Yonghuang Wu — accepted AI-conference papers

2026

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences

Pengfei Song, Fangjin Liu, Wenwen Zeng, Yonghuang Wu, Chengqian Zhao, Feiyu Yin +2

IJCAI 2026

Contemporary glioma diagnosis integrates molecular features with histopathology to guide clinical decision-making. However, in clinical settings, divergent imaging protocols result in incomplete MRI sequences, leading to two primary challenges: forcing existing frameworks to discard a large portion

Cited by 0 Scholar ↗

2025

Instance-level Randomization: Toward More Stable LLM Evaluations

Yiyang Li, Yonghuang Wu, Ying Luo, Liangtai Sun, Zishu Qin, Lin Qiu +2

EMNLP 2025

Evaluations of large language models (LLMs) suffer from instability, where small changes of random factors such as few-shot examples can lead to drastic fluctuations of scores and even model rankings. Moreover, different LLMs can have different preferences for a certain setting of random factors. As

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗