RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering

• URL: http://arxiv.org/abs/2602.18425v1
• Date: Fri, 20 Feb 2026 18:48:05 GMT
• Title: RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering
• Authors: Deniz Qian, Hung-Ting Chen, Eunsol Choi,
• Abstract summary: retrieve-verify-retrieve (RVR) is a multi-round retrieval framework designed to maximize answer coverage.<n>Our work presents a promising iterative approach for comprehensive answer recall leveraging a verifier and adapting retrievers to a new inference scenario.
• Score: 32.83942046822049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comprehensively retrieving diverse documents is crucial to address queries that admit a wide range of valid answers. We introduce retrieve-verify-retrieve (RVR), a multi-round retrieval framework designed to maximize answer coverage. Initially, a retriever takes the original query and returns a candidate document set, followed by a verifier that identifies a high-quality subset. For subsequent rounds, the query is augmented with previously verified documents to uncover answers that are not yet covered in previous rounds. RVR is effective even with off-the-shelf retrievers, and fine-tuning retrievers for our inference procedure brings further gains. Our method outperforms baselines, including agentic search approaches, achieving at least 10% relative and 3% absolute gain in complete recall percentage on a multi-answer retrieval dataset (QAMPARI). We also see consistent gains on two out-of-domain datasets (QUEST and WebQuestionsSP) across different base retrievers. Our work presents a promising iterative approach for comprehensive answer recall leveraging a verifier and adapting retrievers to a new inference scenario.

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