Xin He

SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents

Xin He, Yanlin Wang, Mingwei Liu, et al.

Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

Yuyuan Feng, Zhishang Xiang, Chaobin Yang, et al.

FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

Yushu Cai, Qingrui Zhu, Lei Liu, et al.

VEPO: Variable Entropy Policy Optimization for Low-Resource Language Foundation Models

Chonghan Liu, Yimin Du, Qi An, et al.

This paper introduces VEPO, a new training method that helps AI language models work better with low-resource languages (languages with less training data). The method uses reinforcement learning with built-in quality checks to ensure the model produces properly formatted and grammatically correct outputs, while also dynamically balancing between exact accuracy and natural-sounding responses. Tests show VEPO significantly improves translation quality and efficiency for underrepresented languages.