5 citations · 5 across the 6 of their papers we have counts for
6 papers
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning
Jiachen Zhu, Congmin Zheng, Jianghao Lin +5
While large language models (LLMs) have significantly advanced mathematical reasoning, Process Reward Models (PRMs) have been developed to evaluate the logical validity of reasonin…
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…
RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human Demonstrations
Jingxiao Chen, Xinyao Li, Jiahang Cao +7
Humanoid robots have shown success in locomotion and manipulation. Despite these basic abilities, humanoids are still required to quickly understand human instructions and react ba…
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation
Kounianhua Du, Hanjing Wang, Jianxing Liu +7
Large language models (LLMs) have demonstrated remarkable capabilities in various domains, particularly in system 1 tasks, yet the intricacies of their problem-solving mechanisms i…
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
Rong Shan, Jiachen Zhu, Jianghao Lin +5
In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful informa…
A Survey on Diffusion Models for Recommender Systems
Jianghao Lin, Jiaqi Liu, Jiachen Zhu +5
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inad…