用于自动化创意生成的拉蒙·柳利思维机器
The Ramon Llull's Thinking Machine for Automated Ideation
AI总结：
本研究以拉蒙·柳利的《组合艺术》为基础，定义主题、领域、方法三个组合轴，挖掘相关元素后用其组合提示LLM生成多样相关的研究想法，提出轻量可解释的科学创意增强工具。
摘要：
本文重新审视拉蒙·柳利的《组合艺术》——这是中世纪一种通过符号重组生成知识的框架——将其作为构建用于研究创意生成的现代柳利思维机器的概念基础。我们的方法定义了三个组合轴：主题（如效率、自适应性）、领域（如问答、机器翻译）和方法（如对抗训练、线性注意力）。这些元素代表了科学工作中常见的高层抽象——动机、问题设置和技术方法——并作为大语言模型（LLM）驱动的探索的构建模块。我们从人类专家或会议论文中挖掘这些元素，并表明用精心策划的组合提示LLM能产生多样化、相关且基于当前文献的研究想法。这台现代思维机器为增强科学创造力提供了一种轻量、可解释的工具，并指明了人类与AI协作创意生成的方向。
英文摘要：
This paper revisits Ramon Llull's Ars combinatoria - a medieval framework for generating knowledge through symbolic recombination - as a conceptual foundation for building a modern Llull's thinking machine for research ideation. Our approach defines three compositional axes: Theme (e.g., efficiency, adaptivity), Domain (e.g., question answering, machine translation), and Method (e.g., adversarial training, linear attention). These elements represent high-level abstractions common in scientific work - motivations, problem settings, and technical approaches - and serve as building blocks for LLM-driven exploration. We mine elements from human experts or conference papers and show that prompting LLMs with curated combinations produces research ideas that are diverse, relevant, and grounded in current literature. This modern thinking machine offers a lightweight, interpretable tool for augmenting scientific creativity and suggests a path toward collaborative ideation between humans and AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
