Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems
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-...
| Authors | Sara Vera Marjanović, Jiacheng Xu, Aleksandr Laptev et al. |
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| Primary category | cs.MA |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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