PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis

• URL: http://arxiv.org/abs/2602.12561v1
• Date: Fri, 13 Feb 2026 03:20:19 GMT
• Title: PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis
• Authors: Yuanbo Li, Dule Shu, Yanying Chen, Matt Klenk, Daniel Ritchie,
• Abstract summary: We introduce synthesisM, a self-training framework for CAD program synthesis from unlabeled 3D shapes.<n>Given a shape dataset, synthesisM iteratively samples candidate programs, selects high-fidelity executions, and augments programs to construct synthetic program-shape pairs for fine-tuning.<n>We experiment on adapting CAD-Recode from DeepCAD to the unlabeled ABC dataset show consistent improvements in geometric fidelity and program diversity.
• Score: 16.542567548166968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recovering Computer-Aided Design (CAD) programs from 3D geometries is a widely studied problem. Recent advances in large language models (LLMs) have enabled progress in CAD program synthesis, but existing methods rely on supervised training with paired shape-program data, which is often unavailable. We introduce PLLM, a self-training framework for CAD program synthesis from unlabeled 3D shapes. Given a pre-trained CAD-capable LLM and a shape dataset, PLLM iteratively samples candidate programs, selects high-fidelity executions, and augments programs to construct synthetic program-shape pairs for fine-tuning. We experiment on adapting CAD-Recode from DeepCAD to the unlabeled ABC dataset show consistent improvements in geometric fidelity and program diversity.

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