Generative recommendation retrieves items by autoregressively generating semantic identifiers, but beam search may discard a target before its complete identifier is generated. Our preliminary analysis across three benchmarks shows that most missed targets are pruned within the first two decoding steps, highlighting th...
Hong-Liang Sun, Lian-Jie Li, Bolin Zhang et al.· 0 citations
Large language models (LLMs) offer new opportunities for recommendation by interpreting item descriptions, user instructions, and external knowledge through natural-language prompts. However, existing graph-augmented LLM recommenders often use knowledge graphs mainly as prompt-level evidence, leaving ranking decisions...
Yong Wang, Hongliang Sun, Jin-Lan Liu et al.· 0 citations
This work proposes Defense-as-Skill, a defense paradigm that implements the runtime guard itself as an installable, inspectable, and editable skill, and demonstrates transfer across victim models, held-out risk families, and external benchmarks, as well as retained protection against adaptive attackers.
Xiao-Fan Yang, Ziqi Miao, Dianbo Sui et al.· 0 citations
Experiments show that DuPLeR achieves robust performance in data-scarce KGC scenarios, and a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation.
