New framework optimizes multi-agent LLM collaboration with state-aware routing

Researchers have developed STRMAC, a novel state-aware routing framework designed to enhance collaboration within multi-agent systems powered by large language models. This framework encodes interaction history and agent knowledge separately to enable adaptive selection of the most suitable agent at each step, thereby improving efficiency and effectiveness. Additionally, a self-evolving data generation approach was introduced to expedite the collection of high-quality execution paths for system training. Experiments on collaborative reasoning benchmarks show STRMAC achieving state-of-the-art performance with significant improvements over existing methods and a substantial reduction in data collection overhead. AI

IMPACT This framework could significantly improve the efficiency and effectiveness of complex task-solving by LLM-powered multi-agent systems.

RANK_REASON This is a research paper detailing a new framework for multi-agent systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Jingbo Wang
• large language models

• paper
• infra

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