AI 中文总结
TraceCAD是一种用于智能体CAD生成的轨迹引导修复方法，通过引入持久状态等机制提升了CAD生成的几何质量与修复可靠性，在DeepCAD基准测试中取得了良好效果。
AI 中文摘要
基于大语言模型（LLM）的智能体CAD生成模型可输出可执行的参数化程序，但其修正循环可能丢失关于已满足需求、错误操作及先前修复的证据。我们提出TraceCAD，一种恢复层，它将请求的特征、建模步骤、失败证据及候选结果链接为持久状态。TraceCAD诊断可能的错误操作，在其依赖区域内搜索有界编辑，通过执行和保留检查验证候选结果，并在可复用的技能记忆中保留成功与失败的修复结果。在源自DeepCAD的基准测试中，经200个模型的消融实验及1000个模型的对比，TraceCAD在IoU、Chamfer距离及Hausdorff距离方面实现了具有竞争力的几何质量；移除持久状态会使恢复得分近乎减半，移除局部搜索会使几何退化增加一倍以上且代码智能体调用次数翻倍；在不相交的训练模型上初始化技能存储可进一步减少重试次数、token成本及延迟。这些结果表明，持久、局部且可复用的恢复机制可提升最终CAD质量与修复可靠性。
英文摘要
LLM-based CAD agents produce executable parametric programs, but their correction loops may lose evidence about satisfied requirements, faulty operations, and prior repairs. We introduce TraceCAD, a recovery layer that links requested features, modeling steps, failure evidence, and candidate outcomes as persistent state. TraceCAD diagnoses likely faulty operations, searches bounded edits in their dependency regions, validates candidates through execution and preservation checks, and retains successful and failed repair outcomes in reusable skill memory. On DeepCAD-derived benchmarks with 200-model ablations and a 1K-model comparison, TraceCAD achieves competitive geometric quality in terms of IoU, Chamfer distance, and Hausdorff distance. Removing persistent state nearly halves recovery score; removing localized search more than doubles geometric regression and doubles code-agent invocations. Initializing the skill store on disjoint training models further reduces retries, token cost, and latency. These results demonstrate that persistent, localized, and reusable recovery improves final CAD quality and repair reliability.
Comments18 pages, 7 figures; includes supplementary material
