With over a few million publications annually, identifying transformative research opportunities requires systematic exploration of literature connections across temporal and themattic boundaries. However, traditional systematic reviews cannot scale to the volumes of modern literature, while citation-based approaches often miss implicit connections between temporally distant works. Mathematical rigor is often overlooked in the current literature analysis methods, especially in detecting research gaps. Current methods also cannot verify evidence for automatically generated research questions, often suffering from hallucination issues in citation validation. Addressing these challanges, we present ROAD-tv (Research Opportunity Discovery via Topological Data Analysis and Adversarial Multi-LLM Validation), a fve-stage framework. Our approach combines optimized topic modeling with topic-aware embeddings to create semantic spaces. It uses persistent homology to identify structural research gaps in a mathematically rigorous approach. We also integrate domain ontologies to produce semantically verified opportunities. Finally, we use debate-based validation with three different large language models and check evidence iteratively. Evaluated on more than 211K DBLP papers, ROAD-tv achieves 76% performance on benchmark evaluations, surpassing citation-aware baselines by 6%. It also identifies 12 structural gaps, with 83% validation accuracy against known research frontiers. Our proposed framework generates over 50 validated research opportunities for each gap, 62% of which are confirmed as evidence-backed through verifiable citations. We observe that current LLMs achieve only 7% success in DOI validation, necessitating URL based verification as the primary evidence mechanism. This study shows how the combination of topological techniques and multi-perspective validation allows for the systematic discovery of verifiable research opportunities that are not visible to conventional methods. It also offers reliable recommendations for conducting large-scale exploration of understudied research areas.
Afifi et al. (Thu,) studied this question.