5 citations · 8 across the 20 of their papers we have counts for
7 papers · 1 filter
Hierarchical Multi-Task Learning with Liquidity-Aware Signals for Stock Forecasting
Hengyi Yang, Sida Lin, Yiyan Qi +4
Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learnin…
Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation
Fengrui Hua, Hengyi Yang, Xinlei Hao +5
Quantitative trading is moving from isolated predictive models toward agentic workflows that combine reasoning, tool use, memory, and feedback. This survey reviews agentic quantita…
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Yansen Zhang, Yilu Liu, Tianyu Liu +7
Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers ass…
VSearcher: Long-Horizon Multimodal Search Agent via Reinforcement Learning
Ruiyang Zhang, Qianguo Sun, Chao Song +2
Large models are increasingly becoming autonomous agents that interact with real-world environments and use external tools to augment their static capabilities. However, most recen…
Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling
Xiang Li, Zikai Wei, Yiyan Qi +6
Financial market movements are often driven by discrete financial events conveyed through news, whose impacts are heterogeneous, abrupt, and difficult to capture under purely numer…
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting
Yuxing Tian, Fengran Mo, Weixu Zhang +2
The strong capabilities of recent Large Language Models (LLMs) have made them highly effective for zero-shot re-ranking task. Attention-based re-ranking methods, which derive relev…
Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
Yuxing Tian, Yiyan Qi, Fengran Mo +3
Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervi…