27 citations · 30 across the 18 of their papers we have counts for
19 papers
SPD: Single Pass Decoding for Generative Reranking
Emil Laftchiev, Prachi Agrawal, Moe Kayali +7
Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive decoding spends one sequential fo…
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
ExpWeaver: LLM Agents Learn from Experience via Latent RAG
Tao Feng, Tianyang Luo, Jingjun Xu +5
Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods r…
ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents
Tao Feng, Chongrui Ye, Tianyang Luo +8
Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse succe…
LRanker: LLM Ranker for Massive Candidates
Tao Feng, Zijie Lei, Zhigang Hua +4
Large language models (LLMs) have recently shown strong potential for ranking by capturing semantic relevance and adapting across diverse domains, yet existing methods remain const…