2 citations · 2 across the 8 of their papers we have counts for
9 papers
Adaptive Power Sampling for LLM Reasoning
Bingnan Xiao, Chenhao Yang, Bingcong Li +2
Sequence-level power sampling has recently emerged as a training-free approach to reasoning by sampling from a sharpened output distribution of a base large language model (LLM). N…
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Xinyi Xu, Bingnan Xiao, Shuang Qin +2
Low-rank adaptation (LoRA) represents large language model (LLM) updates with two compact matrix factors, i.e., and , providing an efficient way to fine-tune large models in…
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
Bingnan Xiao, Chenhao Yang, Wei Ni +2
Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective.…
Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
Bingnan Xiao, Shuyan Hu, Xiaojing Chen +5
Next-generation wireless systems are expected to be ``AI-native," with neural networks (NNs) embedded throughout the physical (PHY) layer protocol stack to improve spectral efficie…
FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
Bingnan Xiao, Yuan Gao, Bingcong Li +3
Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward flat minima. Under data hete…
Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
Zhiyuan Zhai, Bingcong Li, Bingnan Xiao +2
Test-time compute scaling, the practice of spending extra computation during inference via repeated sampling, search, or extended reasoning, has become a powerful lever for improvi…
Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning
Bingnan Xiao, Feng Zhu, Jingjing Zhang +2
Inherent client drifts caused by data heterogeneity, as well as vulnerability to Byzantine attacks within the system, hinder effective model training and convergence in federated l…
FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Bingnan Xiao, Jingjing Zhang, Wei Ni +1
Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper pre…
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
Bingnan Xiao, Xichen Yu, Wei Ni +2
The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future.…