EfficientGraph-RAG: Structured Retrieval-State Management for Cross-Task Retrieval-Augmented Generation
arXiv:2605.25379v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become the standard way to ground large language models in external knowledge, but many systems still organize evidence as flat chunks and retrieve it through largely unstructured search. This weak structure becomes a bottleneck for complex retrieval: the system must decide where to search, how to move from coarse topics to entity-relation evidence, which evidence has been verified, and which intermediate artifacts can be reused. We define these intermediate variables as a retrieval state and study RAG as
The proliferation of RAG systems highlights the need for more sophisticated retrieval management to move beyond basic keyword searches and unstructured data handling.
This research addresses a key bottleneck in RAG's ability to handle complex queries, enabling more reliable and efficient integration of external knowledge into large language models.
RAG systems could evolve from simple data retrieval to more intelligent, state-managed processes that understand context and relationships within knowledge bases, improving accuracy and reducing hallucinations.
- · AI developers
- · Enterprises leveraging RAG
- · Knowledge management platforms
- · Legacy RAG systems
- · Basic search algorithms
Improved performance and reliability of retrieval-augmented generation in complex applications.
Accelerated adoption of RAG across more sophisticated enterprise use cases requiring nuanced information retrieval.
Enhanced AI agents capable of more autonomous and accurate decision-making by leveraging highly structured and verified information.
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Read at arXiv cs.CL