AI 中文总结
研究多智能体图系统中注意力分配问题，提出自适应目标感知注意力编排框架AGAO，结合目标、拓扑、资源感知注意力，将静态图转变为自适应系统，经实验验证其能提高任务有效性并减少计算等消耗，确立注意力工程方向。
AI 中文摘要
大语言模型使自主智能体能够进行推理、规划和工具使用。近期系统越来越多地将这些智能体组织成由专门的、相互连接的节点构成的图。基于图的编排虽支持灵活分解与协调，但带来了注意力分配这一关键挑战。我们引入了注意力编排范式，将Transformer风格的注意力从令牌表示扩展到工作流级别的智能体协调。我们的框架AGAO基于用户目标、图依赖关系和计算约束动态估计智能体重要性，它结合了目标感知注意力、拓扑感知注意力和资源感知注意力三个组件，能将静态智能体图转变为自适应系统。实验表明，与现有基于图的执行策略相比，AGAO提高了任务有效性，同时减少了不必要的计算、延迟和令牌消耗。我们的工作确立了注意力工程作为可扩展的智能多智能体系统的一个方向。
英文摘要
Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use. Recent systems increasingly organize these agents as graphs of specialized, interconnected nodes. Although graph-based orchestration supports flexible decomposition and coordination, it creates a key challenge: \textbf{attention allocation}. As workflows grow, existing approaches often execute graph components uniformly, wasting resources on irrelevant or low-impact tasks. We introduce \textbf{Attention Orchestration}, a paradigm that extends Transformer-style attention from token representations to workflow-level agent coordination. Our framework, \textbf{Adaptive Goal-aware Attention Orchestration (AGAO)}, dynamically estimates agent importance based on user objectives, graph dependencies, and computational constraints. AGAO combines three components: (1) goal-aware attention, measuring semantic relevance between user goals and agent capabilities; (2) topology-aware attention, modeling structural dependencies in agent graphs; and (3) resource-aware attention, allocating budgets and execution priorities across heterogeneous agents. Together, these mechanisms transform static agent graphs into adaptive systems that focus computation on goal-critical reasoning paths. Experiments across diverse multi-agent workloads show that AGAO improves task effectiveness while reducing unnecessary computation, latency, and token consumption compared with existing graph-based execution strategies. Our work establishes \textbf{Attention Engineering} as a direction for scalable, intelligent multi-agent systems. Code: https://github.com/MingzhouFan97/AGAO.
