Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908366603812864 |
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| author | Xu, Baixuan Li, Chunyang Wang, Weiqi Fan, Wei Zheng, Tianshi Shi, Haochen Fan, Tao Song, Yangqiu Yang, Qiang |
| author_facet | Xu, Baixuan Li, Chunyang Wang, Weiqi Fan, Wei Zheng, Tianshi Shi, Haochen Fan, Tao Song, Yangqiu Yang, Qiang |
| contents | Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically investigate how collaborative reasoning performance is affected by three key design dimensions: (1) Expertise-Domain Alignment, (2) Collaboration Paradigm (structured workflow vs. diversity-driven integration), and (3) System Scale. Our findings reveal that expertise alignment benefits are highly domain-contingent, proving most effective for contextual reasoning tasks. Furthermore, collaboration focused on integrating diverse knowledge consistently outperforms rigid task decomposition. Finally, we empirically explore the impact of scaling the multi-agent system with expertise specialization and study the computational trade off, highlighting the need for more efficient communication protocol design. This work provides concrete guidelines for configuring specialized multi-agent system and identifies critical architectural trade-offs and bottlenecks for scalable multi-agent reasoning. The code will be made available upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study Xu, Baixuan Li, Chunyang Wang, Weiqi Fan, Wei Zheng, Tianshi Shi, Haochen Fan, Tao Song, Yangqiu Yang, Qiang Computation and Language Artificial Intelligence Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically investigate how collaborative reasoning performance is affected by three key design dimensions: (1) Expertise-Domain Alignment, (2) Collaboration Paradigm (structured workflow vs. diversity-driven integration), and (3) System Scale. Our findings reveal that expertise alignment benefits are highly domain-contingent, proving most effective for contextual reasoning tasks. Furthermore, collaboration focused on integrating diverse knowledge consistently outperforms rigid task decomposition. Finally, we empirically explore the impact of scaling the multi-agent system with expertise specialization and study the computational trade off, highlighting the need for more efficient communication protocol design. This work provides concrete guidelines for configuring specialized multi-agent system and identifies critical architectural trade-offs and bottlenecks for scalable multi-agent reasoning. The code will be made available upon acceptance. |
| title | Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.07313 |