Translational medicine turns underspecified development goals into evidence synthesis that must combine literature, trials, patents, and quantitative multi-omics analysis while preserving identifiers, uncertainty, and retrievable provenance. General-purpose foundation models and off-the-shelf tool-augmented or multi-agent systems are not built for this: they tend to produce single-shot answers or run open-endedly, and fall short on the auditable, scenario-specific workflows that heterogeneous biomedical sources demand. This paper introduces Ingenix BioResearcher, a scenario-guided multi-agent system that maps queries to versioned research playbooks, delegates to specialized subagents over 30+ tools and machine-learning endpoints, mixes structured database access with sandboxed code for genome-scale analyses, and applies claim-level multi-model reconciliation before editorial assembly. We evaluate BioResearcher across unit-level capabilities, open-ended biomedical reasoning, and end-to-end clinical discovery. It leads evaluated baselines on 109 single-step tests (83.49% pass rate; 0.892 average score), achieves strong biomedical benchmark performance (89.33% on BixBench-Verified-50 and the top 0.758 mean score on BaisBench Scientific Discovery), and leads on a 30-query clinical end-to-end benchmark with the highest positive hit rate (74.7% ± 3.3%) and negative clear rate (96.8% ± 0.2%). These results show broad, competitive performance across unit-level, open-ended, and end-to-end clinical evaluations.
@article{arxiv.2605.05985,
title = {BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine},
author = {Remigiusz Kinas and Joanna Krawczyk and Rafał Powalski and Przemysław Pietrzak and Agnieszka Kowalewska and Krzysztof Kolmus and Maciej Sypetkowski and Łukasz Smoliński and Tomasz Jetka},
journal= {arXiv preprint arXiv:2605.05985},
year = {2026}
}