Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2511.02200v1
• Date: Tue, 04 Nov 2025 02:41:14 GMT
• Title: Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration
• Authors: Jingbo Wang, Sendong Zhao, Haochun Wang, Yuzheng Fan, Lizhe Zhang, Yan Liu, Ting Liu,
• Abstract summary: STRMAC is a state-aware routing framework designed for efficient collaboration in multi-agent systems.<n>Our method encodes interaction history and agent knowledge to power the router, which adaptively selects the most suitable single agent at each step.
• Score: 20.982210711890513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-agent systems. Our method separately encodes interaction history and agent knowledge to power the router, which adaptively selects the most suitable single agent at each step for efficient and effective collaboration. Furthermore, we introduce a self-evolving data generation approach that accelerates the collection of high-quality execution paths for efficient system training. Experiments on challenging collaborative reasoning benchmarks demonstrate that our method achieves state-of-the-art performance, achieving up to 23.8% improvement over baselines and reducing data collection overhead by up to 90.1% compared to exhaustive search.

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