@inproceedings{marjanovic-etal-2026-mo,
title = "Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems",
author = "Marjanovic, Sara Vera and
Xu, Jiacheng and
Laptev, Aleksandr and
Nalbandyan, Grigor and
Arakelyan, Erik and
Bakhturina, Evelina",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Rozanov, Nikolai and
Hashemzadeh, Maryam and
Arora, Akhil",
booktitle = "Proceedings of the Second Workshop for {RE}search on Agent Language Models ({REALM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.realm-1.33/",
pages = "484--507",
ISBN = "979-8-89176-485-9",
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 may introduce system instability, highlighting model selection as a critical design choice for multi-agent systems."
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%0 Conference Proceedings
%T Mo’ Models, Mo’ Problems: How to best select model pools when designing Multi-Agent Systems
%A Marjanovic, Sara Vera
%A Xu, Jiacheng
%A Laptev, Aleksandr
%A Nalbandyan, Grigor
%A Arakelyan, Erik
%A Bakhturina, Evelina
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F marjanovic-etal-2026-mo
%X 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 may introduce system instability, highlighting model selection as a critical design choice for multi-agent systems.
%U https://aclanthology.org/2026.realm-1.33/
%P 484-507
Markdown (Informal)
[Mo’ Models, Mo’ Problems: How to best select model pools when designing Multi-Agent Systems](https://aclanthology.org/2026.realm-1.33/) (Marjanovic et al., REALM 2026)
ACL