Enabling Generative Design Tools with LLM Agents for Building Novel Devices: A Case Study on Fluidic Computation Interfaces
<|reference_start|>Enabling Generative Design Tools with LLM Agents for Building Novel Devices: A Case Study on Fluidic Computation Interfaces: In the field of Human-Computer Interaction (HCI), the development of interactive devices represents a significant area of focus. The advent of novel hardware and advanced fabrication techniques has underscored the demand for specialized design tools that democratize the prototyping process for such cutting-edge devices. While these tools simplify the process through parametric design and simulation, they typically require a certain learning curve and often fall short in facilitating creative ideation. In this study, we employ fluidic computation interface as a case study to investigate the potential of augmenting design tools of physical devices with Large Language Model (LLM) agents. Enhanced by LLM agents, the Generative Design Tool (GDT) can comprehend the capabilities and limitations of newly developed devices; it can propose varied, insightful, and practical application scenarios, and recommend device designs that are technically and contextually appropriate. Furthermore, it generates the necessary design parameters for the traditional part of the design tool to visualize results and produce support files for fabrication. This paper outlines the GDT's framework, implementation, and performance, while also contemplating its prospects and the obstacles encountered.<|reference_end|>
@article{lu2024enabling,
    title={Enabling Generative Design Tools with LLM Agents for Mechanical
  Computation Devices: A Case Study},
    author={Qiuyu Lu, Jiawei Fang, Zhihao Yao, Yue Yang, Shiqing Lyu, Haipeng Mi,
  Lining Yao},
    journal={arXiv preprint arXiv:2405.17837},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17837},
    primaryClass={cs.HC}
}