55 citations · 93 across the 18 of their papers we have counts for
18 papers
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
Jiashuo Sun, Jimeng Shi, Yixuan Xie +10
Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where so…
RRAttention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference
Siran Liu, Guoxia Wang, Sa Wang +7
The quadratic complexity of attention mechanisms poses a critical bottleneck for large language models processing long contexts. While dynamic sparse attention methods offer input-…
Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation
Jiashuo Sun, Pengcheng Jiang, Saizhuo Wang +13
Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise, even when the required evidence appears in the top-K results. A key reason is that retr…
DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets
Wanyun Zhou, Saizhuo Wang, Mihai Cucuringu +5
The lead-lag effect, where the price movement of one asset systematically precedes that of another, has been widely observed in financial markets and conveys valuable predictive si…
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…
From Deep Learning to LLMs: A survey of AI in Quantitative Investment
Bokai Cao, Saizhuo Wang, Xinyi Lin +4
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in de…
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…
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…
DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction
Jianyuan Zhong, Zhijian Xu, Saizhuo Wang +3
In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation. Traditional methods transform raw stock data of varying freque…
Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment
Hang Yuan, Saizhuo Wang, Jian Guo
Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT. This system…
QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model
Saizhuo Wang, Hang Yuan, Lionel M. Ni +1
Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence.However, tailoring these agents for specialized do…
A Principled Framework for Knowledge-enhanced Large Language Model
Saizhuo Wang, Zhihan Liu, Zhaoran Wang +1
Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in…
On the Evolution of Knowledge Graphs: A Survey and Perspective
Xuhui Jiang, Chengjin Xu, Yinghan Shen +6
Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensiv…
Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
Saizhuo Wang, Hang Yuan, Leon Zhou +3
One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-craft…
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang +6
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep an…
Low-Complexity Iterative Methods for Complex-Variable Matrix Optimization Problems in Frobenius Norm
Sai Wang, Yi Gong
Complex-variable matrix optimization problems (CMOPs) in Frobenius norm emerge in many areas of applied mathematics and engineering applications. In this letter, we focus on solvin…
Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence
Jian Guo, Saizhuo Wang, Lionel M. Ni +1
Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mains…