SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2510.15682v1
• Date: Fri, 17 Oct 2025 14:20:55 GMT
• Title: SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation
• Authors: Ines Besrour, Jingbo He, Tobias Schreieder, Michael Färber,
• Abstract summary: SQuAI is a scalable and trustworthy multi-agent retrieval-augmented generation framework for scientific question answering.<n>Built on over 2.3 million full-text papers from arXiv.org, SQuAI employs four collaborative agents to decompose complex questions into sub-questions.<n>Our system improves faithfulness, answer relevance, and contextual relevance by up to +0.088 (12%) over a strong RAG baseline.
• Score: 4.224843546370802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present SQuAI (https://squai.scads.ai/), a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large language models (LLMs). SQuAI addresses key limitations of existing RAG systems in the scholarly domain, where complex, open-domain questions demand accurate answers, explicit claims with citations, and retrieval across millions of scientific documents. Built on over 2.3 million full-text papers from arXiv.org, SQuAI employs four collaborative agents to decompose complex questions into sub-questions, retrieve targeted evidence via hybrid sparse-dense retrieval, and adaptively filter documents to improve contextual relevance. To ensure faithfulness and traceability, SQuAI integrates in-line citations for each generated claim and provides supporting sentences from the source documents. Our system improves faithfulness, answer relevance, and contextual relevance by up to +0.088 (12%) over a strong RAG baseline. We further release a benchmark of 1,000 scientific question-answer-evidence triplets to support reproducibility. With transparent reasoning, verifiable citations, and domain-wide scalability, SQuAI demonstrates how multi-agent RAG enables more trustworthy scientific QA with LLMs.

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