@inproceedings{tang-etal-2026-scalable,
title = "A Scalable Multi-{LLM} Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement",
author = "Tang, Shengji and
Cao, Jianjian and
Lin, Weihao and
Hong, Jiale and
Zhang, Bo and
Hu, Shuyue and
Bai, Lei and
Chen, Tao and
Ouyang, Wanli and
Ye, Peng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.578/",
pages = "12679--12697",
ISBN = "979-8-89176-390-6",
abstract = "Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose SMCS, a Scalable Multi-LLM Collaboration System designed to effectively coordinate multiple open-source LLMs. The system consists of two core components: a Retrieval-based Prior Selection (RPS) module, which dynamically selects the most suitable LLMs for each input, and an Exploration{--}Exploitation-Driven Posterior Enhancement (EPE) module, which fosters response diversity and selects high-quality outputs through a hybrid scoring mechanism. Experiments on eight mainstream benchmarks validate the effectiveness of our system: by integrating fifteen open-source LLMs, SMCS outperforms prevailing closed-source LLMs, e.g., GPT-4.1(**+5.36{\%}**) and GPT-o3-mini(**+5.28{\%}**) across multiple tasks. Remarkably, it even exceeds the average of best results on different datasets with open-source LLMs (**+2.86{\%}**), significantly advancing the empirical performance frontier of open-source collaboration. The code is released at https://github.com/magent4aci/SMCS."
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<abstract>Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose SMCS, a Scalable Multi-LLM Collaboration System designed to effectively coordinate multiple open-source LLMs. The system consists of two core components: a Retrieval-based Prior Selection (RPS) module, which dynamically selects the most suitable LLMs for each input, and an Exploration–Exploitation-Driven Posterior Enhancement (EPE) module, which fosters response diversity and selects high-quality outputs through a hybrid scoring mechanism. Experiments on eight mainstream benchmarks validate the effectiveness of our system: by integrating fifteen open-source LLMs, SMCS outperforms prevailing closed-source LLMs, e.g., GPT-4.1(**+5.36%**) and GPT-o3-mini(**+5.28%**) across multiple tasks. Remarkably, it even exceeds the average of best results on different datasets with open-source LLMs (**+2.86%**), significantly advancing the empirical performance frontier of open-source collaboration. The code is released at https://github.com/magent4aci/SMCS.</abstract>
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%0 Conference Proceedings
%T A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement
%A Tang, Shengji
%A Cao, Jianjian
%A Lin, Weihao
%A Hong, Jiale
%A Zhang, Bo
%A Hu, Shuyue
%A Bai, Lei
%A Chen, Tao
%A Ouyang, Wanli
%A Ye, Peng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F tang-etal-2026-scalable
%X Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose SMCS, a Scalable Multi-LLM Collaboration System designed to effectively coordinate multiple open-source LLMs. The system consists of two core components: a Retrieval-based Prior Selection (RPS) module, which dynamically selects the most suitable LLMs for each input, and an Exploration–Exploitation-Driven Posterior Enhancement (EPE) module, which fosters response diversity and selects high-quality outputs through a hybrid scoring mechanism. Experiments on eight mainstream benchmarks validate the effectiveness of our system: by integrating fifteen open-source LLMs, SMCS outperforms prevailing closed-source LLMs, e.g., GPT-4.1(**+5.36%**) and GPT-o3-mini(**+5.28%**) across multiple tasks. Remarkably, it even exceeds the average of best results on different datasets with open-source LLMs (**+2.86%**), significantly advancing the empirical performance frontier of open-source collaboration. The code is released at https://github.com/magent4aci/SMCS.
%U https://aclanthology.org/2026.acl-long.578/
%P 12679-12697
Markdown (Informal)
[A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement](https://aclanthology.org/2026.acl-long.578/) (Tang et al., ACL 2026)
ACL
- Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, and Peng Ye. 2026. A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12679–12697, San Diego, California, United States. Association for Computational Linguistics.