这篇论文提出了AGAO，像给智能体图装了注意力聚焦，减少无用计算，提升任务效果，值得搞多智能体系统的人看看。
论文提出 AGAO（Adaptive Goal-aware Attention Orchestration）框架，将 Transformer 式的注意力机制从令牌表示扩展到多智能体工作流级编排。AGAO 包含三个组件：目标感知注意力（评估用户目标与智能体能力的语义相关性）、拓扑感知注意力（建模智能体图的结构依赖）和资源感知注意力（分配异构智能体的预算和执行优先级）。实验在多种多智能体工作负载上显示，相比现有图执行策略，AGAO 在提升任务效果的同时减少了计算、延迟和令牌消耗。该工作将注意力工程确立为可扩展、智能多智能体系统的一个方向。
Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems
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.