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
Temporal Autoregressive Alignment (TAAL) is proposed, which improves NDCG@10 over the standard baseline by 39.5%, and aligns the early-prefix distribution with a forward KL objective during training and during inference, it calibrates candidate scores with pointwise mutual information (PMI) to reduce the influence of g...
Lian-Jie Li, Zhi-Ying Tu, Dianhui Chu 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.
The proposed knowledge-enhanced visual diagnostic system enhances the transparency of traditional Chinese medicine diagnostic reasoning and the interpretability of treatment plans through knowledge graph-driven visualization and multimodal interaction, offering a practical solution for trustworthy artificial intelligen...
Yunhan Wang, Yu-Die Wang, Zhiying Tu et al.· arXiv.org· 0 citations
This work introduces Model Automated Deployment Engine (MADE), a dual-agent coordination system that iteratively constructs and validates the deployment artifacts, updates its deployment belief based on execution feedback, and revisits invalid upstream artifacts until the model is successfully served as a ready-to-call...
Yicheng Liu, Bolin Zhang, Weiran Liu et al.· 0 citations
This work proposes VPO, a negative gradient constraint method for human non-preference samples based on V -usable information, which can alleviate the squeezing effect of DPO, enhance alignment with the generation objective, and maintain the model’s ability to distinguish between preference and non-preference samples.
Zecheng Wang, Chunshan Li, Yupeng Zhang et al.· Neural Information Processin...· 1 citation
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