Reproduce “SiliconMind-V1: Multi-Agent Distillation and Debug-Reasoning Workflows for Verilog Code Generation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Mu-Chi Chen, Yu-Hung Kao, Po-Hsuan Huang, Shao-Chun Ho, Hsiang-Yu Tsou, I-Ting Wu, En-Ming Huang, Yu-Kai Hung and 5 more
SiliconMind-V1 trains a local language model to generate Verilog, reason through representative test cases, and revise code from failure reports. Its inference-time tests are written reasoning reports, not simulator runs. On cleaned-up VerilogEval-v2-NTU, its Qwen2.5-Coder-7B-Instruct variant reaches 73.9% pass@1 versus CodeV-R1’s 73.2%, under a three-interaction cap; it ties on original VerilogEval-v2 and trails on RTLLM-v2 (63.8% versus 66.1%).
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.