NVIDIA NeMo Gym 优化芯片设计 Agent 可靠性与工具调用效率
原标题：Optimizing Agent Reliability and Tool Calling Efficiency for Chip Design Using NVIDIA NeMo Gym
AI 摘要
NVIDIA 发布技术博客，介绍其开源库 NeMo Gym 如何用于提升芯片设计 AI Agent 的可靠性。西门子 Fuse EDA AI Agent 团队利用 NeMo Gym 对标准单元表征任务进行端到端评估，通过验证最终 Liberty 文件而非中间步骤，发现并修复了错误传播问题。团队据此改进了 Agent 技能和 MCP 工具描述，使基准综合得分从约 55% 提升至约 95%，同时减少了交互轮次和输出 token。
正文节选
AI agents offer remarkable productivity gains in complex chip design workflows, but their tendency to generate plausible yet incorrect artifacts makes reliability a major barrier to production adoption. In multistage engineering workflows, an undetected error early in setup can silently propagate through downstream stages and final artifact generation. Evaluating and refining these agents against verified final outputs, rather than isolated intermediate steps, is essential for building trustwort