Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations
Junlan Chen, Kexin Zhang, Daifeng Li, Yangyang Feng, Yuxuan Zhang, Bowen Deng
TL;DR
This work argues that scientific innovation emerges from structured recombinations of research problems and methods and leverages large language models to model, evaluate, and optimize these combinations. It introduces the Disruptive Index (DI) to quantify paradigm-shifting potential, and a three-module framework: Problem-Driven Method Exploration, Disruptive Index Prediction (with LoRA fine-tuning, entropy-aware learning, and deviation-aware alignment), and Dynamic Method Optimization with Greedy Perturbation to iteratively improve method configurations. Across DBLP, PubMed, and PatSnap, the framework demonstrates superior problem-method summarization quality and predictive accuracy for disruptiveness, outperforming strong baselines and showing robust gains in identifying high-impact combinations. The results highlight the potential for computationally guided, structurally grounded scientific ideation, while noting data availability and computational costs as avenues for future improvement and scalability.
Abstract
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.