Memory-Augmented Reinforcement Learning Agent for CAD Generation
A new paper presents a memory-augmented reinforcement learning framework aimed at improving computer-aided design (CAD) generation. The proposed method addresses limitations of existing large language model-based approaches by incorporating a structured toolchain and a dual-track memory module. Experimental results indicate significant improvements in success rates and geometric consistency for complex CAD tasks.
- ▪The framework encapsulates a geometric kernel into a structured toolchain callable by the agent.
- ▪It features a dual-track memory module consisting of a case library and a skill library.
- ▪The agent can effectively avoid retrieval traps and enable online self-correction without needing large-scale annotated data.
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Computer Science > Artificial Intelligence arXiv:2605.19748 (cs) [Submitted on 19 May 2026] Title:Memory-Augmented Reinforcement Learning Agent for CAD Generation Authors:Yin Xiaolong, Liu Yu, Shen Jiahang, Lu Xingyu, Ni Jingzhe, Fan Fengxiao, Sang Fan View a PDF of the paper titled Memory-Augmented Reinforcement Learning Agent for CAD Generation, by Yin Xiaolong and 6 other authors View PDF HTML (experimental) Abstract:Automatic generation of computer-aided design (CAD) models is a core technology for enabling intelligence in advanced manufacturing.
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