papers
Publications (6)
cs.IR2026
Prompt Generation Technical Report
Dan Ou, Gui Ling, Hao Wan +25
The paper introduces Prompt Generation (PG), a configuration‑driven framework that separates feature processing from model architecture for generative retrieval systems, enabling f…
#generative retrieval#prompt generation#feature engineering#configuration-driven framework
cs.IR2025
ReaSeq: Unleashing World Knowledge via Reasoning for Sequential Modeling
Jiakai Tang, Chuan Wang, Gaoming Yang +31
cs.OS2026
RTP-LLM: High-Performance Alibaba LLM Inference Engine
Boyu Tan, Jiarui Guo, Zongwei Lv +26
cs.IR2026
RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
Kairui Fu, Changfa Wu, Kun Yuan +8
cs.IR2025
TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Zida Liang, Changfa Wu, Dunxian Huang +9
cs.IR2025
HHFT: Hierarchical Heterogeneous Feature Transformer for Recommendation Systems
Liren Yu, Wenming Zhang, Silu Zhou +3