# A Solver-Aided Hierarchical Language for LLM-Driven CAD Design

arXiv: https://arxiv.org/abs/2502.09819 · HTML: https://mlsanity.com/paper/2502.09819/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 10 of 10)

- [Large Language Models for Computer-Aided Design: A Survey](https://mlsanity.com/paper/2505.08137/) (2025)
- [MoVer: Motion Verification for Motion Graphics Animations](https://mlsanity.com/paper/2502.13372/) (2025)
- [Aligning Constraint Generation with Design Intent in Parametric CAD](https://mlsanity.com/paper/2504.13178/) (2025)
- [HistCAD: Constraint-Aware Parametric CAD Histories for Evaluating Editability](https://mlsanity.com/paper/2602.19171/) (2025)
- [A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation](https://mlsanity.com/paper/2512.23719/) (2025)
- [Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision](https://mlsanity.com/paper/2606.29301/) (2026)
- [HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding](https://mlsanity.com/paper/2607.11339/) (2026)
- [aDSL: Agentic 3D Creation via Joint Agent-Program Design](https://mlsanity.com/paper/2608.17975/) (2026)
- [Three-Phase Evaluation of AI-Assisted Software Development Life Cycle](https://mlsanity.com/paper/2607.05125/) (2026)
- [TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking](https://mlsanity.com/paper/2608.25462/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have been enormously successful in solving a wide variety of structured and unstructured generative tasks, but they struggle to generate procedural geometry in Computer Aided Design (CAD). These difficulties arise from an inability to do spatial reasoning and the necessity to guide a model through complex, long range planning to generate complex geometry. We enable generative CAD Design with LLMs through the introduction of a solver-aided, hierarchical domain specific language (DSL) called AIDL, which offloads the spatial reasoning requirements to a geometric constraint solver. Additionally, we show that in the few-shot regime, AIDL outperforms even a language with in-training data (OpenSCAD), both in terms of generating visual results closer to the prompt and creating objects that are easier to post-process and reason about.