From the 2 of 6 linked papers with an AI index.
6 papers
EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
Jie Mao, Changlun Li, Xiang Li +7
EVOQUANT is a framework that uses large language models together with a verifier pipeline to automatically diagnose, edit, and improve quantitative trading strategies, achieving hi…
NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management
Changlun Li, Peixian Ma, Qiqi Duan +2
The paper presents NextFund, a platform that records and visualizes the full decision-making process of LLM‑based financial agents in live markets, enabling detailed comparison and…
Can Agentic Trading Systems Pay for Their Own Intelligence?
Qiqi Duan, Changlun Li, Chen Wang +10
Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce tradin…
Model Merging to Evolution: Parameter Space Exploration for Expert Models
Chao Wang, Yuchen Guo, Zheng Tan +4
Model merging integrates the capabilities of multiple expert models to create strong models for multiple tasks without additional training, thereby reducing computational resource…
TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
Yuxiang Luo, Haonan Long, Chen Wang +6
Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and naïve runs can…
Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking
Changlun Li, Yao Shi, Chen Wang +7
Large Language Models (LLMs) have demonstrated notable capabilities across financial tasks, including financial report summarization, earnings call transcript analysis, and asset c…