"Identifying Disruptive Technologies via Dynamic Hypergraph Modeling and Large Language Models"

"Identification of potential disruptive technologies is crucial for formulating science, technology, and innovation policies. Existing studies not only exhibit limitations in semantic analysis and multi-dimensional relationship modeling but also fail to incorporate external influences such as policy, market, and social factors. To address these limitations, a large language model (LLM) is employed to extract sentence-level technical directions from heterogeneous scientific texts for topic modeling with BERTopic. Subsequently, multiple co-occurring themes within individual documents are synthesized with themes extracted from their respective citing literature to construct hyperedges, thereby encapsulating the underlying mechanisms of knowledge recombination and evolutionary trajectories. Furthermore, tracking the evolution of dynamic hypergraphs across time slices facilitates the extraction of multidimensional signals, thereby enabling a more forward-looking and systematic detection of early-stage disruptive technology seeds. Finally, we leverage an LLM to architect a multi-agent system incorporating perspectives from technology, enterprises, policy, industry associations, and venture capital to simulate the inherent conflicts and potential coordination of evaluation criteria among different stakeholders, thereby filtering candidate technologies with the highest market development potential. We applied our framework to an empirical study on hydrogen energy, underscoring the method\u2019s scientific rigor and practical utility. Our approach provides an interpretable and scalable paradigm for pinpointing disruptive technologies transcending the limits of traditional bibliometrics."