用大模型分析方法组合，发现有颠覆潜力的科研新思路。
Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations
- 基于对比学习识别历史上颠覆性方法组合的特征。
- 利用LLM链式思维搜索，发现新问题的高潜力知识重组方案。
- 适合想借助计算发现创新方向的研究者和机构。
大型语言模型为科学知识的结构化探索提供了新可能。我们提出一种新框架，强调方法组合在催生突破性洞察中的核心作用，而非孤立看待科学发现。该框架聚焦于与方法设计相关的知识单元，研究其建模与重组机制以实现研究突破。针对两大挑战：一是引入基于对比学习的机制，在问题驱动背景下识别历史上具有颠覆性的方法组合的独特特征；二是提出一种推理引导的蒙特卡洛搜索算法，利用大模型的链式思维能力，为新问题生成有前景的知识重组方案。多领域实证研究表明，该框架能有效建模创新的结构性动态，并成功识别出具有高颠覆潜力的方法组合。本研究为基于结构化推理与历史数据建模的计算辅助科学构想提供了新路径。
原文摘要 · Abstract (English)
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.
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