LATIDIA · Robótica

ReliCAD: de la generación incierta de LLM al modelado CAD paramétrico confiable

arXiv: 2609.22325v1Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes han mostrado un potencial considerable para el modelado CAD paramétrico basado en el lenguaje natural. Sin embargo, existe una contradicción fundamental entre su proba

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arXiv:2609.22325v1 Announce Type: new 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