16 citations · 30 across the 5 of their papers we have counts for
6 papers
MemCut: Every Secret Leaves a Trail in the Long-Term Memory of LLM Agents
Cheng Zhang, Xuanning Cao, Zhicheng Zheng +1
Long-term memory has become a crucial component of large language model (LLM) agents, with most research focusing on retrieving relevant information accurately and efficiently. How…
LLM-ALSO: LLM-Driven Adaptive Learning-Signal Optimization for Multi-Agent Reinforcement Learning
Xiaoguang Wu, Zhi Zheng, Hui Xiong
Effective training-time guidance is central to multi-agent reinforcement learning (MARL), yet remains difficult in sparse-reward settings where weak supervision limits coordination…
Unleashing the Power of Large Language Model for Denoising Recommendation
Shuyao Wang, Zhi Zheng, Yongduo Sui +1
Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve…
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Yuyang Ye, Zhi Zheng, Yishan Shen +6
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converti…
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu +2
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textua…
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
Shuyao Wang, Yongduo Sui, Jiancan Wu +2
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to p…