{"ID":2879519,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16517","arxiv_id":"2508.16517","title":"ARSP: Automated Repair of Verilog Designs via Semantic Partitioning","abstract":"Debugging functional Verilog bugs consumes a significant portion of front-end design time. While Large Language Models (LLMs) have demonstrated great potential in mitigating this effort, existing LLM-based automated debugging methods underperform on industrial-scale modules. A major reason for this is bug signal dilution in long contexts, where a few bug-relevant tokens are overwhelmed by hundreds of unrelated lines, diffusing the model's attention. To address this issue, we introduce ARSP, a two-stage system that mitigates dilution via semantics-guided fragmentation. A Partition LLM splits a module into semantically tight fragments; a Repair LLM patches each fragment; edits are merged without altering unrelated logic. A synthetic data framework generates fragment-level training pairs spanning bug types, design styles, and scales to supervise both models. Experiments show that ARSP achieves 77.92% pass@1 and 83.88% pass@5, outperforming mainstream commercial LLMs including Claude-3.7 and SOTA automated Verilog debugging tools Strider and MEIC. Also, semantic partitioning improves pass@1 by 11.6% and pass@5 by 10.2% over whole-module debugging, validating the effectiveness of fragment-level scope reduction in LLM-based Verilog debugging.","short_abstract":"Debugging functional Verilog bugs consumes a significant portion of front-end design time. While Large Language Models (LLMs) have demonstrated great potential in mitigating this effort, existing LLM-based automated debugging methods underperform on industrial-scale modules. A major reason for this is bug signal diluti...","url_abs":"https://arxiv.org/abs/2508.16517","url_pdf":"https://arxiv.org/pdf/2508.16517v1","authors":"[\"Bingkun Yao\",\"Ning Wang\",\"Xiangfeng Liu\",\"Yuxin Du\",\"Yuchen Hu\",\"Hong Gao\",\"Zhe Jiang\",\"Nan Guan\"]","published":"2025-08-22T16:40:17Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.PL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}