4 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets
Xingtong Yu, Jiarun Zhou, Guanlin Ding +12
AI-based trading methods have rapidly evolved from machine learning and reinforcement learning to large language models (LLMs) and trading agents, yet their performance is still pr…
TradingMoE: Routing the Right Experts in Evolving Markets
Chang Zhou, Xingtong Yu, Minbin Huang +4
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can…
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
Xingtong Yu, Zhongwei Kuai, Chang Zhou +6
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…
Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights
Xingtong Yu, Shenghua Ye, Ruijuan Liang +4
Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in gr…