Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks c...
Andrew B. Sellergren, S. Kazemzadeh, Fereshteh Mahvar et al.· Nature Medicine· 530 citations· ⚡84
An extension and comprehensive real-world validation of Co-Scientist, a Gemini-based multi-agent system designed to accelerate end-to-end scientific research across hypothesis generation, experimentation, and manuscript generation, demonstrates progress toward closed-loop multi-agent scientific AI systems capable of ac...
Samuel Schmidgall, Xiao-Kai Zhu, Marian Shaw et al.· 2 citations
MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.
Xiaomin Li, Yuexing Hao, Jian Hou et al.· 1 citation
A controlled study of large language model agents across 260 configurations shows when multi-agent collaboration helps or hurts performance, and introduces a predictive model that selects the best architecture in 87% of held-out within-domain configurations.
Y. Kim, Ken Gu, Chanwoo Park et al.· Nature Machine Intelligence· 6 citations
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