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

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

作者：Peiyuan Gao, Gaoyuan Zhang, Haojie Qin, Yahui Sun, Qianyi Zhang, Yunhao Zhang, Zeyu Wang, Wei Lu

来源：arXiv cs.DB（数据库）

正文

Computer Science > Information Retrieval

arXiv:2609.11390v1 (cs)

[Submitted on 10 Sep 2026]

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

Authors:Peiyuan Gao, Gaoyuan Zhang, Haojie Qin, Yahui Sun, Qianyi Zhang, Yunhao Zhang, Zeyu Wang, Wei Lu

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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.

Subjects:

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB); Machine Learning (cs.LG)

Cite as:

arXiv:2609.11390 [cs.IR]

(or

arXiv:2609.11390v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2609.11390

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主题

信息检索 · 人工智能 · 自然语言处理 · 数据库 · 机器学习

由「前沿雷达」于 2026-09-10 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。