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VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space - CloudYume
VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space
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What happened
arXiv:2608.02878v1 Announce Type: new Abstract: Large language models have shown promise for automated Verilog RTL generation, yet state-of-the-art multi-agent systems plateau at ~95% accuracy on standard benchmarks. We
Analysis and impact
导读摘要
一项名为 VeriTrace 的新型多智能体系统，通过模仿人类工程师的时序探索行为，首次在 VerilogEval-V2 基准测试中实现了 100% 的功能正确率，突破了现有 AI 硬件设计工具的精度瓶颈。
这项研究由马里兰大学等机构合作完成，其成果与当前 AI 智能体在专业领域的深化应用趋势高度契合。正如 OpenAI 与 HSP GRUPPE 的合作 展示了 AI 在税务咨询等知识密集型工作中的潜力，VeriTrace 则证明了在更为复杂的硬件设计领域，通过赋予 AI 更接近人类专家的认知与探索能力，同样可以攻克此前难以逾越的精度天花板。这预示着未来的 EDA 工具将不再仅仅是代码补全器，而是能真正理解设计意图和物理实现的智能协作者。
arXiv:2608.02878v1 Announce Type: new Abstract: Large language models have shown promise for automated Verilog RTL generation, yet state-of-the-art multi-agent systems plateau at ~95% accuracy on standard benchmarks. We trace this ceiling to an incomplete debugging action space: existing systems restrict which signals the agent can inspect, which time windows it can query, or both, reducing debugging to pattern matching on a narrow, predetermined view of circuit behavior rather than hypothesis-driven root-cause analysis. We present VeriTrace, a multi-agent system whose Inspector agent operates over a complete debugging action space, with independent control over signal selection, time-window bounds, and iteration depth. This capability, which we term Agentic Temporal Exploration, enables the agent to form hypotheses about failure causes, query the waveform for evidence, and refine its understanding iteratively, mirroring the exploratory process of human verification engineers. VeriTrace achieves 100% Pass@1 on VerilogEval-V2, the first system to attain perfect functional correctness on this benchmark. On a shared Claude Sonnet 4.0 backbone, VeriTrace outperforms the strongest reproduced baseline by +5.1%, demonstrating that debugging agency closes the final accuracy gap.