🤖 AI Summary
This work addresses the limitations of conventional retrieval-augmented generation (RAG) pipelines when retrieval contexts are noisy, incomplete, or contain evidence scattered across multiple heterogeneous documents. To overcome these challenges, the authors propose a multi-agent collaborative RAG framework in which specialized agents perform distinct roles—evidence summarization, key information extraction, and multi-step reasoning—and subsequently synthesize their perspectives to generate a coherent final answer. Leveraging large language models to orchestrate this collaborative agent system, the approach demonstrates significant performance gains over strong baselines across four benchmark datasets, with particularly pronounced improvements in scenarios where relevant evidence is dispersed. These results substantiate the efficacy of multi-perspective analysis and collaborative synthesis in enhancing RAG robustness and accuracy.
📝 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 \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.