Qinghua Xing — accepted AI-conference papers

2026

NAIPv2: Debiased Pairwise Learning for Efficient Paper Quality Estimation

Penghai Zhao, Jinyu Tian, Qinghua Xing, Xin Zhang, Zheng Li, Jianjun Qian +2

ICLR 2026poster

The ability to estimate the quality of scientific papers is central to how both humans and AI systems will advance scientific knowledge in the future. However, existing LLM-based estimation methods suffer from high inference cost, whereas the faster direct score regression approach is limited by sca…

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2025

From Words to Worth: Newborn Article Impact Prediction with LLM

Penghai Zhao, Qinghua Xing, Kairan Dou, Jinyu Tian, Ying Tai, Jian Yang +2

AAAI 2025technical

Predicting the future impact of newly published articles is pivotal for advancing scientific discovery in an era of unprecedented scholarly expansion. This paper introduces a promising approach, leveraging the capabilities of LLMs to predict the future impact of newborn articles solely based on titl…

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