VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents

• URL: http://arxiv.org/abs/2609.11390v1
• Date: Thu, 10 Sep 2026 11:25:59 GMT
• Title: VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents
• Abstract summary: State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs.<n>We present sf VikingRAG, a directory-aware semantic data management system that tightly integrates semantic and structural access.<n>With retrieval-trace reuse and adaptive escalation, token costs drop to 5.1%--32.5% while maintaining competitive accuracy and practical document-storage performance.
• Score: 16.5373268346586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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