5 citations · 8 across the 20 of their papers we have counts for
21 papers
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…
ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning
Zhengzhuo Xu, SiNan Du, Yiyan Qi +4
Multimodal Large Language Models (MLLMs) have emerged as powerful tools for chart comprehension. However, they heavily rely on extracted content via OCR, which leads to numerical h…
NUM2EVENT: Interpretable Event Reasoning from Numerical time-series
Ninghui Feng, Yiyan Qi
Large language models (LLMs) have recently demonstrated impressive multimodal reasoning capabilities, yet their understanding of purely numerical time-series signals remains limite…
QuantBench: Benchmarking AI Methods for Quantitative Investment
Saizhuo Wang, Hao Kong, Jiadong Guo +7
The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This…
Unleashing Expert Opinion from Social Media for Stock Prediction
Wanyun Zhou, Saizhuo Wang, Xiang Li +3
While stock prediction task traditionally relies on volume-price and fundamental data to predict the return ratio or price movement trend, sentiment factors derived from social med…
CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization
Sida Lin, Yankai Chen, Yiyan Qi +5
The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance. Emerging learning-base…
Financial Wind Tunnel: A Retrieval-Augmented Market Simulator
Bokai Cao, Xueyuan Lin, Yiyan Qi +3
Market simulator tries to create high-quality synthetic financial data that mimics real-world market dynamics, which is crucial for model development and robust assessment. Despite…
Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making
Jian Guo, Saizhuo Wang, Yiyan Qi
Multi-stage decision-making is crucial in various real-world artificial intelligence applications, including recommendation systems, autonomous driving, and quantitative investment…
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
Muzhi Li, Cehao Yang, Chengjin Xu +5
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vu…
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
Xiaojun Wu, Junxi Liu, Huanyi Su +10
As large language models (LLMs) increasingly permeate the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. Existing…
ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
Zhengzhuo Xu, Bowen Qu, Yiyan Qi +4
Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart…
MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
Zhanpeng Chen, Chengjin Xu, Yiyan Qi +1
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities. However, a significant draw…
Financial Knowledge Large Language Model
Cehao Yang, Chengjin Xu, Yiyan Qi
Artificial intelligence is making significant strides in the finance industry, revolutionizing how data is processed and interpreted. Among these technologies, large language model…
Context Graph
Chengjin Xu, Muzhi Li, Cehao Yang +4
Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples. However, triple-based KGs lack the conte…
ChartBench: A Benchmark for Complex Visual Reasoning in Charts
Zhengzhuo Xu, Sinan Du, Yiyan Qi +3
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in image understanding and generation. However, current benchmarks fail to accurately evaluate the chart…