This Review examines volumetric foundation models, language alignment and compression strategies, and agentic systems that extend MLLMs through planning, tools, memory, and workflow interaction, and introduces a Claim-Design-Validation framework to assess whether technical, workflow, and clinical claims are matched by appropriate design and validation.
Zanting Ye, Shengyuan Liu, Xin Liu et al.· 0 citations
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