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        <namePart type="given">Xingchen</namePart>
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    <abstract>Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when retrieved contexts are noisy, incomplete, or heterogeneous, a single generation process often struggles to reconcile evidence effectively. We propose MASS-RAG, a multi-agent synthesis approach to retrieval-augmented generation that structures evidence processing into multiple role-specialized agents. MASS-RAG applies distinct agents for evidence summarization, evidence extraction, and reasoning over retrieved documents, and combines their outputs through a dedicated synthesis stage to produce the final answer. This design exposes multiple intermediate evidence views, allowing the model to compare and integrate complementary information before answer generation. Experiments on four benchmarks show that MASS-RAG consistently improves performance over strong RAG baselines, particularly in settings where relevant evidence is distributed across retrieved contexts.</abstract>
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%0 Conference Proceedings
%T MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
%A Xiao, Xingchen
%A Huang, Heyan
%A Liu, Runheng
%A Xie, Jincheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xiao-etal-2026-mass
%X Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when retrieved contexts are noisy, incomplete, or heterogeneous, a single generation process often struggles to reconcile evidence effectively. We propose MASS-RAG, a multi-agent synthesis approach to retrieval-augmented generation that structures evidence processing into multiple role-specialized agents. MASS-RAG applies distinct agents for evidence summarization, evidence extraction, and reasoning over retrieved documents, and combines their outputs through a dedicated synthesis stage to produce the final answer. This design exposes multiple intermediate evidence views, allowing the model to compare and integrate complementary information before answer generation. Experiments on four benchmarks show that MASS-RAG consistently improves performance over strong RAG baselines, particularly in settings where relevant evidence is distributed across retrieved contexts.
%U https://aclanthology.org/2026.findings-acl.480/
%P 9865-9883

Markdown (Informal)

[MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation](https://aclanthology.org/2026.findings-acl.480/) (Xiao et al., Findings 2026)

• MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation (Xiao et al., Findings 2026)

ACL

• Xingchen Xiao, Heyan Huang, Runheng Liu, and Jincheng Xie. 2026. MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9865–9883, San Diego, California, United States. Association for Computational Linguistics.