Recent advances in agentic LLMs have demonstrated remarkable automated Verilog code generation capabilities. However, existing approaches either demand substantial computational resources or rely on LLM-assisted single-agent prompt learning techniques, which we observe for the first time has a degeneration issue - characterized by deteriorating generative performance and diminished error detection and correction capabilities. This paper proposes a novel multi-agent prompt learning framework to address these limitations and enhance code generation quality. We show for the first time that multi-agent architectures can effectively mitigate the degeneration risk while improving code error correction capabilities, resulting in higher-quality Verilog code generation. Experimental results show that the proposed method could achieve 96.4% and 96.5% pass@10 scores on VerilogEval Machine and Human benchmarks, respectively while attaining 100% Syntax and 99.9% Functionality pass@5 metrics on the RTLLM benchmark.

翻译：近期，智能体化大型语言模型在自动化Verilog代码生成方面展现出卓越能力。然而，现有方法要么需要大量计算资源，要么依赖于LLM辅助的单智能体提示学习技术——我们首次发现此类技术存在退化问题，其表现为生成性能逐步恶化，以及错误检测与纠正能力持续衰减。本文提出一种新颖的多智能体提示学习框架以应对这些局限并提升代码生成质量。我们首次证明多智能体架构能有效缓解退化风险，同时增强代码纠错能力，从而实现更高质量的Verilog代码生成。实验结果表明，所提方法在VerilogEval Machine和Human基准测试中分别达到96.4%和96.5%的pass@10分数，同时在RTLLM基准测试中获得100%语法通过率和99.9%功能通过率的pass@5指标。