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arXiv · 2609.22325

ReliCAD: From Uncertain LLM Generation to Reliable Parametric CAD Modeling

Peng Zheng·Xintong Dong·Chuanyang Li·Jiaxin Jing·Chuqi Han·Hailong Shen·Yanzhi Song·Zhouwang Yang
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Abstract

Large language models have shown considerable potential for natural-language-driven parametric CAD modeling. However, a fundamental contradiction exists between their probabilistic generation and the deterministic requirements of CAD modeling, resulting in limitations in reliability, design-intent preservation, and geometric validity. Existing methods typically rely on large-scale annotated datasets, lack explicit modeling of design intent, and underutilize the deterministic capabilities of CAD kernels. To address these limitations, we propose ReliCAD, a unified framework that transforms uncertain LLM generation into reliable parametric CAD modeling. Through explicit design-intent modeling, ReliCAD converts user requirements into structured design specifications and explicitly models geometric relations, topological dependencies, and feature construction order. It then generates constraint-aware parametric instructions and invokes the CAD kernel through an Agent-ready API to perform geometric construction and constraint solving. ReliCAD further records runtime evidence and employs a verification-feedback mechanism to assess consistency between the generated model and the design specifications, enabling error localization and iterative repair. Experiments on the public HistCAD generation dataset and our multi-granularity CAD editing dataset demonstrate that ReliCAD significantly outperforms baseline methods, achieving 99.8\% validity rate and 0.8753 IoU. ReliCAD provides a verifiable, repairable, and generalizable approach to natural-language-interactive CAD modeling.

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Peng Zheng, Xintong Dong, Chuanyang Li, Jiaxin Jing, Chuqi Han, Hailong Shen, Yanzhi Song, Zhouwang Yang. 2026-09-16. ReliCAD: From Uncertain LLM Generation to Reliable Parametric CAD Modeling. https://arxiv.org/abs/2609.22325

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