CADre: Agentic CAD Reverse Engineering without 3D Encoders

Reverse engineering 3D Computer-Aided Design (CAD) shapes is a fundamental challenge in modern engineering workflows, which aims to recover editable construction sequences from static geometry. Large language models are a natural fit for this task because they can synthesize procedural CAD programs, which can be represented as Python code. However, they cannot natively support 3D CAD as a modality, since there are no pretraining data comparable in scale and diversity to image and text data. Prior work bridges this modality gap by training 3D encoders to align geometry with the language model. Yet the alignment is itself a difficult task due to data scarcity, and these approaches tie the encoder to a fine-tuned model, precluding the use of frozen frontier LLMs. We observe that CAD geometry need not be learned as a new modality. An LLM can write programs to read or estimate its structures and dimensions, and render it to grasp the overall shape, so the input arrives entirely as text and images. Building on this observation, we propose CADre, a multi-agent framework for CAD reverse engineering that equips frontier LLMs with a Python environment and a renderer, allowing them to look at and query CAD shapes directly without training a 3D encoder. In our system, a cadre of specialized agents investigate the target geometry, generate CAD programs, and verify the resulting reconstructions collaboratively. Experiments on three CAD datasets show that our system substantially outperforms prior state-of-the-art methods by up to 38 IoU points on the hardest shapes.