While multi-modal sequential recommendation has achieved significant success, existing methods typically rely on powerful sequential encoders, such as Transformers, to capture dynamic interests within a user's history. However, this paradigm is confined to modeling local, intra-sequence dependencies, causing it to over...
Jie Luo, Wen-Yu Zhang, Yuan Fang et al.· ACM Transactions on Informat...· 0 citations
Generator--critic trading systems pair an LLM-based trader that makes trading decisions with a red team agent that evaluates these decisions and provides feedback for revision. However, improving individual trading decisions does not ensure that both agents learn from experience and improve together over time. This rai...
Chang Zhou, Jia-Run Zhou, Xing-Tong Yu et al.· 0 citations
A unified benchmark that evaluates representative machine learning, reinforcement learning, LLM-based, and agent-based trading methods in cryptocurrency markets through three progressively more realistic stages through historical backtesting, prospective exchange-based paper trading, and real-money live trading is pres...
Xing-Tong Yu, Jia-Run Zhou, Guan-Lin Ding et al.· 0 citations
This work introduces a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys and proposes TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts.
Chang Zhou, Xing-Tong Yu, Minbin Huang et al.· 0 citations
Molecular graph representation learning is widely used in chemical and biomedical research, and reusing widely available and well-validated pre-trained 2D encoders, while incorporating molecular domain knowledge during downstream adaptation, offers a more practical alternative.
