Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics

ArXi:2603.20204v1 Announce Type: cross Understanding how interdisciplinary research teams converge on shared knowledge is a persistent challenge. This paper presents a novel, multi-layer, AI-driven analytical framework for mapping research convergence in interdisciplinary teams. The framework integrates large language models (LLMs), graph-based visualization and analytics, and human-in-the-loop evaluation to examine how research viewpoints are shared, influenced, and integrated over time.