1 citations · 1 across the 10 of their papers we have counts for
11 papers
Adaptive Robust Estimator for Multi-Agent Reinforcement Learning
Zhongyi Li, Wan Tian, Jingyu Chen +8
Multi-agent collaboration has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models, yet it suffers from interaction-level ambiguity that…
Your Group-Relative Advantage Is Biased
Fengkai Yang, Zherui Chen, Xiaohan Wang +10
Reinforcement Learning from Verifier Rewards (RLVR) has emerged as a widely used approach for post-training large language models on reasoning tasks, with group-based methods such…
LLMBoost: Make Large Language Models Stronger with Boosting
Zehao Chen, Tianxiang Ai, Yifei Li +11
Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or fina…
FLeW: Facet-Level and Adaptive Weighted Representation Learning of Scientific Documents
Zheng Dou, Deqing Wang, Fuzhen Zhuang +2
Scientific document representation learning provides powerful embeddings for various tasks, while current methods face challenges across three approaches. 1) Contrastive training w…
Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan +5
The rise of cloud-device collaborative computing has enabled intelligent services to be delivered across distributed edge devices while leveraging centralized cloud resources. In t…
ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling
Huishi Luo, Fuzhen Zhuang, Yongchun Zhu +6
Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task l…
H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
Wei Guo, Siyuan Lu, Yiqi Tong +5
Different from existing federated fine-tuning (FFT) methods for foundation models, hybrid heterogeneous federated fine-tuning (HHFFT) is an under-explored scenario where clients ex…
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
Wei Guo, Yiyang Duan, Zhaojun Hu +7
In vertical federated learning (VFL), multiple enterprises address aligned sample scarcity by leveraging massive locally unaligned samples to facilitate collaborative learning. How…
CDC: Causal Domain Clustering for Multi-Domain Recommendation
Huishi Luo, Yiqing Wu, Yiwen Chen +2
Multi-domain recommendation leverages domain-general knowledge to improve recommendations across several domains. However, as platforms expand to dozens or hundreds of scenarios, t…
AgriCHN: A Comprehensive Cross-domain Resource for Chinese Agricultural Named Entity Recognition
Lingxiao Zeng, Yiqi Tong, Wei Guo +6
Agricultural named entity recognition is a specialized task focusing on identifying distinct agricultural entities within vast bodies of text, including crops, diseases, pests, and…
TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems
Xiao Zhang, Qi Wang, Mingyi Li +4
Implementing large language models (LLMs)-driven root cause analysis (RCA) in cloud-native systems has become a key topic of modern software operations and maintenance. However, ex…