TOPOGEN: TOPOLOGY-GUIDED MULTIMODAL GENERATION FOR EXECUTABLE CADQUERY SYNTHESIS

Multimodal models have shown promise in programmatic CAD generation; however, generating executable and structurally faithful programs from visual observations remains challenging. A model may reproduce the overall shape while failing to capture the structural relations and modeling operations required for engineering validity. Unlike many generative tasks, CAD provides an executable geometric representation that a geometric kernel can deterministically verify, making structural errors explicitly measurable. We present TopoGen, a topology-guided multimodal framework for CadQuery synthesis from point clouds and multi-view images. Our central insight is that CAD topology provides a shared structural space bridging heterogeneous visual observations and executable programs. TopoGen encodes topology as a constraint graph and derives geometry-grounded structural constraints from BReps for supervision during training. For execution-grounded reinforcement learning, we introduce Topology Relative Policy Optimization (ToRPO), which uses essential-operation matching to prevent reward exploitation and provide structural feedback during program generation. This design preserves both the geometric and topological relations of generated CAD models. We further construct TopoCAD-1M, a topology-augmented corpus that enriches large-scale mechanical engineering data with structural constraints and alleviates the long-tailed distribution of CAD operations.