@inproceedings{yuan-etal-2026-coselector,
title = "{C}o{S}elector: Multi-Agent Collaboration for Multilingual Retrieval-Augmented Generation",
author = "Yuan, Long and
Qi, Rui and
Zhang, Xue and
Xu, Jinan and
Huang, Kaiyu",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.84/",
pages = "1838--1856",
ISBN = "979-8-89176-439-2",
abstract = "General Retrieval-Augmented Generation (RAG) systems would suffer from performance degradation in multilingual scenarios due to the complexity of integrating different retrieval results from multilingual collections. In this paper, we propose CoSelector, a multi-agent collaborative framework for multilingual retrieval selection, which is optimized by the centralized training with decentralized execution. Under this paradigm, agents share the same goal but execute decisions using only local observations. CoSelector consists of a Selector agent and a Generator agent. The Selector dynamically filters and composes the most pertinent multilingual documents into a coherent context, while the Generator processes this context to understand and integrate information for answer generation. Across the 15 languages evaluated on MKQA and XOR-TyDi QA, CoSelector achieves competitive improvement in response language consistency, demonstrating its effectiveness in both linguistic alignment and the selection of retrieved results."
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<abstract>General Retrieval-Augmented Generation (RAG) systems would suffer from performance degradation in multilingual scenarios due to the complexity of integrating different retrieval results from multilingual collections. In this paper, we propose CoSelector, a multi-agent collaborative framework for multilingual retrieval selection, which is optimized by the centralized training with decentralized execution. Under this paradigm, agents share the same goal but execute decisions using only local observations. CoSelector consists of a Selector agent and a Generator agent. The Selector dynamically filters and composes the most pertinent multilingual documents into a coherent context, while the Generator processes this context to understand and integrate information for answer generation. Across the 15 languages evaluated on MKQA and XOR-TyDi QA, CoSelector achieves competitive improvement in response language consistency, demonstrating its effectiveness in both linguistic alignment and the selection of retrieved results.</abstract>
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%0 Conference Proceedings
%T CoSelector: Multi-Agent Collaboration for Multilingual Retrieval-Augmented Generation
%A Yuan, Long
%A Qi, Rui
%A Zhang, Xue
%A Xu, Jinan
%A Huang, Kaiyu
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F yuan-etal-2026-coselector
%X General Retrieval-Augmented Generation (RAG) systems would suffer from performance degradation in multilingual scenarios due to the complexity of integrating different retrieval results from multilingual collections. In this paper, we propose CoSelector, a multi-agent collaborative framework for multilingual retrieval selection, which is optimized by the centralized training with decentralized execution. Under this paradigm, agents share the same goal but execute decisions using only local observations. CoSelector consists of a Selector agent and a Generator agent. The Selector dynamically filters and composes the most pertinent multilingual documents into a coherent context, while the Generator processes this context to understand and integrate information for answer generation. Across the 15 languages evaluated on MKQA and XOR-TyDi QA, CoSelector achieves competitive improvement in response language consistency, demonstrating its effectiveness in both linguistic alignment and the selection of retrieved results.
%U https://aclanthology.org/2026.aacl-long.84/
%P 1838-1856
Markdown (Informal)
[CoSelector: Multi-Agent Collaboration for Multilingual Retrieval-Augmented Generation](https://aclanthology.org/2026.aacl-long.84/) (Yuan et al., AACL-IJCNLP 2026)
ACL
- Long Yuan, Rui Qi, Xue Zhang, Jinan Xu, and Kaiyu Huang. 2026. CoSelector: Multi-Agent Collaboration for Multilingual Retrieval-Augmented Generation. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1838–1856, Hengqin, China. Association for Computational Linguistics.