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This research establishes fundamental information-theoretic limits for multi-agent systems, proving that success probability decays exponentially when task constraints create communication bottlenecks between agents with limited processing capacity. The study defines a "minimum cut cost" metric to quantify these structural limitations and validates the theory using real-world data from SWE-bench submissions. Consequently, practitioners should prioritize restructuring complex tasks over merely scaling agent counts or bandwidth when facing high information bottleneck costs.
How Task Structure Limits Multi-Agent Success: An Information-Theoretic Analysis
arXiv:2606.13733v1 Announce Type: cross Abstract: Multi-agent systems (MAS) were expected to overcome the limitation of single-agent systems (SAS) through collaboration. However, under typicality conditions on the task's constraint graph and bounded inter-agent communication, we prove that the success probability of a MAS is closely tied to the connectivity of task constraints, where each agent has limited information-processing capacity. Specifically, the success probability decays exponentially with an information bottleneck that emerges from partitioning the task's constraint graph among agents. We define this quantity as the \emph{minimum cut cost} $C_{\min}$ of the potential constraint graph of each task. This information-theoretic bound applies to both open systems with external feedback and closed systems without. We validate our theory on both synthetic experiments and real-world empirical data from SWE-bench submissions. From our framework, effective MAS design should incorporate task-inherent constraints alongside engineering optimization, and when $\Cmin$ is high, practitioners should restructure tasks rather than simply scaling agents or communication.
Abstract:Multi-agent systems (MAS) were expected to overcome the limitation of single-agent systems (SAS) through collaboration. However, under typicality conditions on the task's constraint graph and bounded inter-agent communication, we prove that the success probability of a MAS is closely tied to the connectivity of task constraints, where each agent has limited information-processing capacity. Specifically, the success probability decays exponentially with an information bottleneck that emerges from partitioning the task's constraint graph among agents. We define this quantity as the \emph{minimum cut cost} $C_{\min}$ of the potential constraint graph of each task. This information-theoretic bound applies to both open systems with external feedback and closed systems without. We validate our theory on both synthetic experiments and real-world empirical data from SWE-bench submissions. From our framework, effective MAS design should incorporate task-inherent constraints alongside engineering optimization, and when $\Cmin$ is high, practitioners should restructure tasks rather than simply scaling agents or communication.
Information Theory (cs.IT); Machine Learning (cs.LG)
arXiv:2606.13733 [cs.IT]
(or arXiv:2606.13733v1 [cs.IT] for this version)
https://doi.org/10.48550/arXiv.2606.13733 Focus to learn more arXiv-issued DOI via DataCite
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