BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model

• URL: http://arxiv.org/abs/2505.23579v1
• Date: Thu, 29 May 2025 15:49:27 GMT
• Title: BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
• Authors: Adibvafa Fallahpour, Andrew Magnuson, Purav Gupta, Shihao Ma, Jack Naimer, Arnav Shah, Haonan Duan, Omar Ibrahim, Hani Goodarzi, Chris J. Maddison, Bo Wang,
• Abstract summary: We introduce BioReason, a pioneering architecture that integrates a DNA foundation model with a Large Language Model.<n>BioReason's sophisticated multi-step reasoning is developed through supervised fine-tuning and targeted reinforcement learning.<n>On biological reasoning benchmarks, BioReason demonstrates an average 15% performance gain over strong single-modality baselines.
• Score: 12.596088399210581
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Unlocking deep, interpretable biological reasoning from complex genomic data is a major AI challenge hindering scientific discovery. Current DNA foundation models, despite strong sequence representation, struggle with multi-step reasoning and lack inherent transparent, biologically intuitive explanations. We introduce BioReason, a pioneering architecture that, for the first time, deeply integrates a DNA foundation model with a Large Language Model (LLM). This novel connection enables the LLM to directly process and reason with genomic information as a fundamental input, fostering a new form of multimodal biological understanding. BioReason's sophisticated multi-step reasoning is developed through supervised fine-tuning and targeted reinforcement learning, guiding the system to generate logical, biologically coherent deductions. On biological reasoning benchmarks including KEGG-based disease pathway prediction - where accuracy improves from 88% to 97% - and variant effect prediction, BioReason demonstrates an average 15% performance gain over strong single-modality baselines. BioReason reasons over unseen biological entities and articulates decision-making through interpretable, step-by-step biological traces, offering a transformative approach for AI in biology that enables deeper mechanistic insights and accelerates testable hypothesis generation from genomic data. Data, code, and checkpoints are publicly available at https://github.com/bowang-lab/BioReason

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