most citedEngineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future

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

collaborators

7 papers

cs.CY2026

Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future

Qingwen Zeng, Hanlin Xu, Nanjun Xu +5

Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage thei…

cs.CR2026

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech

Qingwen Zeng, Zhenghao Zhao, Yitian Yang +6

Artificial intelligence is now embedded as a primary decision engine in continuously operated financial AI pipelines spanning training and updating, deployment and inference, and o…

cs.AI2026

Latent Action Reparameterization for Efficient Agent Inference

Wenhao Huang, Qingwen Zeng, Qiyue Chen +11

Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inference cost. While prior wor…

cs.SE2026

Human-aligned AI Model Cards with Weighted Hierarchy Architecture

Pengyue Yang, Haolin Jin, Qingwen Zeng +3

The proliferation of Large Language Models (LLMs) has led to a burgeoning ecosystem of specialized, domain-specific models. While this rapid growth accelerates innovation, it has s…

cs.IR2025

M-REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs

Lining Chen, Qingwen Zeng, Huaming Chen

Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods,…

cs.CR2025

Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection

Jan Lum Fok, Qingwen Zeng, Shiping Chen +2

Credit card fraud detection (CCFD) is a critical application of Machine Learning (ML) in the financial sector, where accurately identifying fraudulent transactions is essential for…