西门子EDA团队借助NVIDIA NeMo Gym核查其芯片设计智能体产出的工程文件、追踪错误并改进智能体技能与工具描述。在标准单元表征基准中，复合验证分数从约55%提升至约95%，智能体轮次减少66%，输出token减少64%。NVIDIA已发布技术博客，介绍团队测量了什么、改了什么，以及其他工程团队如何复用该方法。
How do you make a chip design agent more reliable and efficient?
The @Siemens EDA team used NVIDIA NeMo Gym to check the engineering files its agent produced, trace errors, and improve the agent’s skills and tool descriptions.
In its standard cell characterization benchmark, those changes raised the composite verification score from ~55% to ~95%, with 66% fewer agent turns and 64% fewer output tokens.
We just published a new techblog that walks through what the team measured, what they changed, and how other engineering teams can apply the approach → https://nvda.ws/4Beujcb
来源：NVIDIA AI · x.com