activity
20242026
most citedAutomate Strategy Finding with LLM in Quant Investment

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System

Xujia Li, Xin Li, Junquan Huang +3

Heterogeneous Multi-Embodied Agent Systems involve coordinating multiple embodied agents with diverse capabilities to accomplish tasks in dynamic environments. This process require…

cs.CL2026

LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

Yushi Sun, Xujia Li, Nan Tang +3

Column Type Annotation (CTA), which assigns a semantic type to a table column, underpins data integration, cleaning, and search over data lakes. State-of-the-art annotators are pre…

cs.AI2025

Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models

Yuxiang Chen, Zuohan Wu, Ziwei Wang +6

Motivated by the observed human-like behaviours in Large Reasoning Models (LRMs), this paper introduces a comprehensive taxonomy to characterise atomic reasoning steps and analyse…

q-fin.ST2025

Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization

Hao Wang, Jingshu Peng, Yanyan Shen +4

Stock recommendation is critical in Fintech applications, which leverage price series and alternative information to estimate future stock performance. Traditional time-series fore…

q-fin.PM2024★ 3 cited

Automate Strategy Finding with LLM in Quant Investment

Zhizhuo Kou, Holam Yu, Junyu Luo +7

We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our a…