5 papers
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…
NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management
Changlun Li, Peixian Ma, Qiqi Duan +2
Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information a…
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…