9 papers
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…
AEL: Agent Evolving Learning for Open-Ended Environments
Wujiang Xu, Jiaojiao Han, Minghao Guo +4
LLM agents increasingly operate in open-ended environments spanning hundreds of sequential episodes, yet they remain largely stateless: each task is solved from scratch without con…
Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning
Shanyong Wang, Shuhang Lin, Yining Zhao +2
Recent advances in large language models (LLMs) have significantly improved the alignment of models with general human preferences. However, a major challenge remains in adapting L…
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…
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…
Cache Mechanism for Agent RAG Systems
Shuhang Lin, Zhencan Peng, Lingyao Li +3
Recent advances in Large Language Model (LLM)-based agents have been propelled by Retrieval-Augmented Generation (RAG), which grants the models access to vast external knowledge ba…