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bioRxiv · 10.64898/2025.12.22.696019

Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature

Punuru, P.·Ibtehaz, N.·Giri, S. J.·Srirangam, H.·Tugolukova, E.·Kihara, D.
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Abstract

The rapid expansion of biomedical literature has made comprehensive manual synthesis increasingly difficult to perform effectively, creating a pressing need for AI systems capable of reasoning across verified evidence rather than merely retrieving it. However, existing retrieval-augmented generation (RAG) methods often fall short when faced with complex biomedical questions that require iterative reasoning and multi-step synthesis. Here, we developed Queryome, a deep research system consisting of specialized large language model (LLM) agents that can adapt their orchestration dynamically to a wide range of queries. Using a hybrid semantic-lexical retrieval engine spanning 28.3 million PubMed abstracts, it performs iterative, evidence-grounded synthesis. On the MIRAGE benchmark, Queryome achieved 88.98 % accuracy, surpassing prior systems by up to 14 points, and improved reasoning accuracy on the biomedical Humans Last Exam (HLE) subset from 15.8% to 19.3%. Moreover, in a task for constructing a review article, it earned the highest composite score in comparison with Deep Research from OpenAI, Google, Perplexity, and Scite.AI, reflecting its strong literature retrieval and synthesis capabilities.

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Punuru, P., Ibtehaz, N., Giri, S. J., Srirangam, H., Tugolukova, E., Kihara, D.. 2025-12-24. Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature. https://doi.org/10.64898/2025.12.22.696019

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