Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement |

Chenxu Wu, Qingpeng Kong, Zihang Jiang & S. Kevin Zhou

• We present Di-Fusion, an self-supervised framework that utilizes diffusion models for denoising diffusion MRI.

Equivariant Sampling for Improving Diffusion Model-based Image Restoration |

Chenxu Wu, Qingpeng Kong, Peiang Zhao, Wendi Yang, Wenxin Ma, Fenghe Tang ,Zihang Jiang & S.Kevin Zhou

• We introduce EquS, a diffusion model-based image restoration method that imposes equivariant information through dual sampling trajectories.

AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP |

Wenxin Ma, Xu Zhang, Qingsong Yao, Fenghe Tang, Chenxu Wu, Yingtai Li, Rui Yan, Zihang Jiang & S.Kevin Zhou

• AA-CLIP enhances CLIP’s anomaly discrimination ability in both text and visual spaces while preserving its generalization capability.

Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation |

Fenghe Tang, Qingsong Yao, Wenxin Ma, Chenxu Wu, Zihang Jiang & S. Kevin Zhou

• We pre-train Hi-End-MAE on a large-scale dataset of 10K CT scans and evaluated its performance across seven public medical image segmentation benchmarks.

U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking |

Fenghe Tang, Chengqi Dong, Wenxin Ma, Zikang Xu, Heqin Zhu, Zihang Jiang, Rongsheng Wang, Yuhao Wang, Chenxu Wu, S. Kevin Zhou

• We propose U-Bench, the first large-scale, statistically rigorous benchmark evaluating 100 U-Net variants across 28 datasets and 10 imaging modalities, with a novel U-Score metric capturing the performance-efficiency trade-off.

A General Knowledge Injection Framework for ICD Coding |

Xu Zhang, Kun Zhang, Wenxin Ma, Rongsheng Wang, Chenxu Wu, Yingtai Li, S. Kevin Zhou

• We propose a novel, general knowledge injection framework that integrates three key types of knowledge, namely ICD Description, ICD Synonym, and ICD Hierarchy, without specialized design of additional modules.

On-Device Large Language Models: A Survey of Model Compression and System Optimization |

Wanyi Chen, Junhao Wang, Yiwei Zhang, Yufan Shi, Tianyi Jiang, Shengxian Zhou, Chenxu Wu, Andi Zhang, Chenyue Zhou, Minxuan Wang, Xinyu Liu, Xiaoshuai Hao, Yinan Wu, Yichen Li, Yuwei Hu, Zhao Cao, Yang Lu, Mengke Li, Yanbiao Ma, Zhiwu Lu, Jungong Han, Yike Guo

• This survey systematically surveys the on-device technology stack from algorithm to system, covering quantization, pruning, knowledge distillation, low-rank adaptation, and hybrid pipelines for deploying LLMs on device and edge.

From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models

Chenyue Zhou, Mingxuan Wang, Yanbiao Ma, Chenxu Wu, Wanyi Chen, Zhe Qian, …

• This survey aims to provide a clear, structured perspective for understanding the intrinsic limitations of current MLLMs and to illuminate the path toward building next-generation models capable of deep reasoning and a genuine understanding of the world.