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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.07350 |
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| _version_ | 1866916936533671936 |
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| author | Zhang, Jusheng Huang, Zimeng Fan, Yijia Liu, Ningyuan Li, Mingyan Yang, Zhuojie Yao, Jiawei Wang, Jian Wang, Keze |
| author_facet | Zhang, Jusheng Huang, Zimeng Fan, Yijia Liu, Ningyuan Li, Mingyan Yang, Zhuojie Yao, Jiawei Wang, Jian Wang, Keze |
| contents | As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies. We introduces Knowledge-Aware Bayesian Bandits (KABB), a novel framework that enhances multi-agent system coordination through semantic understanding and dynamic adaptation. The framework features three key innovations: a three-dimensional knowledge distance model for deep semantic understanding, a dual-adaptation mechanism for continuous expert optimization, and a knowledge-aware Thompson Sampling strategy for efficient expert selection. Extensive evaluation demonstrates KABB achieves an optimal cost-performance balance, maintaining high performance while keeping computational demands relatively low in multi-agent coordination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems Zhang, Jusheng Huang, Zimeng Fan, Yijia Liu, Ningyuan Li, Mingyan Yang, Zhuojie Yao, Jiawei Wang, Jian Wang, Keze Artificial Intelligence As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies. We introduces Knowledge-Aware Bayesian Bandits (KABB), a novel framework that enhances multi-agent system coordination through semantic understanding and dynamic adaptation. The framework features three key innovations: a three-dimensional knowledge distance model for deep semantic understanding, a dual-adaptation mechanism for continuous expert optimization, and a knowledge-aware Thompson Sampling strategy for efficient expert selection. Extensive evaluation demonstrates KABB achieves an optimal cost-performance balance, maintaining high performance while keeping computational demands relatively low in multi-agent coordination. |
| title | KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.07350 |