Geometry Before Symbols: Latent State-Transition Modeling for Autoregressive CAD Generation

Recent image-to-CAD methods increasingly formulate CAD reconstruction as autoregressive generation of executable feature sequences. However, the next feature is predicted in symbolic sequence space, while its semantics are ultimately defined by the geometric state transition it induces. We introduce ForeCAD, a framework for latent geometric state-transition modeling in autoregressive CAD generation. Before generating each feature, ForeCAD forms internal representations of the current construction state and the anticipated feature-induced transition, so that the symbolic feature is generated as the realization of an internal geometric decision. We ground these representations with execution-derived supervision of intermediate states, material additions and removals, and state–transition consistency. We further introduce Representation-Guided Execution Alignment (RGEA), which aligns generated feature consequences with the geometric intent encoded by the learned representations. To support feature-aligned supervision and evaluation, we construct FeatureState-CAD, spanning Procedural Construction and DeepCAD-Complex. ForeCAD consistently outperforms sequence-based and geometry-aware baselines; on DeepCAD-Complex, it improves IoU from 36.86% to 39.53%, execution rate from 88.25% to 90.00%, and mean CD from 0.090 to 0.079. Controlled ablations and representation interventions further validate the learned latent state–transition representations and the benefit of aligning their geometric intent with executable feature generation.