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cs.CL2026
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Zheng Yuan, Chuang Zhou, Linhao Luo +4
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably intro…
cs.CL2026
Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
Junru Lu, Jiarui Qin, Lingfeng Qiao +35
We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that r…