Large language models (LLMs) are increasingly used for information seeking, where users find, compare, and evaluate information through dialogue. In this role, the assistant does more than retrieve or generate content: it shapes how users articulate constraints, ask follow-up questions, verify claims, and decide when a...
Abdisalam Abukar, Junchen Fu, Cheng-Li Zhai et al.· 0 citations
Experiments on five real-world datasets show that \textsc{DS-Frame} consistently improves representative sequential recommendation backbones, with larger gains on challenging groups and effective accuracy--efficiency trade-offs, highlighting the potential of adaptive inference for more efficient and robust recommendati...
Zi-Chen Yuan, Xiao-Xuan Dong, Linkun Dai et al.· 0 citations
This survey reviews core tasks, representative methods, and datasets in multimodal facial state analysis, focusing on facial expression recognition, AU detection, and face-based soft biometric estimation, and emphasizing the unique value of language in providing contextual semantics, enhancing reasoning, and generating...
Xuri Ge, Tian-Shuo Zhang, Ruihan Li et al.· 0 citations
Rec is proposed, an RL-free framework that decouples reasoning from prediction, overcoming the fixed d-dimensional state bottleneck of prior methods, and points to a decoupled, multi-vector recipe that unleashes latent reasoning from the single-state bottleneck of prior methods.
Wenhao Deng, Junchen Fu, Han-Wen Du et al.· arXiv.org· 0 citations
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