The emergence of large language models offers new possibilities for structured exploration of scientific knowledge. Rather than viewing scientific discovery as isolated ideas or content, we propose a structured approach that emphasizes the role of method combinations in shaping disruptive insights. Specifically, we investigate how knowledge unit--especially those tied to methodological design--can be modeled and recombined to yield research breakthroughs. Our proposed framework addresses two key challenges. First, we introduce a contrastive learning-based mechanism to identify distinguishing features of historically disruptive method combinations within problem-driven contexts. Second, we propose a reasoning-guided Monte Carlo search algorithm that leverages the chain-of-thought capability of LLMs to identify promising knowledge recombinations for new problem statements.Empirical studies across multiple domains show that the framework is capable of modeling the structural dynamics of innovation and successfully highlights combinations with high disruptive potential. This research provides a new path for computationally guided scientific ideation grounded in structured reasoning and historical data modeling.

本研究解决了科学发现过程中孤立观点的局限，提出一种结构化方法强调方法组合在形成突破性见解中的作用。通过对历史上具有颠覆性的研究方法组合进行对比学习，并结合引导性蒙特卡洛搜索算法，本研究展示了其在多个领域识别高潜力知识组合的能力，为基于结构化推理和历史数据建模的科学创意提供了新路径。

科学创新的结构化：一种影响知识组合建模和发现的框架