MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
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 ...
| Authors | Xingchen Xiao, Heyan Huang, Runheng Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 April 2026 |
| Last updated at source | 21 April 2026 |
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