PromptV: Leveraging LLM-powered Multi-Agent Prompting for High-quality Verilog Generation

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.

@article{mi2025_2412.11014,
  title={ CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation },
  author={ Zhendong Mi and Renming Zheng and Haowen Zhong and Yue Sun and Seth Kneeland and Sayan Moitra and Ken Kutzer and Zhaozhuo Xu Shaoyi Huang },
  journal={arXiv preprint arXiv:2412.11014},
  year={ 2025 }
}