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Impact of incremental graph updates on long-term answer drift in graph-based RAG

Ascertain the impact of incremental update algorithms on answer drift over months in knowledge-graph-based retrieval-augmented generation pipelines by quantifying how graph maintenance and node/link updates affect the stability and accuracy of generated answers over time.

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Background

In structure-aware RAG, documents and entities are represented as nodes and edges, enabling retrieval and reasoning over explicit relations. While incremental graph update algorithms have been proposed to maintain evolving knowledge graphs, the review highlights that their long-term effects on generated answers are not established.

Specifically, the authors point to potential issues from stale or mislinked nodes and emphasize that, although update mechanisms exist, the degree to which they mitigate or exacerbate answer drift across months remains to be evaluated.

References

Incremental update algorithms exist , but their impact on answer drift over months is unknown.

— A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges (2508.06401 - Brown et al., 8 Aug 2025) in Section “What are the innovative methods and approaches compared to the standard retrieval augmented generation?”, Subsubsection “Structure-aware and Graph-based RAG”