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Empowering Biomedical Discovery with AI Agents

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arxiv 2404.02831 v2 pith:BOSAEKY5 submitted 2024-04-03 cs.AI

classification cs.AI
keywords agentsbiomedicaldiscoverylearningmodelsknowledgelargetasks
verification ladder T0 review T1 audit T2 compute T3 formal
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We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Toward Generalist Autonomous Research via Hypothesis-Tree Refinement

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    Arbor combines a coordinator, executors, and a hypothesis tree to enable cumulative autonomous research, outperforming Codex and Claude Code by over 2.5x on six real tasks and reaching 86.36% Any Medal on MLE-Bench Lite.

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