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arXiv · 2609.17306

Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems

Sara Vera Marjanović·Jiacheng Xu·Aleksandr Laptev·Grigor Nalbandyan·Erik Arakelyan·Evelina Bakhaturina
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Abstract

Multi-agent Systems (MAS) combine multiple model outputs to solve complex reasoning tasks. However, despite rapid growth of available open-source models, there is limited research on how to select optimal model candidates out of this massive pool. We systematically evaluate 8 model selection strategies (including model size, accuracy and answer diversity) across before-generation (routing) and after-generation (majority-voting, LLM-as-a-judge) MAS architectures on challenging scientific benchmarks. Our findings show a significant gap between theoretical oracle potential and actual performance: Expanding candidate pool sizes often degrades performance below that of the top performing base-model. We find that candidate selection within a single model family is the strategy that yields the best relative performance over a standalone model. These results demonstrate that adding arbitrary models to a heterogeneous MAS can introduce system instability, highlighting model selection as a critical design choice for multi-agent systems.

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Sara Vera Marjanović, Jiacheng Xu, Aleksandr Laptev, Grigor Nalbandyan, Erik Arakelyan, Evelina Bakhaturina. 2026-09-15. Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems. https://arxiv.org/abs/2609.17306

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