发表机构
City University of Hong Kong; Southeast University; National Center of Technology Innovation for EDA（香港城市大学; 东南大学; 国家EDA技术创新中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对智能体RTL代码生成的时序信息传递难题，本研究提出含SeQuery和SeGraph机制的SeqFeed，通过满足三项反馈要求提升LLM的RTL代码生成通过率。
AI 中文摘要
RTL代码生成是硬件设计的关键阶段，智能体系统的出现为该过程的自动化提供了新机遇。为生成正确的RTL代码，智能体必须理解顺序行为，包括信号如何在多个时钟周期内演化和传播。然而，向智能体有效传递此类时序信息仍是重大挑战：RTL代码不会暴露特定执行的周期级信号行为，而完整仿真波形对于大语言模型（LLM）分析而言过于庞大且存在噪声。为解决这些局限，本研究分析人类工程师推理顺序行为的方式，确定有效反馈需满足三项要求：可事件寻址、可依赖追踪、可迭代查询。基于这些要求，我们提出SeqFeed，它包含两种互补机制：（1）SeQuery，一种类SQL的波形查询语言，使智能体能将查询锚定到语义事件，并在相对时间点采样信号值；（2）SeGraph，一种跟踪跨时钟周期信号传播的依赖图。对多种LLM的实验结果表明，SeqFeed可提高通过率，SeQuery和SeGraph单独使用均有效，组合使用时还能提供互补增益。
英文摘要
RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To generate correct RTL code, agents must understand sequential behavior, including how signals evolve and propagate over multiple clock cycles. However, effectively conveying such temporal information to agents remains a significant challenge. RTL code does not expose cycle-level signal behavior for a specific execution, whereas full simulation waveforms are too voluminous and noisy for effective LLM analysis. To address these limitations, we study how human engineers reason about sequential behavior and identify three requirements for effective feedback: it should be event-addressable, dependency-traceable, and iteratively-queryable. Guided by these requirements, we propose \textit{SeqFeed}, which comprises two complementary mechanisms: (1) \textit{SeQuery}, an SQL-like waveform query language that enables agents to anchor queries to semantic events and sample signal values at relative time points; and (2) \textit{SeGraph}, a dependency graph that tracks signal propagation across clock cycles. Experimental results across multiple LLMs demonstrate the effectiveness of SeqFeed in improving pass rates. SeQuery and SeGraph are each effective independently and provide complementary benefits when used together.
