OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains

Abstract:Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms of agent organization. Existing studies typically adopt predefined organizations, such as Star, Chain, and Tree, or learn communication graphs to improve performance on specific tasks. However, these methods are largely designed and evaluated within a limited set of application domains, providing little understanding of whether an organization effective in one domain remains suitable for others with different collaboration requirements. We present \textbf{OpenDGO}, a controlled benchmark and analysis framework covering six real-world domains and three collaboration settings: Sequential Dependency, Parallel Decomposition, and Information Integration. Under matched tasks, agents, tools, and resource limits, OpenDGO compares fixed and learned organizations while separately examining communication graphs, role assignments, input assignments, resource budgets, and component failures. Our results show that there is no ``one-organization-for-all'' solution: preferred organizations vary across applications, and even the same communication graph can yield different outcomes under different role and input assignments. These findings highlight the need for application-dependent organization design, aiming to understand \emph{which organization is suitable for a given application, under what conditions, and why}.

Machine Learning (cs.LG)

arXiv:2610.17143 [cs.LG]

(or arXiv:2610.17143v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.17143 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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