Author

Zicong Hong

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#federated learning Nov 2026

Toward Fair Federated Edge Learning Through Prototype-Guided Distributed Adversarial Networks

Federated learning (FL) is a privacy-preserving machine learning (ML) paradigm that can learn models from distributed datasets owned by mobile terminals (MTs). However, ML models usually contain bias on some user groups/sensitive attributes (e.g., gender and race), which poses additional challenges on FL in real-world...

Kang Wei, Xin-Nan Yuan, Zi-Cong Hong et al. · IEEE Transactions on Mobile... · 0 citations

Preprint Sep 2026

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, proprioceptive, and action tokens. Quantization reduces inference cost, but comparable numerical errors in different streams can have markedly...

Yun-Han Wang, Hao-Dong Wang, Zhi-Ming Liu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse Imagination

World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and actions. This capability comes at a substantial inference cost, as dense future-frame tokens are repeatedly processed during denoising. Prior methods address this by toke...

Xin-Ling Xie, Hao-Dong Wang, Jia-Zhi Mi et al. · 0 citations

#machine learning Preprint Sep 2026

OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling

Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeling. While recent efforts leverage simulator-generated data to enhance this capability, existing training pipelines face two fundamental limitations. First, static offline...

Yi-Kun Miao, Fang-Qi Zhu, Quan-Xin Shou et al. · 2 citations

K AIROX : Adaptive GPU–CPU Hybrid LLM Inference via Online Neuron Balancing

K AIROX introduces a Live Pipeline designed to prefetch neurons by predicting next-layer activation patterns, a mechanism that dynamically redistributes neurons between the GPU and CPU based on activation patterns, and a Temporal Activation Momentum cache policy to prioritize neurons with sustained utility while minimi...

Yapeng Jiang, Minghao Gan, Zi-Cong Hong et al. · 0 citations

#robotics Preprint Jul 2026

RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy

Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused by distribution shifts during deployment. While offline reinforcement learning (RL) provides a practical way to improve deployed policies using rollout data, existing...

Zhengyang Yan, Junhao Li, Fangqi Zhu et al. · 2 citations