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BioR5: A Three-Layer Architecture for Biological Reasoning in Scientific AI

Workshop: Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities

Authors: Peng Ding (University of Chicago, Argonne National Laboratory (ANL)); Thomas Brettin (Argonne National Laboratory (ANL)); and Rick Stevens (Argonne National Laboratory (ANL), University of Chicago)

Abstract: Biological research requires diverse reasoning modes, from phylogenetic analysis to mechanistic understanding, each demanding specific methods and data types. Current AI systems typically employ single methodologies, limiting effectiveness in complex biological domains. We present BioR5, a three-layer architecture implementing eleven distinct biological reasoning modes with intelligent triage and specialized tool integration. Layer A provides parametric memory via large language models, Layer B incorporates specialized foundation models for multimodal data, and Layer C connects external databases and computational tools. Our system features an intelligent reasoning mode selection combining keyword matching with LLM analysis to choose appropriate strategies automatically. We demonstrate the framework through toxicology specialization, integrating TX-Gemma predictions with PubChem, ToxCast, and ChEMBL data. The open-source implementation supports dynamic registration of new reasoning modes and tools, enabling collaborative development and community-driven expansion. BioR5 represents an architecture-first approach to developing reasoning-mode-aware AI systems that scale easily with new biological use cases.

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