activity
20242026
most citedWhen AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

4 citations · 4 across the 1 of their papers we have counts for

collaborators

15 papers

q-fin.TR20264 cited

When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

Chong Zhang, Xinyi Liu, Zhongmou Zhang +10

Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fund…

cs.CV2026

SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosis

Zhixiang Lu, Shijie Xu, Kaicheng Yan +6

The deployment of vision-language models (VLMs) in dermatology is hindered by the trilemma of high computational costs, extreme data scarcity, and the black-box nature of deep lear…

cs.CL2026

SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia

Zhixiang Lu, Chong Zhang, Yulong Li +5

The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language mod…

cs.CV2026

StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors

Qinkai Yu, Chong Zhang, Gaojie Jin +11

Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This challenge is further compounded…

cs.CL2025

Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility

Zhaoqian Xue, Guanhong Liu, Chong Zhang +7

Monitoring health resource disparities during public health crises is critical, yet traditional methods, like surveys, lack the requisite speed and spatial granularity. This study…

cs.CL2025

Node-as-Agent: Graph Agentic Network

Minghao Guo, Xi Zhu, Qingyue Jiao +7

Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However…