Scientific idea generation has been extensively studied in creativity theory and computational creativity research, providing valuable frameworks for understanding and implementing creative processes. However, recent work using Large Language Models (LLMs) for research idea generation often overlooks these theoretical foundations. We present a framework that explicitly implements combinatorial creativity theory using LLMs, featuring a generalization-level retrieval system for cross-domain knowledge discovery and a structured combinatorial process for idea generation. The retrieval system maps concepts across different abstraction levels to enable meaningful connections between disparate domains, while the combinatorial process systematically analyzes and recombines components to generate novel solutions. Experiments on the OAG-Bench dataset demonstrate our framework's effectiveness, consistently outperforming baseline approaches in generating ideas that align with real research developments (improving similarity scores by 7\%-10\% across multiple metrics). Our results provide strong evidence that LLMs can effectively realize combinatorial creativity when guided by appropriate theoretical frameworks, contributing both to practical advancement of AI-assisted research and theoretical understanding of machine creativity.

翻译：科学想法的生成在创造力理论和计算创造力研究中得到了广泛探讨，为理解和实施创造性过程提供了有价值的框架。然而，近期利用大型语言模型（LLMs）进行科研想法生成的研究常常忽视了这些理论基础。我们提出了一个明确运用组合式创造力理论并基于LLMs实现的框架，该框架包含一个用于跨领域知识发现的泛化级检索系统和一个用于想法生成的结构化组合过程。检索系统通过在不同抽象层次间映射概念，使得迥异领域之间能够建立有意义的联系；而组合过程则系统地分析与重组各类要素，以生成新颖的解决方案。在OAG-Bench数据集上的实验证明了我们框架的有效性，其在生成符合实际研究进展的想法方面持续优于基线方法（在多项指标上相似度得分提升7\%–10\%）。我们的研究结果为“LLMs在适当理论框架指导下能够有效实现组合式创造力”提供了有力证据，既推动了AI辅助科研的实际进展，也深化了对机器创造力的理论理解。