面向生物学家的AI：代理系统时代基础模型的知识边界
AI for Biologists: The Knowledge Boundaries of Foundation Models in the Age of Agentic Systems
Tian Cai, Andrew Kleist, Jaimin Patel, Duccio Malinverni, Alexander K. Taylor, Malvika Sharan, Renato Umeton, M. Madan Babu
AI 速读 基于摘要
这篇综述讨论生物基础模型与基于大语言模型的代理系统，提出用数据、架构、训练方案、评估方案和后分析五个设计要素来界定模型输出的可靠范围（知识边界）。
q-bio.BM · cs.AI
查看原文摘要
Artificial intelligence (AI) is changing how biological research is done. Biological foundation models trained on millions of nucleotides, protein, and single-cell measurements predict protein structures, mutation effects, and cellular responses to genetic perturbations. Agentic systems, AI assistants built on large language models, can run multistep analyses from plain-language requests and increasingly call foundation models as tools. Yet it is often unclear when these models' outputs can be trusted. We frame this as a model's knowledge boundary: the range of inputs, tasks, and conditions over which its outputs are reliable. This review explains how foundation models are built and proposes that five design elements (data, architecture, training scheme, evaluation scheme, and post-analysis) define this boundary. We examine evidence on where current models succeed or fail and present standardized descriptions of model limits so that researchers, and the agents acting for them, can use these tools reliably.