{"ID":2839667,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15767","arxiv_id":"2511.15767","title":"TB or Not TB: Coverage-Driven Direct Preference Optimization for Verilog Stimulus Generation","abstract":"With the rapid advancement of Large Language Models (LLMs), there is growing interest in applying them to hardware design and verification. Among these stages, design verification remains the most time-consuming and resource-intensive phase, where generating effective stimuli for the design under test (DUT) is both critical and labor-intensive. We present {\\it TB or not TB}, a framework for automated stimulus generation using LLMs fine-tuned through Coverage-Driven Direct Preference Optimization (CD-DPO). To enable preference-based training, we introduce PairaNet, a dataset derived from PyraNet that pairs high- and low-quality testbenches labeled using simulation-derived coverage metrics. The proposed CD-DPO method integrates quantitative coverage feedback directly into the optimization objective, guiding the model toward generating stimuli that maximize verification coverage. Experiments on the CVDP CID12 benchmark show that {\\it TB or not TB} outperforms both open-source and commercial baselines, achieving up to 77.27\\% improvement in code coverage, demonstrating the effectiveness of Coverage-driven preference optimization for LLM-based hardware verification.","short_abstract":"With the rapid advancement of Large Language Models (LLMs), there is growing interest in applying them to hardware design and verification. Among these stages, design verification remains the most time-consuming and resource-intensive phase, where generating effective stimuli for the design under test (DUT) is both cri...","url_abs":"https://arxiv.org/abs/2511.15767","url_pdf":"https://arxiv.org/pdf/2511.15767v1","authors":"[\"Bardia Nadimi\",\"Khashayar Filom\",\"Deming Chen\",\"Hao Zheng\"]","published":"2025-11-19T17:23:06Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.PL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}