ResCAD: Language-Grounded CAD Assembly via Geometry-Constrained Interface Resolution

Advances in CAD generation and retrieval make individual components increasingly available, but independently produced parts often lack the assembly semantics required to connect them. We study language-grounded CAD assembly: given a natural-language request and candidate STEP solids for each requested component, the goal is to select the components, ground their connections to boundary-representation (B-rep) faces or edges, and construct a CAD-kernel-verified assembly without predefined assembly interfaces. The central challenge is that multiple component combinations and connection locations can satisfy the same local geometric requirements. We introduce ResCAD, a framework that separates feasibility, grounding, and execution. Its key principle is to use learning to resolve ambiguity left by geometric constraints rather than replace geometric reasoning. A frozen language model compiles the request into a connection graph and interface requirements; deterministic B-rep filtering removes incompatible hypotheses; language-conditioned B-rep rankers resolve component and interface choices; and a CAD kernel computes poses and verifies the resulting assembly. On 114 unseen Fusion 360 assemblies, geometric filtering increases component-assignment recall within 25 proposals from 30.70% to 89.47%. Under the same candidates and feasible set, ResCAD achieves 71.05% complete-connection recall within 25 proposals, compared with 35.09% for an adapted JoinABLe-style scorer. On 50 end-to-end requests with geometrically unique interfaces, ResCAD recovers 82% exact assemblies. These results show that geometric pruning and language-conditioned interface grounding play complementary roles in converting independently produced CAD geometry into verified assemblies.