activity
20242026
collaborators

9 papers

cs.IR2026

ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services

Qingqing Long, Haotian Chen, Chenyang Zhao +6

The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sha…

cs.LG2025

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization

Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +7

Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream ma…

cs.CL2025

Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training

Meng Xiao, Xunxin Cai, Qingqing Long +3

Corpus distillation for biomedical large language models (LLMs) seeks to address the pressing challenge of insufficient quantity and quality in open-source annotated scientific cor…

cs.LG2025

FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies

Tianqi He, Xiaohan Huang, Yi Du +6

Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspec…

cs.LG2025

SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models

Chuan Qin, Xin Chen, Chengrui Wang +13

In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discov…

cs.AI2025

Comprehensive Metapath-based Heterogeneous Graph Transformer for Gene-Disease Association Prediction

Wentao Cui, Shoubo Li, Chen Fang +4

Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological…