IntentCAD: Learning Text-to-CAD from the Intent Behind the Shape

A CAD program that reproduces the right shape can still hide how the part was designed. The same mounting plate can be written as four separate circle cuts at literal coordinates or as one hole pattern with a shared diameter, and many text-to-CAD training pairs take the first form, with descriptions that leave the part’s purpose unstated. We ask whether making this design information explicit in training improves generation even when test prompts describe geometry alone. To test this, we construct IntentCAD, 446K text–program pairs from 223K parts, whose programs organize operations into features with named parameters and explicit relations, are verified against the source geometry, and are paired with descriptions that also state the part’s use and design intent. In controlled experiments with a 4B model on Text2CAD-Bench, normalizing programs and descriptions lowers Chamfer distance by 27% on average. Adding design intent, which no test prompt mentions, lowers it by a further 11% and raises IoU on all four splits. The resulting model also produces valid programs more often than four general- purpose LLMs, with an average invalid rate of 11.0% against 20.7–63.6%. These results suggest that what a text-to-CAD model learns depends on the design information its training data exposes, beyond the geometry it reproduces.