Position: Towards a Responsible LLM-empowered Multi-Agent Systems
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915135529943040 |
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| author | Hu, Jinwei Dong, Yi Ao, Shuang Li, Zhuoyun Wang, Boxuan Singh, Lokesh Cheng, Guangliang Ramchurn, Sarvapali D. Huang, Xiaowei |
| author_facet | Hu, Jinwei Dong, Yi Ao, Shuang Li, Zhuoyun Wang, Boxuan Singh, Lokesh Cheng, Guangliang Ramchurn, Sarvapali D. Huang, Xiaowei |
| contents | The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. Tools like LangChain and Retrieval-Augmented Generation have expanded LLM capabilities, enabling deeper integration into MAS through enhanced knowledge retrieval and reasoning. However, these advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound across interactions, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential. Such an approach enhances traditional passive oversight by facilitating coherent inter-agent communication and effective system governance, allowing MAS to achieve desired outcomes more efficiently. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position: Towards a Responsible LLM-empowered Multi-Agent Systems Hu, Jinwei Dong, Yi Ao, Shuang Li, Zhuoyun Wang, Boxuan Singh, Lokesh Cheng, Guangliang Ramchurn, Sarvapali D. Huang, Xiaowei Multiagent Systems Artificial Intelligence The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. Tools like LangChain and Retrieval-Augmented Generation have expanded LLM capabilities, enabling deeper integration into MAS through enhanced knowledge retrieval and reasoning. However, these advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound across interactions, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential. Such an approach enhances traditional passive oversight by facilitating coherent inter-agent communication and effective system governance, allowing MAS to achieve desired outcomes more efficiently. |
| title | Position: Towards a Responsible LLM-empowered Multi-Agent Systems |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2502.01714 |