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cs.IR2026
Trust or Abstain? A Self-Aware RAG Approach
Xi Zhu, Ziqi Wang, Kai Mei +5
Retrieval-augmented generation (RAG) improves large language models (LLMs) by incorporating external evidence, but it also introduces knowledge conflicts when retrieved contextual…
cs.IR2026
RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing
Ziqi Wang, Xi Zhu, Shuhang Lin +3
Retrieval-augmented generation (RAG) has evolved into a family of paradigms with distinct performance profiles and resource demands, turning paradigm selection into a multi-criteri…
cs.IR2026
RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm
Jiahe Liu, Qinkai Yu, Jingcheng Niu +5
Despite the success of Retrieval-Augmented Generation (RAG) in grounding LLMs with external knowledge, its application over heterogeneous sources (e.g., private databases, global c…