arXiv cs.AI· Shao-Jun Xia, Yuner Zhang, Huixin Zhang, Di Wang, Yifan Jiang, Liangxi Liu·· 12 小时前AI 评分31
自动化多智能体组织设计:从架构搜索到组织学习
Automated Multi-Agent Organization Design: From Architecture Search to Organizational Learning
AI 导读
研究者提出自动化多智能体组织设计(AMOD),把组织本身作为一等设计对象,显式建模智能体团队如何组成、承担哪些功能职责、如何协调与集体决策。AMOD 以执行驱动的设计循环运行:自动设计器为每个任务实例化组织,执行后检查任务结果、中间产物与交互轨迹,识别缺失、重复或无效的职责并据此修订组织。研究还提出在匹配资源预算下通过移除职责、替换为通用角色或改变分配来检验所发现的职责。
正文 · 原文
Abstract:Agentic system design is increasingly shifting from hand-crafted prompts and workflows toward automated search over agentic programs and interaction structures, with multi-agent systems receiving growing attention. Yet a central question remains unresolved: when does an automatically generated multi-agent structure exhibit a functionally meaningful division of responsibility? We argue that automated multi-agent design should move beyond searching for effective architectures toward identifying which responsibilities are functionally necessary, how they should be allocated, and when they should change. To explore this, we propose Automated Multi-Agent Organization Design (AMOD), which treats organization itself as a first-class design object, explicitly modeling how agent teams are composed, what functional responsibilities the agents assume, how they coordinate, and how collective decisions are made. We formulate AMOD as an execution-driven design loop. For each task, an automated designer instantiates an organization by assigning functional responsibilities and coordination rules to a set of agents. After execution, task outcomes, intermediate artifacts, and interaction traces are examined to identify which responsibilities may be missing, duplicated, or ineffective, and the organization is revised accordingly. We also propose testing discovered responsibilities by removing them, replacing them with generic roles, or changing their assignment under matched resource budgets, with complementary evaluations. AMOD provides a principled foundation for evidence-based and adaptive multi-agent organization design.
| Comments: | First draft: 4 pages, 3 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.27531 [cs.AI] |
| (or arXiv:2610.27531v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27531 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Huixin Zhang [view email]
[v1]
Tue, 20 Oct 2026 02:47:52 UTC (206 KB)
来源:arXiv cs.AI · arxiv.org