[2603.20513] ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation

Computer Science > Information Retrieval arXiv:2603.20513 (cs) [Submitted on 20 Mar 2026] Title:ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation Authors:Anton Korikov, Scott Sanner View a PDF of the paper titled ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation, by Anton Korikov and 1 other authors View PDF HTML (experimental) Abstract:LLM-reranking is limited by the top-k documents retrieved by vector similarity, which neither enables contextual query-document token interactions nor captures multimodal relevance distributions. While LLM query reformulation attempts to improve recall by generating improved or additional queries, it is still followed by vector similarity retrieval. We thus propose to address these top-k retrieval stage failures by introducing ReBOL, which 1) uses LLM query reformulations to initialize a multimodal Bayesian Optimization (BO) posterior over document relevance, and 2) iteratively acquires document batches for LLM query-document relevance scoring followed by posterior updates to optimize relevance. After exploring query reformulation and document batch diversification techniques, we evaluate ReBOL against LLM reranker baselines on five BEIR datasets and using two LLMs (Gemini-2.5-Flash-Lite, GPT-5.2). ReBOL consistently achieves higher recall and competitive rankings, for example compared to the best LLM reranker on the Robust04 da...