arXiv:2606.16517v1 Announce Type: new Abstract: Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins. These models are built through post-training, yet how each stage shapes reasoning and generalization remains poorly understood. We study when post-training improves performance and when it induces over-specialization. Across genomics, transcriptomics, and proteins, we train and evaluate more than 100 biological reasoning models under controlled variation in backbone, continued pre-training
The proliferation of biological data across genomics, transcriptomics, and proteomics is enabling the development of advanced AI models tailored for biological understanding.
Understanding how various post-training methods shape biological reasoning models is crucial for accelerating drug discovery, materials science, and biotechnological innovation.
The ability to systematically analyze and optimize the training of biological AI models enables the creation of more accurate and generalizable tools for scientific discovery.
- · Biotechnology sector
- · Pharmaceutical companies
- · AI model developers
- · Life science researchers
- · Traditional biological research methods
- · Companies relying on less sophisticated data analysis
Improved design and efficacy of AI models for complex biological problems.
Faster development cycles for new therapies, diagnostics, and bio-engineered products.
Potential for AI to independently generate novel biological hypotheses and experimental designs, significantly altering scientific methodology.
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Read at arXiv cs.LG