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Life sciences · Preprint
arXiv · September 10, 2026
Posted before peer review. The findings may change or fail to hold.
VikingRAG is a proposed system architecture for token-efficient retrieval-augmented generation over structured documents, reported in an unrefereed preprint. The authors describe experiments showing reduced token consumption (5.1%–32.5% of baseline) while maintaining competitive accuracy, but the work has not been peer reviewed and lacks standard methodological transparency.
Preprint. Intervention: VikingRAG system with retrieval-trace reuse and adaptive escalation strategy. Compared with: State-of-the-art retrieval-augmented generation methods.
Base VikingRAG system consumes 11.6%–51.9% of token costs of state-of-the-art methods while matching accuracy With retrieval-trace reuse and adaptive escalation, token costs drop to 5.1%–32.5% of baseline while maintaining competitive accuracy
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This is an unrefereed arXiv preprint describing a computer science system (VikingRAG) for retrieval-augmented generation; it reports experimental results on token efficiency and accuracy but has not undergone peer review.
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State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs. To reduce structural-context tokens without compromising high RAG accuracy, we present {\sf VikingRAG}, a directory-aware semantic data management system that tightly integrates semantic and structural access to support structural-context-efficient, evidence-gap-driven multi-round retrieval. To further reduce token overhead of multi-round interaction, we materialize agentic multi-round retrieval traces as experience edges, and reuse these edges for similar queries, avoiding repeated multi-round exploration. To additionally reduce token costs when agentic multi-round retrieval is unnecessary, we introduce an adaptive escalation strategy that answers from one-round experience-augmented retrieval when the evidence is sufficient, and invokes agentic multi-round retrieval only otherwise. Experiments on real datasets show that the base system {\sf VikingRAG} matches high accuracy of state-of-the-art methods while consuming only 11.6\%--51.9\% of their tokens. With retrieval-trace reuse and adaptive escalation, token costs drop to 5.1\%--32.5\% while maintaining competitive accuracy and practical document-storage performance, showing the utility of this work for emerging AI knowledge bases.
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