Peng Wang

RadSight: Towards Perceptually Reliable Multimodal Radiology Image Understanding

Jianqin Liu, Weiwei Cao, Wanxing Chang, Ruifeng Yuan, Bowen Shi, Zhilin Zheng, et al.

Medical multimodal large language models (MLLMs) are increasingly expected to perform complex image understanding tasks, yet their reliability is often compromised by frequent errors in visual interpretation. To systematically trace these failures, we traverse the hierarchy from…Read more

OffNadirLoc: Benchmark and Framework for Challenging UAV-to-Satellite Geo-Localization under Large Off-Nadir Views

Qian Qiao, Wenye Liu, Ting Liu, Jiuhe Shu, Peng Wang

Cross-view geo-localization between UAV and satellite imagery remains a fundamental yet highly challenging task, especially under large off-nadir views where drastic perspective distortions, occlusions, and appearance gaps occur. Existing benchmarks and methods primarily focus on…Read more

Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving

Yun Li, Jiachen Gong, Simon Thompson, Ehsan Javanmardi, Qunli Zhang, Zifan Zeng, et al.

Large language models bring instruction following and scene reasoning to end-to-end driving, but their inference latency collides with the control rate a vehicle requires. Existing closed-loop agents hide this gap by invoking the model on alternate simulation ticks and replaying…Read more

A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images

Jie Liu, Yue Wang, Danping Zeng, Xiaowen Mao, Yunyun Yu, Huijun Feng, et al.

Subspace Consensus of Matrix-Weighted Networks

Yuhao Chen, Lulu Pan, Xiaohui Gong, Peng Wang, Haibin Shao

This paper investigates the subspace consensus problem of matrix-weighted multi-agent networks, where each agent possesses a vector-valued state in $\mathbb{R}^{d}$ and interactions between neighboring agents are characterized by matrix-valued edge weights. Besides all dimensions…Read more

Shadow Pricing of Static Voltage Stability Services within Unit Commitment for Inverter-Dominated Power Systems

Peng Wang, Luis Badesa

Modern power systems are increasingly dominated by Inverter-Based Resources (IBR), most of which work in Grid-following (GFL) mode. This implies that they do not directly control their terminal voltage, so the static voltage stability at these buses may be compromised, especially…Read more

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

Lang Cao, Renhong Chen, Luyi Li, Peng Wang, Mofan Peng, Yitong Li

Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control. However, most existing policies remain limited by behavior cloning or supervised fine-tuning (SFT) from fixed…Read more

The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory

Zihan Chen, Songwei Dong, Chengshuai Shi, Peng Wang, Song Wang, Cong Shen, et al.

Sequentially evolving LLM memory enables agents to reuse past experience, but existing systems usually deploy each locally generated memory update without checking whether it improves future behavior. As a result, updates that help the current task may overwrite useful knowledge,…Read more

Sequential Planning via Anchored Robotic Keypoints

Bryce Grant, Aryeh Rothenberg, Logan Senning, Zonghe Chua, Zach Patterson, Peng Wang

We present Sequential Planning via Anchored Robotic Keypoints, SPARK, a training-free neurosymbolic manipulation system that reaches 43.7% on six LIBERO-PRO position \& task cells, more than doubling CaP-Agent0 and Vision-Language-Action (VLA) baselines. CaP-Agent0, a multi-turn…Read more

Predictive Framework for Regional Patent Output Using Digital Economic Indicators: A Stacked Machine Learning and Geospatial Ensemble to Address R&D Disparities

Amelia Zhao, Peng Wang

As digital transformation becomes an increasingly central focus of national and regional policy agendas, parallel efforts are intensifying to stimulate innovation as a critical driver of firm competitiveness and high-quality economic growth. However, regional disparities in innov…Read more

A machine learning approach for predicting thermal expansion in perovskite oxide cathodes via tolerance factor τ minimization

Jie Zhao, Changqing Dong, Junjie Xue, Xiaoying Hu, Jun-jiao Zhang, Peng Wang