不用额外验证器，靠候选池内部共识就能挑出好CAD模型，几何指标还更准，省事又有效。
本文提出一种无需验证器的3D CAD生成测试时扩展方法，称为共识选择。该方法从候选池中采样N个参数化CAD程序，编译为3D模型后，选择与池中其余候选最一致的那个。几何共识在三个几何指标上优于现有方法的验证器，拓扑共识在拓扑指标上与之持平。在多种LLM和提示变体下，几何共识比随机选择降低1-10%的Chamfer距离。
Test-Time Scaling for CAD Generation via Verifier-Free Consensus Selection
Large language models can write parametric CAD programs from a natural-language description (text-to-CAD generation), but a single sample is often wrong. Increasing test-time compute by sampling multiple candidates only helps if a good candidate can be identified, yet no ground-truth model is available at generation time. Existing systems often require a separate verifier, such as a vision-language judge, to select among candidates. We investigate whether the candidate pool itself provides enough signal for effective selection and a verifier-free alternative. We introduce 3D CAD consensus selection, hereafter consensus selection: sample $N$ parametric CAD programs, compile them to 3D models, and return the candidate that agrees most with the rest of the pool. The method is training-free and compatible with existing CAD agents. We investigate geometric and topological notions of agreement, each of which improves its corresponding evaluation metric. On the exact candidate pools of a state-of-the-art CAD generation method, geometric consensus improves all three geometric metrics over the method's verifier, while topological consensus matches it on topology. Across every tested LLM and prompt variant, geometric consensus also improves geometric accuracy over random selection from the same pool, reducing Chamfer distance by $1-10\%$.