AGAO: Adaptive Goal-aware Attention Orchestration for Multi-Agent LLM Systems
A novel framework named Adaptive Goal-aware Attention Orchestration (AGAO) tackles the issue of attention distribution in graph-based multi-agent systems that utilize large language models. Traditional methods become inefficient as workflows expand, applying the same treatment to all graph elements. AGAO assesses the significance of agents in real-time, taking into account user goals, graph interdependencies, and computational limitations. It integrates goal-aware attention, which evaluates the semantic relevance between user objectives and agent skills, along with additional components to enhance coordination. This framework advances Transformer-style attention from token representations to the coordination of agents at the workflow level. The paper can be found on arXiv, reference 2607.23678.
Key facts
- AGAO is introduced as a paradigm for attention allocation in multi-agent graph systems.
- The framework extends Transformer-style attention to workflow-level coordination.
- AGAO dynamically estimates agent importance based on user objectives, graph dependencies, and computational constraints.
- It combines goal-aware attention with other components.
- The paper is available on arXiv with ID 2607.23678.
- Large language models enable autonomous agents for reasoning, planning, and tool use.
- Graph-based orchestration supports flexible decomposition but creates attention allocation challenges.
- Existing approaches execute graph components uniformly, wasting resources on irrelevant tasks.
Entities
Institutions
- arXiv