Queryome: orchestrating retrieval, reasoning, and synthesis across biomedical literature

The rapid expansion of biomedical literature has made comprehensive manual synthesis increasingly difficult, creating a pressing need for AI systems capable of reasoning over verified evidence rather than merely retrieving it. Existing retrieval-augmented generation (RAG) methods often struggle with complex biomedical queries that require accurate citation, iterative reasoning, and multi-step evidence synthesis. To address this challenge, we developed Queryome, a deep research system composed of specialized large language model (LLM) agents that dynamically adapt their orchestration to the complexity of each query. Queryome operates on a hybrid semantic-lexical retrieval engine covering 28.3 million PubMed abstracts and performs iterative, evidence-grounded synthesis through a hierarchical multi-agent architecture.

On the MIRAGE benchmark, Queryome achieved 89.93% accuracy, outperforming the best prior system by up to 9.96% points. On the biomedical subset of Humanity's Last Exam (HLE), it improved reasoning accuracy from 14.6% to 24.9% relative to the base reasoning model. In a review article construction task, Queryome achieved the highest composite score among deep research systems from OpenAI, Google, Perplexity, and Scite.ai, demonstrating strong capabilities in literature retrieval and synthesis.

Queryome demonstrates that hierarchical, dynamically orchestrated multi-agent systems can substantially enhance evidence-based reasoning and synthesis over biomedical literature. Its consistent performance gains across diverse benchmarks suggest that agentic RAG architectures offer a promising path toward reliable, citation-grounded biomedical knowledge synthesis at scale.