7 citations · 21 across the 18 of their papers we have counts for
4 papers · 1 filter
When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems
Ganlin Xu, Linghao Zhang, Zhitao Yin +7
Retrieval-Augmented Generation (RAG) effectively grounds large language models (LLMs) in external knowledge but struggles with \textbf{exploratory reasoning problems (ERPs)} that a…
Deep Research as Rubric for Reinforcement Learning
Wangyi Mei, Zhouhong Gu, Zhenhan Bai +9
Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a promising alternative, but ex…
ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation
Hongru Hou, Tiehua Mei, Denghui Geng +5
Proactive Recommender Systems (PRSs) aim to guide user preference shift toward target items by generating paths of intermediate recommendations. Reinforcement learning (RL) provide…
What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty
Bowei Zhang, Jin Xiao, Guanglei Yue +4
Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignor…