[Submitted on 16 Sep 2026]
Title:ReliCAD: From Uncertain LLM Generation to Reliable Parametric CAD Modeling
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.22325 [cs.RO]
(or arXiv:2609.22325v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.22325
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xintong Dong [view email] [v1] Wed, 16 Sep 2026 07:11:30 UTC (6,120 KB)
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