[2602.23949] HotelQuEST: Balancing Quality and Efficiency in Agentic Search

Computer Science > Information Retrieval arXiv:2602.23949 (cs) [Submitted on 27 Feb 2026] Title:HotelQuEST: Balancing Quality and Efficiency in Agentic Search Authors:Guy Hadad, Shadi Iskander, Oren Kalinsky, Sofia Tolmach, Ran Levy, Haggai Roitman View a PDF of the paper titled HotelQuEST: Balancing Quality and Efficiency in Agentic Search, by Guy Hadad and 5 other authors View PDF HTML (experimental) Abstract:Agentic search has emerged as a promising paradigm for adaptive retrieval systems powered by large language models (LLMs). However, existing benchmarks primarily focus on quality, overlooking efficiency factors that are critical for real-world deployment. Moreover, real-world user queries often contain underspecified preferences, a challenge that remains largely underexplored in current agentic search evaluation. As a result, many agentic search systems remain impractical despite their impressive performance. In this work, we introduce HotelQuEST, a benchmark comprising 214 hotel search queries that range from simple factual requests to complex queries, enabling evaluation across the full spectrum of query difficulty. We further address the challenge of evaluating underspecified user preferences by collecting clarifications that make annotators' implicit preferences explicit for evaluation. We find that LLM-based agents achieve higher accuracy than traditional retrievers, but at substantially higher costs due to redundant tool calls and suboptimal routing that fails...