation
Yiheng Shen, Wei Zheng, Xiao Wei +3
Large Language Models (LLMs) have shown remarkable potential in Verilog code generation, yet existing datasets contain con siderable noise and redundancy. Prior data selection meth…
cs.SE2026
Semantic Consensus Decoding: Backdoor Defense for Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Large language models (LLMs) for Verilog code generation are increasingly adopted in hardware design, yet remain vulnerable to backdoor attacks where adversaries inject malicious t…