4 citations · 4 across the 11 of their papers we have counts for
12 papers
FraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy
Keyang Chen, Mingxuan Jiang, Zeping Li +10
Financial fraud caused over $15.9 billion in losses in the United States in 2025, according to the Federal Trade Commission~\citep{ftc2026databook}. Understanding such large-scale…
From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning
Yuhang Zhou, Yixin Cao, Guangnan Ye
Reasoning prefixes shape the future trajectory of LLM problem solving, yet existing process reward models usually evaluate them through local step correctness. We argue that correc…
TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering
Keyang Chen, Mingxuan Jiang, Yongsheng Zhao +9
Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled b…
Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation
Zeping Li, Guancheng Wan, Keyang Chen +6
Recent works have increasingly applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phen…
Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents
Zeping Li, Hongru Wang, Yiwen Zhao +7
Tool-using agents based on Large Language Models (LLMs) excel in tasks such as mathematical reasoning and multi-hop question answering. However, in long trajectories, agents often…
GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation
Yifan Wang, Mingxuan Jiang, Zhihao Sun +5
Retrieval-Augmented Generation (RAG) grounds large language models with external evidence, but many implementations rely on pre-built indices that remain static after construction.…
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
Mingxuan Jiang, Keyang Chen, Yongxin Wang +10
Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient. Exis…
Teach2Eval: An Indirect Evaluation Method for LLM by Judging How It Teaches
Yuhang Zhou, Xutian Chen, Yixin Cao +8
Recent progress in large language models (LLMs) has outpaced the development of effective evaluation methods. Traditional benchmarks rely on task-specific metrics and static datase…
SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning
Yuhang Zhou, Zeping Li, Siyu Tian +4
Large language models (LLMs) are increasingly being applied across various specialized fields, leveraging their extensive knowledge to empower a multitude of scenarios within these…
RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection
Haolin Li, Shuyang Jiang, Lifeng Zhang +3
Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspecti…
Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
Yuhang Zhou, Yuchen Ni, Yunhui Gan +7
Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e…
-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
Yuhang Zhou, Yu He, Siyu Tian +8
While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural lang…