VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents
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 reduc...
| Authors | Peiyuan Gao, Gaoyuan Zhang, Haojie Qin et al. |
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| Primary category | cs.IR |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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