4 citations · 4 across the 11 of their papers we have counts for
6 papers · 1 filter
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.…