arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably introduce irrelevant information that may mislead generation for complex reasoning. Inspired by the conditional computation of mixture of experts (MoE), where a router sparsely selects specialized experts alongside shared ones for each input, we propose \textbf{M}ixture \textbf{o}f experts for \textbf{G}raph-based Retrieval-Augmented Generation, i.e., \textbf{MoG}. It organi
The continuous drive to improve the reliability and reasoning capabilities of large language models is leading to more sophisticated methods for integrating external knowledge.
This development addresses a key limitation of current retrieval-augmented generation (RAG) systems by improving the relevance and quality of retrieved information, which is crucial for complex AI applications.
Current monolithic retrieval systems for LLMs may evolve towards more specialized, adaptive, and scalable architectures that can handle diverse and complex information needs more effectively.
- · AI developers
- · Enterprises deploying RAG systems
- · Graph database providers
- · Monolithic RAG systems
- · Companies relying on naive RAG
Improved reliability and accuracy of AI applications using RAG.
Increased adoption of hybrid AI architectures combining LLMs with specialized knowledge retrieval and reasoning components.
Enhanced AI agents capable of more nuanced decision-making and interaction by leveraging specialized, context-aware information.
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Read at arXiv cs.CL