Editorial: The Year of Retrieval�From BM25 to Multi-Vector Indexes
Abstract
We reflect on a rapid shift from sparse retrieval to hybrid and multi-vector stores, outlining open problems in consistency, observability, and cost control for RAG-heavy stacks.
Cite this article
Hosseini, A. & Fischer, N. (2025). Editorial: The Year of Retrieval�From BM25 to Multi-Vector Indexes. Research Explorations in Global Knowledge & Technology (REGKT), 3 (5). Retrieved from https://regkt.com/article.php?id=118&slug=editorial-the-year-of-retrieval-from-bm25-to-multi-vector-indexes