Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration
arXiv:2511.02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models. However, the full potential of such systems is hindered by rigid agent scheduling and inefficient coordination strategies that fail to adapt to evolving task requirements. In this paper, we propose STRMAC, a state-aware routing framework designed for efficient collaboration in multi
The rapid advancement of large language models (LLMs) has enabled complex multi-agent systems, but current coordination strategies are proving inefficient, necessitating more adaptive frameworks.
Efficient multi-agent collaboration is critical for scaling AI's capabilities beyond individual models, influencing the autonomous deployment of AI in white-collar workflows and complex problem-solving.
The development of state-aware routing frameworks like STRMAC signifies a move towards more intelligent and adaptive AI orchestration, improving the practical utility and robustness of multi-agent systems.
- · AI developers
- · SaaS providers leveraging AI agents
- · Industries with complex task automation needs
- · Companies relying on rigid, human-intensive coordination for complex tasks
- · Inefficient AI agent orchestration platforms
Improved efficiency and performance of multi-agent AI systems in diverse applications.
Accelerated deployment of autonomous AI agents across various professional domains, leading to workflow automation and cost reductions.
Increased demand for robust and secure AI agent collaboration platforms, potentially reshaping the competitive landscape for AI software providers.
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Read at arXiv cs.AI