BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913098547331072 |
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| author | Kinas, Remigiusz Krawczyk, Joanna Powalski, Rafał Pietrzak, Przemysław Kowalewska, Agnieszka Kolmus, Krzysztof Sypetkowski, Maciej Smoliński, Łukasz Jetka, Tomasz |
| author_facet | Kinas, Remigiusz Krawczyk, Joanna Powalski, Rafał Pietrzak, Przemysław Kowalewska, Agnieszka Kolmus, Krzysztof Sypetkowski, Maciej Smoliński, Łukasz Jetka, Tomasz |
| contents | 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% $\pm$ 3.3%) and negative clear rate (96.8% $\pm$ 0.2%). These results show broad, competitive performance across unit-level, open-ended, and end-to-end clinical evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05985 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine Kinas, Remigiusz Krawczyk, Joanna Powalski, Rafał Pietrzak, Przemysław Kowalewska, Agnieszka Kolmus, Krzysztof Sypetkowski, Maciej Smoliński, Łukasz Jetka, Tomasz Artificial Intelligence Multiagent Systems Quantitative Methods 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% $\pm$ 3.3%) and negative clear rate (96.8% $\pm$ 0.2%). These results show broad, competitive performance across unit-level, open-ended, and end-to-end clinical evaluations. |
| title | BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine |
| topic | Artificial Intelligence Multiagent Systems Quantitative Methods |
| url | https://arxiv.org/abs/2605.05985 |