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 \textbf{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.

翻译：大语言模型（LLM）在检索增强生成（RAG）中广泛用于在推理阶段引入外部知识。然而，当检索到的上下文存在噪声、不完整或异构时，单一生成过程往往难以有效协调证据。我们提出**MASS-RAG**，一种面向检索增强生成的多智能体合成方法，将证据处理结构化为多个角色特化的智能体。MASS-RAG分别应用证据总结、证据抽取和检索文档推理的智能体，并通过专用的合成阶段将其输出整合以生成最终答案。该设计暴露了多个中间证据视图，使模型能够在答案生成前比较和整合互补信息。在四个基准上的实验表明，MASS-RAG在强RAG基线上持续提升性能，特别是在相关证据分布在检索上下文中的场景下。