2 citations · 3 across the 12 of their papers we have counts for
13 papers
SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation
Songwei Dong, Bingyan Lu, Makayla Kienlen +2
The exponential growth of wireless devices is driving unprecedented spectrum demand, pushing spectrum management toward more fine-grained decisions across space, time, and device c…
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory
Zihan Chen, Songwei Dong, Chengshuai Shi +4
Sequentially evolving LLM memory enables agents to reuse past experience, but existing systems usually deploy each locally generated memory update without checking whether it impro…
Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
Songwei Dong, Zihan Chen, Chengshuai Shi +3
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluation…
Lean Clients, Full Accuracy: Hybrid Zeroth- and First-Order Split Federated Learning
Zhoubin Kou, Zihan Chen, Jing Yang +1
Split Federated Learning (SFL) enables collaborative training between resource-constrained edge devices and a compute-rich server. Communication overhead is a central issue in SFL…
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Xingbo Fu, Zehong Wang, Zihan Chen +7
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent s…
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
Zihan Chen, Song Wang, Zhen Tan +2
In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle diverse tasks by incorporating multiple input-output examples, known as demonstrations, into the input of…
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Zihan Chen, Xingbo Fu, Yushun Dong +2
Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods us…
A Survey of Scaling in Large Language Model Reasoning
Zihan Chen, Song Wang, Zhen Tan +6
The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration. How…
Channel-Wise Mixed-Precision Quantization for Large Language Models
Zihan Chen, Bike Xie, Jundong Li +1
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substan…
Verification of Machine Unlearning is Fragile
Binchi Zhang, Zihan Chen, Cong Shen +1
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, foll…
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
Zihan Chen, Song Wang, Cong Shen +1
In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts. Since generating the prompts needs to sample…
Safety in Graph Machine Learning: Threats and Safeguards
Song Wang, Yushun Dong, Binchi Zhang +7
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have b…
Personalized Federated Learning with Attention-based Client Selection
Zihan Chen, Jundong Li, Cong Shen
Personalized Federated Learning (PFL) relies on collective data knowledge to build customized models. However, non-IID data between clients poses significant challenges, as collabo…