# CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation

arXiv: https://arxiv.org/abs/2608.09296 · HTML: https://mlsanity.com/paper/2608.09296/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 21 of 21)

- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://mlsanity.com/paper/2105.09492/) (2021)
- [JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints](https://mlsanity.com/paper/2111.12772/) (2021)
- [Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities](https://mlsanity.com/paper/2505.06507/) (2025)
- [CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation](https://mlsanity.com/paper/2505.14646/) (2025)
- [Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts](https://mlsanity.com/paper/2409.17106/) (2024)
- [ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility](https://mlsanity.com/paper/2309.16909/) (2023)
- [Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation](https://mlsanity.com/paper/2605.18430/) (2026)
- [CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation](https://mlsanity.com/paper/2605.10873/) (2026)
- [Text-to-CAD Evaluation with CADTests](https://mlsanity.com/paper/2605.07807/) (2026)
- [IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing](https://mlsanity.com/paper/2606.13368/) (2026)
- [ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation](https://mlsanity.com/paper/2604.10992/) (2026)
- [Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data](https://mlsanity.com/paper/2604.24479/) (2026)
- [MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation](https://mlsanity.com/paper/2605.28579/) (2026)
- [CADEvolve: Creating Realistic CAD via Program Evolution](https://mlsanity.com/paper/2602.16317/) (2026)
- [Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding](https://mlsanity.com/paper/2603.11831/) (2026)
- [HistCAD: Constraint-Aware Parametric CAD Histories for Evaluating Editability](https://mlsanity.com/paper/2602.19171/) (2025)
- [neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editing](https://mlsanity.com/paper/2604.16170/) (2026)
- [Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design](https://mlsanity.com/paper/2605.19717/) (2026)
- [BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending](https://mlsanity.com/paper/2603.13102/) (2026)
- [Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback](https://mlsanity.com/paper/2605.17448/) (2026)

## Built on by (most cited first, 5 of 5)

- [CADWorld: Computer-Use Benchmark for Long-Horizon Computer-Aided Design](https://mlsanity.com/paper/2609.16251/) (2026)
- [RealCADBench: Benchmarking Parametric CAD Modeling from Industrial Design Intents](https://mlsanity.com/paper/2609.03773/) (2026)
- [EngiWorld: What Can Frontier Agents Deliver in Professional Engineering Environments?](https://mlsanity.com/paper/2609.37686/) (2026)
- [SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation](https://mlsanity.com/paper/2610.02304/) (2026)
- [Language models can notice an impossible engineering problem yet still report it as solved](https://mlsanity.com/paper/2610.06668/) (2026)

## Abstract (arXiv)

A CAD model is not engineering-grade merely because it looks correct. It must satisfy design requirements, respond predictably to parameter changes, support controlled edits, match a reference structural response under a declared analysis, and connect to other parts through valid joints. We present CADEngBench, a two-track benchmark for these capabilities. CADEngBench-P evaluates 300 parametric parts, each used for one zero-to-CAD task and one functional-editing task (600 tasks in total), through boundary-representation (B-Rep) validity, engineering and DFM checks, parameter-family perturbations, functional editing, and matched linear-static FEA in CalculiX. CADEngBench-A evaluates 150 body pairs through ranked joint retrieval, exact face-and-edge grounding, joint-frame prediction, and kinematic verification. Across eight multimodal, code-capable models, editing supplied CAD is substantially easier than generating it, while complex edits and matched FEA remain difficult. Assembly predictions often locate the relevant region but fail to recover the recorded joint or mating entities. These results show that CAD evaluation must test engineering behavior rather than appearance alone.