6 citations · 7 across the 7 of their papers we have counts for
8 papers
Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors
Bo Qu, Mingguang Chen, Licheng Wang
Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them. We ask which of those…
The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agents
Mingguang Chen, Licheng Wang, Bo Qu
Frontier language models solve reasoning problems in a single forward pass that would have been research contributions years ago, yet fail at multi-hour tasks: losing track of earl…
The Calibration Floor: Format Repair Can Masquerade as Self-Correction at Small-to-Mid Scale
Mingguang Chen, Bo Qu, Licheng Wang
Accuracy changes after language-model self-revision are usually interpreted as changes in reasoning. We show this can fail at the answer-extraction boundary, and test the failure c…
CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
Bo Qu, Mingguang Chen
LLM agents are increasingly cast as autonomous portfolio managers, yet the dominant evaluation idiom, a leaderboard of returns over a fixed window, certifies neither the soundness…
InvestPhilBench: A Multi-Layer Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy
Mingguang Chen, Bo Qu
Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and apply the specific procedural…
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops
Mingguang Chen, Licheng Wang, Bo Qu
AI systems increasingly participate in their own improvement: revising their outputs, adapting their harnesses during deployment, training on data they generate, and conducting AI…
LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection
Bo Qu, Zhurong Wang, Daisuke Yagi +4
This paper presents a novel approach to e-commerce payment fraud detection by integrating reinforcement learning (RL) with Large Language Models (LLMs). By framing transaction risk…
Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection
Bo Qu, Zhurong Wang, Minghao Gu +4
The burgeoning e-Commerce sector requires advanced solutions for the detection of transaction fraud. With an increasing risk of financial information theft and account takeovers, d…