用中世纪思想框架构建AI创意生成工具，助力科研灵感迸发。
The Ramon Llull's Thinking Machine for Automated Ideation
- 以主题、领域、方法三轴组合符号，生成可解释的研究构想。
- 结合专家论文提炼要素，提示大模型产出多样且贴合文献的创意。
- 适合科研人员提升创意效率，推动人机协同创新。
本文重新审视中世纪罗曼·鲁尔思想体系——一种通过符号重组生成知识的框架——作为现代鲁尔思维机器的理论基础，用于支持研究创意自动生成。该方法定义三个组合维度：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）。这些元素代表科学工作中常见的动机、问题设定与技术路径，构成大模型驱动探索的基础模块。我们从人类专家或会议论文中提取这些要素，证明以精选组合提示大模型，能生成多样化、相关性强且扎根于现有文献的研究想法。该现代思维机器提供了一种轻量级、可解释的工具，可增强科学创造力，并为人类与AI协作创意提供了可行路径。
原文摘要 · Abstract (English)
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.
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