PRISM: Agentic Retrieval with LLMs for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2510.14278v1
• Date: Thu, 16 Oct 2025 04:02:29 GMT
• Title: PRISM: Agentic Retrieval with LLMs for Multi-Hop Question Answering
• Authors: Md Mahadi Hasan Nahid, Davood Rafiei,
• Abstract summary: We introduce an Agentic Retrieval System that leverages large language models (LLMs) in a structured loop to retrieve relevant evidence with high precision and recall.<n>Our framework consists of three specialized agents: a Question Analyzer that decomposes a multi-hop question into sub-questions, a Selector that identifies the most relevant context for each sub-question, and an Adder that brings in any missing evidence.
• Score: 10.971852280240357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval plays a central role in multi-hop question answering (QA), where answering complex questions requires gathering multiple pieces of evidence. We introduce an Agentic Retrieval System that leverages large language models (LLMs) in a structured loop to retrieve relevant evidence with high precision and recall. Our framework consists of three specialized agents: a Question Analyzer that decomposes a multi-hop question into sub-questions, a Selector that identifies the most relevant context for each sub-question (focusing on precision), and an Adder that brings in any missing evidence (focusing on recall). The iterative interaction between Selector and Adder yields a compact yet comprehensive set of supporting passages. In particular, it achieves higher retrieval accuracy while filtering out distracting content, enabling downstream QA models to surpass full-context answer accuracy while relying on significantly less irrelevant information. Experiments on four multi-hop QA benchmarks -- HotpotQA, 2WikiMultiHopQA, MuSiQue, and MultiHopRAG -- demonstrates that our approach consistently outperforms strong baselines.

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