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Qiangwei Peng · Zihan Wang · Junda Ying · Yuhao Sun · Qing Nie · Lei Zhang · Tiejun Li · Peijie Zhou
Tianqi Gao · Chengkai Huang · Zihan Wang · Cao Liu · Ke Zeng · Lina Yao
Large language models (LLMs) have recently been adopted for recommendation by framing user preference modeling as a language generation problem. However, existing latent reasoning approaches typically represent user intent with a single latent vector, which struggles to capture the inherently mul...
Xihang Wang · Zihan Wang · Chengkai Huang · Cao Liu · Ke Zeng · Quan Z. Sheng · Lina Yao
Multimodal Retrieval-Augmented Generation (MRAG) is widely adopted for Multimodal Large Language Models (MLLMs) with external evidence to reduce hallucinations. Despite its success, most existing MRAG frameworks treat retrieved evidence as indivisible documents, implicitly assuming that all conte...
Xihang Wang · Zihan Wang · Chengkai Huang · Quan Z. Sheng · Lina Yao
Multimodal Retrieval-Augmented Generation (MRAG) addresses key limitations of Multimodal Large Language Models (MLLMs), such as hallucination and outdated knowledge. However, current MRAG systems struggle to distinguish whether retrieved multimodal data truly supports the semantic core of an answ...