Topology Matters: Evaluating Topological Fidelity in CAD Generation

Geometric similarity and execution validity do not guarantee structural correctness in Computer-Aided Design (CAD): small geometric perturbations can change a solid's connected components, tunnels, or cavities. We find that such changes can arise during representation preprocessing alone: among 7,590 DeepCAD references reconstructed from both original CAD programs and quantized vectors, 762 (10.0%) have different global topology. Motivated by this gap, we study topology fidelity across five neural CAD generators, using the Betti signature of each final solid as a diagnostic. Under standard generation, exact signature fidelity ranges from 78.2% to 88.9%, and error directions differ across generation paradigms: text-conditioned models tend to omit existing structures, while the evaluated reconstruction-based models show more balanced or over-realized errors. Fidelity is also substantially lower on construction-novel patterns, with a gap persisting after standardization over measured topological complexity and construction length. Finally, even when a model reproduces the baseline topology, it rarely realizes explicit topology-changing instructions, with strict success rates of 0–1.7% across the evaluated text-conditioned models. These findings show that aggregate topology fidelity alone is insufficient to characterize CAD generators: models can differ substantially in their failure modes, sensitivity to construction patterns, and ability to modify existing topology.