AI科学家能否在运行时协调？
原标题：Can AI Scientists Coordinate at Runtime?
AI 摘要
该论文提出运行时智能体协调（RAC）框架，用于多智能体AI科学家系统，在运行时根据当前研究状态选择智能体、分配工作契约并进行基于产物的验证。作者在Agent Laboratory、EvoScientist和ARK三个宿主系统上、在匹配预算下进行了单种子探索性评估，发现运行时选择能提升各宿主的平均得分，但加入契约与验证后得分反而下降，且效果因宿主而异。研究结论支持运行时协调的价值，同时揭示了在预算受限下额外协调机制的局限。
正文节选
Can AI Scientists Coordinate at Runtime? Abstract Multi-agent AI scientists have shown improving performance across a diverse range of tasks. Yet a common approach is design-time agentic orchestration, which typically relies on fixed workflows. In contrast, human scientists coordinate and adjust their division of labor at runtime. We therefore ask: can AI scientists also coordinate at runtime? To this end, we introduce Runtime Agent Coordination (RAC), which selects agents from existing AI-scien