Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems

• URL: http://arxiv.org/abs/2607.23678v1
• Date: Sun, 26 Jul 2026 14:23:33 GMT
• Title: Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems
• Abstract summary: Large language models (LLMs) enable autonomous agents for reasoning, planning, computation and tool use.<n>Recent systems increasingly organize these agents as graphs of specialized, interconnected nodes.<n>Existing approaches often execute graph components uniformly, wasting resources on irrelevant or low-impact tasks.<n>We introduce textbfAttention Orchestration, a paradigm that extends Transformer-style attention from token representations to workflow-level agent coordination.
• Score: 28.41165257733149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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