3 papers
cs.LG2026
Can Language Models Discover Scaling Laws?
Haowei Lin, Haotian Ye, Wenzheng Feng +8
Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To invest…
cs.LG2025
LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges
Xu Cheng, Liang Yao, Feng He +6
Graph Neural Networks (GNNs) have emerged as powerful tools for various graph mining tasks, yet existing scalable solutions often struggle to balance execution efficiency with pred…
cs.CL2025
Learning Evolving Tools for Large Language Models
Guoxin Chen, Zhong Zhang, Xin Cong +5
Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of…