Yang Liu

Advanced autonomous collision avoidance for maritime navigation: A reinforcement learning approach with ship dynamics and environmental awareness

Lichao Yang, Jingxian Liu, Qin Zhou, Zhao Liu, Yukuan Wang, Yang Liu, et al.

Machine learning quantifies dew formation mechanisms across China’s climate gradients

Zhe Li, Lin Zhao, Hanbo Ren, Ranmao Yang, Xinxin Li, Zhijiang Zhang, et al.

Phosphorylation of tumour suppressor Amotl2 by IKBKE kinase promotes YAP1 signalling, enhancing glioblastoma growth

Gaochao Guo, Yan Sun, Rujun Hong, Yalin Lu, Xingjie Chen, Liming Zhao, et al.

Inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKBKE), a member of the serine/threonine kinase family, is an important oncogene in glioblastoma. IKBKE is involved in the progression of multiple tumours in glioblastoma (GBM), including tumour invasion, migration, and…Read more

Spectral optimisation and anomaly detection-based ageing identification of polyethylene using terahertz time-domain spectroscopy

Xinna Jiang, Hongquan Jiang, Maojie Zhang, Yang Liu, Xiaolu Feng, Zhan Yq

This study proposes a THz-TDS-based framework for spectral optimisation and polyethylene (PE) ageing identification. First, a filtering-pooling and peak attention network (FPAN) is developed to mitigate water vapour interference and system noise under conventional conditions. By…Read more

HCPG-Flow:Hierarchical Contact-Progress Guidance for Flow-Policy Robot Manipulation

Guanghu Xie, Mingxu Li, Shuo Zhang, Yonglong Zhang, Yifan Yang, Yang Liu, et al.

Flow policies can represent multimodal action distributions for robot manipulation, yet a robot must execute one action at each control step. When several proposals are sampled, critic-based ranking makes data collection depend on value estimates over candidate actions that may b…Read more

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, et al.

Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems. Existing VLM-based anomaly reasoning methods are capable of detecting open-ended anomalies in g…Read more

Transmit Beamforming Design for Integrated Sensing and Communication Using Transmissive RIS Transceiver

Yuan Guo, Wen Chen, Yang Liu, Qiong Wu, Weiren Zhu

Integrated sensing and communication (ISAC) is a key technology for future wireless networks, calling for hardware-efficient architectures to jointly support communication and sensing. In this paper, a transmissive reconfigurable intelligent surface (TRIS) transceiver is leverage…Read more

Transmissive RIS Transceiver-Empowered ISAC Systems: Energy Efficiency Optimization for Perfect and Imperfect CSI

Yuan Guo, Wen Chen, Yang Liu, Kunlun Wang, Zhendong Li, Qiong Wu

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver is employed to enable an integrated sensing and communication (ISAC) system supporting both communication and sensing. Under both perfect and imperfect channel state information (CSI), we st…Read more

Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction

Shiyuan Piao, Fan Zehui, Yang Liu, Hong Cheng, Juepeng Zheng, Jie Zhou, et al.

Accurate short-term wind power forecasting is essential for grid stability and operational planning, yet remains challenging due to the complex interactions between atmospheric conditions and turbine dynamics. However, existing methods fail to effectively incorporate weather fore…Read more

PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution

Yang Liu, Weixing Chen, Xinshuai Song, Tao Pu, Siwen Mo, Yongjie Bai, et al.

Vision-language-action models, world models, and agentic planners each advance physical intelligence, yet their composition lacks a common execution abstraction, shared state, semantic verification, and persistent experience across heterogeneous embodiments. We present PhyAgentOS…Read more

Music-to-Dance Generation via Atomic Movements

Xinhao Cai, Yixuan Sun, Minghang Zheng, Qingchao Chen, Xin Jin, Song-chun Zhu, et al.

Music-driven dance generation aims to produce human motion that is both rhythmically synchronized and semantically consistent with music. While recent neural approaches have achieved impressive visual realism, they typically model motion as a continuous signal and neglect its com…Read more

Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild

Ting Lei, Jialin Liu, Zhu Xu, Yuxin Peng, Yang Liu

Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories. While such paradigms achieve strong performance on closed-set benchmarks, they fundamentally entangle interaction understanding wi…Read more

A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learning

Yang Liu, Yuhao Liu, Yunran Wei

We propose a noise-robust elicit-to-optimize framework that integrates inverse reinforcement learning (IRL) and reinforcement learning (RL) for eliciting agents' risk preferences and optimizing policies under a broad class of risk objectives characterized by distortion riskmetric…Read more

ABot-3DWorld 0: A Universal World Model to Explore Any 3D Space

Mingchao Sun, Luyang Tang, Yu Liu, Xu Yan, Zhan Li, Yunwei Zhang, et al.

We present ABot-3DWorld 0, a universal multimodal 3D world model that turns text, image, and video inputs into high-fidelity, explorable 3D worlds. At the heart of our framework is a unified Spatial Generative Primitive (SGP), a compact tuple of a high-quality panorama and a spat…Read more

MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

Yuliang Liu, Zhang Li, Ziyang Zhang, Shuo Zhang, Qiang Liu, Jiajun Song, et al.

Mainstream visual encoders are pretrained on natural images and cannot be effectively applied to document images without document-oriented adaptation, as dense text and fine-grained character strokes demand character-level visual perception. We present MonkeyOCRv2, a visual-text…Read more

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

Jing Liu, Kun Yang, Yan Wang, Dingkang Yang, Xiaoshuai Hao, Wei Zhang, et al.

Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network environments, Multimodal Large Language Models (MLLMs) serve as…Read more

PhysMRV: Physical Memory Retrieval and Verification for Physics Plausibility Reasoning

Wenyuan Wang, Lianyu Hu, Hao Wang, Yang Liu

Video-language models (VLMs) have achieved remarkable performance on video understanding and visual question answering, yet they remain unreliable in reasoning about physical plausibility, where understanding object interactions, causal dynamics, and fundamental physical principl…Read more

Beyond Time Shifts: Adapting Omni-LLM as a Reference-Free Evaluator for Generative Audio-Visual Models

Yijie Qian, Juncheng Wang, Chao Xu, Huihan Wang, Yuxiang Feng, Yang Liu, et al.

As audio-visual generative models evolve into world simulators, cross-modal synchronization stands as a critical proxy for assessing the consistency of world dynamics and causality in generated content. However, existing evaluation metrics presume structural correctness, reducing…Read more

Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies

Yuxiang Feng, Juncheng Wang, Chao Xu, Wenlong Hou, Huihan Wang, Yijie Qian, et al.

Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial design, but it remains challenging because true progression signals are subtle in longitudinal MRI. In this low-signal regime, tr…Read more

Understanding and Mitigating the Video-Action Generalization Gap via Temporal Ratio

Utkarsh A. Mishra, Yongxin Chen, Danfei Xu, Yang Liu, Xi Chen, Jiayuan Mao

Generative video foundation models exhibit strong compositional priors, yet world-action models (WAMs) and video-action models (VAMs) often lose these priors after finetuning on robotic action data. We refer to this discrepancy as the video-action generalization gap. In this pape…Read more

Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation

Shun Liu, Nan Xi, Yang Liu, Tianyu Luan, Xuan Gong, David Doermann

Referring Image Segmentation (RIS) aims to segment image regions specified by natural language, enabling fine-grained and controllable visual understanding. Extending RIS to endoscopic imagery, however, presents unique challenges, including scarce high-quality annotations and com…Read more

Akashic: A Low-Overhead LLM Inference Service with MemAttention

Yang Liu, Zhaokai Luo, Huayi Jin, Ruozhou He, Chenchen Hong, Zhiyong Wang, et al.

Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows. Replaying the full history for every request quickly becomes impractical: long contexts increase prefill cost, may exceed context limits,…Read more

Controlling Tool Use with Heading-Specific Activation Steering

Yuqi Chen, Vincent Siu, Yang Liu, Dawn Song, Chenguang Wang

Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily. We investigate whether tool-use decisions have any stable internal representation that can be extracted and manipulated, a que…Read more

GlaKG: A Biomarker-Centric Fundus Knowledge Graph for Explainable Glaucoma Diagnosis and Risk Assessment

Cheng Huang, Jia Zhang, Yi Jiang, Yang Liu, Karanjit Kooner, Yadi Liu, et al.

Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability. We present GlaKG, a biomarker-centric fundus knowledge graph that integrates structural biomarker…Read more

ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection

Joongwon Chae, Lihui Luo, Yang Liu, Dongmei Yu, Peiwu Qin, Runming Wang, et al.

Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies. However, most memory methods still use the memory bank as a nearest-neighbor lookup table: a test patch is treated as normal i…Read more

Neuroprotective potential of the natural polyphenol Procyanidin B2 in spinal cord injury: a comprehensive study utilizing machine learning, network pharmacology, and in vivo validation

Chunyu Xiang, Yang Liu, Rui Gu, Wanguo Liu, Jingwei Shi

Background The secondary injury cascade following spinal cord injury (SCI) drives severe inflammation and tissue destruction. Although the natural polyphenol Procyanidin B2 (PCB2) has well-documented neuroprotective properties, its specific therapeutic efficacy in SCI, as well as…Read more

FRFDet: Efficient UAV Small Object Detection with Symmetric Sampling and Scalable Fusion

Yunzhong Si, Huiying Xu, Xinzhong Zhu, Yang Liu, Yao Dong, Wenhao Zhang, et al.

Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe background clutter, fine-grained detail degradation, and suboptima…Read more

Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models

Jiaqi Tang, Shaoyang Zhang, Fandong Zhang, Shu Zhang, Yang Liu, Qingchao Chen

The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, which is computationally expensive. Most of the existing Transferability Estimation (TE) metrics are pri…Read more

Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking

Ante Wang, Jiaqi Fu, Xuanyi Chen, Ruotian Ma, Zhaopeng Tu, Weizhi Ma, et al.

Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upon receiving a user response, inevitably introduces latency that compromises conversational fluidity.…Read more

Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning

Chengzhen Yu, Canran Xiao, Siyuan Ma, Yang Liu

Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making similarity brittle and overly confident under paraphrase and omitted details. We aim to learn representations whose matching is stabl…Read more

MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support

Lihui Luo, Joongwon Chae, Ziyan Chen, Yang Liu, Siyi Cheng, Weihan Gao, et al.

Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility. The application of multimodal artificial intelligence to TCM clinical tasks, such as syndrome differentiation and prescription ge…Read more

GAP-GDRNet: Geometry-aware monocular 6D pose estimation for spacecraft using synthetic geometric supervision

Zongwu Xie, Yonglong Zhang, Yifan Yang, Yang Liu, Guanghu Xie

Monocular spacecraft 6D pose estimation remains difficult under weak texture, thin structures, illumination variation, and occlusion. This article presents GAP-GDRNet, a geometry-aware RGB framework built on GDR-Net for a single-target synthetic spacecraft benchmark. The method s…Read more

Bridge-WA: Predicting Where and How the World Changes for Robotic Action

Yongjie Bai, Hanting Wang, Mingtong Dai, Qijun Zhong, Yang Liu, Liang Lin

General-purpose vision-language-action models benefit from large vision-language priors, but effective manipulation also requires anticipating action-relevant scene changes. Existing world-action models often rely on large generative world models or dense future rollouts, which a…Read more

SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests

Yaoqi Guo, Yang Liu, Jie M. Zhang, Yun Ma, Yiling Lou, Zhenpeng Chen

Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. Bug reproduction tests (BRTs) are an important building block for such agents and have been shown use…Read more

Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization

Ruichen Ma, Xiaoyang Zhang, Jian Bai, Guanchao Qiao, Liwei Meng, Ning Ning, et al.

The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce runtime multiplications, which weaken the hardware-efficiency motivation of SNNs. To address this t…Read more

Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation

Ying Chen, Jinyue Li, Kun Wang, Qiankun Li, Yang Liu

The Segment Anything Model with Concepts (SAM3) heralds a new paradigm for open-vocabulary segmentation through natural language interaction, offering significant potential for medical image analysis. However, effectively adapting such a powerful vision-language model to the dive…Read more

Rethinking Foundation Model Collaboration: Enhancing Specialized Models through Proxy Task Reasoning

Hongyi Lin, Yang Liu, Jinhua Zhao, Xiaobo Qu

Foundation models are increasingly integrated into embodied intelligence systems, but directly assigning them structured prediction tasks requires precise geometric and numerical estimation, where specialized models often remain stronger. This capability mismatch raises a key que…Read more

CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts

Lianyu Hu, Shengqian Qin, Zeqin Liao, Qing Guo, Liang Wan, Wei Feng, et al.

Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle complex visual reasoning tasks by generating explicit intermediate reasoning steps in natural language. However, this text-based reasoning paradigm is inherently slow at inference tim…Read more

Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation

Shengqi Xu, Guojin Zhong, Yang Liu, Fanjie Wang, Hu Luo, Hanyu Zhou, et al.

Visuo-Tactile policies leveraging optical tactile sensors have shown great promise in contact-rich manipulation. These sensors achieve high spatial resolution and multi-dimensional force sensing by utilizing an internal camera to monitor the deformation of their elastic gel surfa…Read more

Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Fields in Passive Object-State World Models

Yang Liu, Yuming Chen

World models can predict future physical states, but prediction accuracy alone does not explain how physical information is organized and used inside their latent dynamics. We introduce a controlled diagnostic protocol for studying event-conditioned latent physical structure in p…Read more

Drag, Infer, Reproject: Grounding LLMs through Spatial Interaction for Image Clustering

Yang Liu, Xuxin Tang, Jiahao Xu, Chris North

Dimension reduction and semantic interaction support image clustering by making similarity structure visible and manipulable. Existing semantic interaction methods encode users' clustering criterion (a user-interpretable semantic dimension, e.g., action, location, or mood) from d…Read more

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

Xiaomeng Fu, Junfan Lin, Yang Liu, Yaowei Wang, Guanbin Li, Liang Lin, et al.

Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and physical realism. Large language model (LLM)-based approaches can interpret diverse open-vocabulary…Read more

Data-Driven Urban Color Governance for Digital City Planning: A Machine Learning-Assisted Framework Using Street View Images in Jiading District, Shanghai

Jie Xu, Zhongnan Ye, Di Wang, Shasha Huang, Yang Liu, Yu Xiang

Urban color plays a fundamental role in shaping the visual character and cultural identity of cities. Yet in many contexts, current practices remain fragmented, with color analysis often disconnected from planning implementation and governance. To address this issue, this study p…Read more

Computational Insights into the Molecular Mechanisms of Coptis chinensis Franch. in Treating Chronic Atrophic Gastritis: An Integrated Network Pharmacology, Machine Learning, and Molecular Dynamics Study

Chengxiang Hu, Yang Liu, Yiyao Ding, Yue Jin, Weiwei Han

Chronic atrophic gastritis (CAG) is a precancerous gastric condition with limited therapeutic interventions, and the mechanisms underlying the benefits of Coptis chinensis Franch. (CCF) remain insufficiently defined. This study employed an integrated computational strategy to cla…Read more

Computer Vision-Based Deep Learning Modeling for Salmon Part Segmentation and Defect Identification

Chunxu Zhang, Yuanshan Zhao, Wude Yang, Liuqian Gao, Wenyu Zhang, Yang Liu, et al.

Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A…Read more

Deep Learning-Based Digital Surface Model Reconstruction of ZY-3 Satellite Imagery

Yanbin Zhao, Yang Liu, Shuang Gao, Guohua Liu, Zhiqiang Wan, Denghui Hu

This study introduces a novel satellite image digital surface model (DSM) reconstruction framework grounded in deep learning methodology. The proposed framework effectively utilizes a rational polynomial camera (RPC) model to establish the mapping relationship between image coord…Read more

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Song Zhang, Xinting Yang, Yizhong Wang, Zhenxi Zhao, Jintao Liu, Yang Liu, et al.

In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish…Read more