Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908980045938688 |
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| author | Wang, Zixiang Gong, Mengjia Sun, Qiyu Xu, Jing Mao, Shuai Jin, Xin Han, Qing-Long Tang, Yang |
| author_facet | Wang, Zixiang Gong, Mengjia Sun, Qiyu Xu, Jing Mao, Shuai Jin, Xin Han, Qing-Long Tang, Yang |
| contents | With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFMs). This survey provides a systematic review and comparative analysis of classical MASs (CMASs) and LFM-based MASs (LMASs). First, within a closed-loop coordination framework, CMASs are reviewed across four fundamental dimensions: perception, communication, decision-making, and control. Beyond this framework, LMASs integrate LFMs to lift collaboration from low-level state exchanges to semantic-level reasoning, enabling more flexible coordination and improved adaptability across diverse scenarios. Then, a comparative analysis is conducted to contrast CMASs and LMASs across architecture, operating mechanism, adaptability, and application. Finally, future perspectives on MASs are presented, summarizing open challenges and potential research opportunities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18133 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures Wang, Zixiang Gong, Mengjia Sun, Qiyu Xu, Jing Mao, Shuai Jin, Xin Han, Qing-Long Tang, Yang Artificial Intelligence With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFMs). This survey provides a systematic review and comparative analysis of classical MASs (CMASs) and LFM-based MASs (LMASs). First, within a closed-loop coordination framework, CMASs are reviewed across four fundamental dimensions: perception, communication, decision-making, and control. Beyond this framework, LMASs integrate LFMs to lift collaboration from low-level state exchanges to semantic-level reasoning, enabling more flexible coordination and improved adaptability across diverse scenarios. Then, a comparative analysis is conducted to contrast CMASs and LMASs across architecture, operating mechanism, adaptability, and application. Finally, future perspectives on MASs are presented, summarizing open challenges and potential research opportunities. |
| title | Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.18133 |