Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm
Score
81首次将经典组织理论系统应用于多智能体LLM系统，并通过严谨消融实验揭示责任分配与模型族的关系，信息增量极大。
方法设计清晰、实验对照严谨（固定协议比较+消融），逻辑链条完整，方法论与结论均有充分支撑。
提供可配置的工程框架和自适应路由算法，直接指导多智能体系统的部署与调优，但短期内落地需适配具体接口。
当前多智能体系统设计缺乏理论指导，该论文正好填补这一空白，且arXiv发表日期为最新。
arXiv预印本，作者机构为学术机构，引用文献规范，工作基于已有开源基准和LLM评估，可信度高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper is the first to systematically introduce organizational theory into multi-agent LLM systems with rigorous empirical validation, high information density and methodological novelty.
AI commentary
「This paper brings real organizational theory from management science into multi-agent systems, not just as an analogy but as executable, verifiable configurations. For engineers and researchers working on agent orchestration, it offers both principled insights and practical recipes—especially the finding that organizational design is not one-size-fits-all, but must be recalibrated for each model family.」
Quick overview
Multi-agent frameworks often entangle organization, coordination, and collaboration protocol. This paper proposes IMACS, which decouples these three into orthogonal, swappable layers, making classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) an executable configuration, and unifies six published collaboration algorithms behind a common interface. It further introduces Adaptive Org Routing, a contextual-bandit meta-protocol that selects a protocol per task under an explicit quality-cost tradeoff, outperforming every fixed protocol in a controlled study. Ablations reveal that accountability placement effects depend on whether the protocol routes the deliverable through the accountable agent, and the winning placement flips across model families, proving that organizational design must be revalidated or learned for each model binding.
- Proposes IMACS framework decoupling organization (Who), coordination (How), and collaboration protocol (Which Algorithm) in multi-agent systems.
- Maps classic organizational theories (Belbin roles, Mintzberg coordination, RACI accountability) into executable configurations.
- Adaptive Org Routing, a contextual-bandit meta-protocol, dynamically selects the best collaboration protocol per task.
- Experiments show accountability placement effects depend on protocol routing, and optimal design varies across model families, forbidding hard-coding.
Full text
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