Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks

Excerpt

arXiv:2610.05750v1 Announce Type: cross Abstract: Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level semantic similarity, which is insufficient for increasingly complex search applications. Here, we investigate agentic retrieval that combines the reasoning capabilities of Large Language Models (LLMs) with the efficient corpus exploration of retrievers in a ReAct agentic