Mingwei Liu — accepted AI-conference papers

2026

Position: Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility

Jialun Cao, Yuk-Kit Chan, Zixuan Ling, Wenxuan Wang, Shuqing Li, Mingwei Liu +9

ICML 2026poster

Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the com- munity interprets model capabilities. In the past few years, awareness of benchmark quality has grown. Yet, after a decade-scale (2014 - 2025) survey over 572 …

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2026

Uncovering Pretraining Code in LLMs: A Syntax-Aware Attribution Approach

Yuanheng Li, Zhuoyang Chen, Xiaoyun Liu, Yuhao Wang, Mingwei Liu, Yang Shi +2

AAAI 2026technical

As large language models (LLMs) become increasingly capable, concerns over the unauthorized use of copyrighted and licensed content in their training data have grown, especially in the context of code. Open-source code, often protected by open source licenses (e.g, GPL), poses legal and ethical chal

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