AI 中文总结
研究多智能体大语言模型系统中组织、协调与协作协议问题，IMACS 将三者分离，通过对照比较和自适应组织路由学习协议，揭示责任分配位置影响结果，强调组织设计需依模型重新验证或学习。
AI 中文摘要
基于大语言模型构建的多智能体框架通常将三个逻辑上不同的问题纠缠在一起：团队成员是谁（组织）、成员如何协调以及哪种算法融合他们的工作（协作协议）。IMACS（智能多智能体协作系统）将这三个问题分离到正交的、可独立互换的层中。经典组织理论（贝尔宾角色、明茨伯格协调、RACI 责任分配）成为可执行、经过验证的配置，该框架将六种已发表的协作算法置于通用接口之后，同时将角色、协调和责任分配作为可独立配置的因素公开。我们利用这种分离进行对照比较，其中组织分配变化而协作协议保持固定。它还将协议选择变成一个可以学习的变量：自适应组织路由，一种上下文博弈元协议，在明确的质量成本权衡下为每个任务选择一种协议，在对照研究中优于每种固定协议，并根据实际基准和大语言模型评判奖励进行在线训练。消融实验揭示了一种机制。当协议通过负责的智能体路由可交付成果时，责任分配的位置会改变结果，并且获胜的位置在不同模型家族中会翻转，因此组织设计不能硬编码；必须针对每个模型绑定重新验证或学习。
英文摘要
Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol). IMACS (Intelligent Multi-Agent Collaboration System) separates the three into orthogonal, independently swappable layers. Classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) becomes executable, validated configuration, and the framework places six published collaboration algorithms behind a common interface while exposing roles, coordination, and accountability as independently configurable factors. We use this separation to conduct controlled comparisons in which organizational assignments vary while the collaboration protocol is held fixed. It also turns protocol choice into a variable that can be learned: Adaptive Org Routing, a contextual-bandit meta-protocol, selects a protocol per task under an explicit quality-cost tradeoff, outperforms every fixed protocol in a controlled study, and trains online on real benchmark and LLM-judge rewards. The ablations expose a mechanism. Accountability placement changes outcomes exactly when the protocol routes the deliverable through the accountable agent, and the winning placement flips across model families, so organizational design cannot be hard-coded; it must be revalidated, or learned, for each model binding.
Comments8 pages, 2 figures
