Xiaoning Ren — accepted AI-conference papers

2026

ProCURE: Addressing the Programming Concept Understanding Gap for Code Generation in LLMs via Concept-Aware Consistency Learning

Xiaoning Ren, Qiang Hu, Wei Ma, Chongyang Liu, Yan Li, Yao Zhang +3

IJCAI 2026

Although Large Language Models (LLMs) excel at code generation, recent research reveals that they exhibit an insufficient grasp of core programming concepts, such as data flow and control flow. This limitation undermines their robustness when encountering variations in these concepts in practice; ho

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2025

Rust-doctor: Enhanced Feature for Rust Ownership and Lifetime Repair with Balanced Training Data Generation

Wenzhang Yang, Xiaoning Ren, Cuifeng Gao, Yinxing Xue

EMNLP 2025

As a relatively new programming language, Rust has gained significant popularity in recent years due to its safety features during compilation. However, Rust developers often face challenges stemming from its strict compilation checks due to the steep learning curve of safety rules. To make matters

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