ReliCAD: From Uncertain LLM Generation to Reliable Parametric CAD Modeling

2026-09-16

AI Summary

为解决LLM生成不确定性和CAD建模确定性之间的矛盾，ReliCAD通过显式设计意图建模和约束感知指令生成可靠参数化CAD模型。

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.

Problem

• Uncertain LLM Generation
• Reliable Parametric CAD Modeling
• Design Intent Preservation

Innovation

• explicit design-intent modeling
• constraint-aware parametric instructions
• Agent-ready API
• verification-feedback mechanism