TeleHyperVis: Hypergraph Visualization Enhanced Multi-Modal Retrieval-Augmented Generation for Telecom Standard QA

Retrieval-augmented generation (RAG) improves the performance of large language models in telecommunications question answering, but existing methods primarily rely on flat vector indices or binary graph structures, limiting their ability to represent higher-order associations among telecom entities. Moreover, retrieved structures are rarely presented in a form that can be jointly interpreted by users and vision-language models. We propose TeleHyperVis-RAG, a multimodal RAG framework that integrates a telecom knowledge corpus, entity–hyperedge retrieval, and multi-view hypergraph visualization. The framework employs dual entity and relation indices, configurable breadth-first neighborhood expansion, and a bipartite-plus-region collage to represent retrieved higher-order relations. We evaluate TeleHyperVis-RAG on five external telecom QA benchmarks. The results show that the proposed configuration achieves the highest observed answer accuracy among the evaluated methods, while latency effects vary across tasks. These findings provide empirical support for combining higher-order retrieval with semantically aligned hypergraph visualization in telecommunications question answering.