A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search
Ad hoc dataset search requires matching underspecified natural-language queries against sparse, heterogeneous metadata records, a task where typical lexical or dense retrieval alone falls short. We reposition dataset search as a software-architecture problem and propose a bounded, auditable reference architecture for agentic hybrid retrieval that combines BM25 lexical search with dense-embedding retrieval via reciprocal rank fusion (RRF), orchestrated by a large language model (LLM) agent that repeatedly plans queries, evaluates the sufficiency of results, and reranks candidates. To reduce the vocabulary mismatch between user intent and provider-authored metadata, we introduce an offline ...
| Authors | Riccardo Terrenzi, Phongsakon Mark Konrad, Tim Lukas Adam et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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