arXiv:2512.08743v2 Announce Type: replace
Abstract: Foundation models (FMs) are increasingly assuming the role of the “brain” of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities — such as GUI interaction or integrated tool use — we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions — spanning dataset construction, evaluation, training paradigms, and safety considerations — for building FMs with native multi-agent intelligence. Read More
