Large Language Model's Multi-Capability Alignment in Biomedical Domain

• URL: http://arxiv.org/abs/2508.04278v1
• Date: Wed, 06 Aug 2025 10:06:11 GMT
• Title: Large Language Model's Multi-Capability Alignment in Biomedical Domain
• Authors: Wentao Wu, Linqing Chen, Hanmeng Zhong, Weilei Wang,
• Abstract summary: BalancedBio is a framework for parameter-efficient biomedical reasoning.<n>It addresses multi-capability integration in domain-specific AI alignment.<n>It achieves state-of-the-art results in its parameter class.<n>Real-world deployment yields 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance.
• Score: 3.1427813443719868
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: BalancedBio is a theoretically grounded framework for parameter-efficient biomedical reasoning, addressing multi-capability integration in domain-specific AI alignment. It establishes the Biomedical Multi-Capability Convergence Theorem, proving orthogonal gradient spaces are essential to prevent capability interference for safe deployment. Key innovations include: (1) Medical Knowledge Grounded Synthetic Generation (MKGSG), extending Source2Synth with clinical workflow constraints and medical ontology validation for factual accuracy and safety; and (2) Capability Aware Group Relative Policy Optimization, deriving optimal hybrid reward weighting to maintain orthogonality in RL, using a reward model with rule-based and model-based scores adapted to biomedical tasks. Mathematical analysis proves Pareto-optimal convergence, preserving performance across capabilities. It achieves state-of-the-art results in its parameter class: domain expertise (80.95% BIOMED-MMLU, +15.32% over baseline), reasoning (61.94%, +7.75%), instruction following (67.95%, +6.44%), and integration (86.7%, +18.5%). Theoretical safety guarantees include bounds on capability preservation and clinical accuracy. Real-world deployment yields 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance. This work provides a principled methodology for biomedical AI alignment, enabling efficient reasoning with essential safety and reliability, with the 0.5B model version to be released.

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