VeriTrace: AI Agent Achieves Human-Like Debugging in Verilog RTL Generation

A recent preprint on arXiv (2608.02878) introduces a multi-agent system called VeriTrace, which tackles the limitations faced by large language models (LLMs) in generating automated Verilog RTL. Existing multi-agent systems achieve a maximum accuracy of around 95% on conventional benchmarks, primarily due to a restricted debugging action space. This limitation confines agents to specific signals and time windows, leading to a focus on pattern matching instead of in-depth root-cause analysis. VeriTrace features an Inspector agent that utilizes a comprehensive debugging action space, allowing for independent signal selection, time-window parameters, and iteration depth. This innovative approach, known as Agentic Temporal Exploration, empowers the agent to hypothesize failure causes, gather evidence from waveforms, and iteratively enhance its understanding, resembling the investigative methods of human verification engineers. The submission was made public on arXiv as 2608.02878v1, marking a notable advancement in automated hardware design, with the potential to enhance the efficiency and reliability of RTL generation in chip design processes.