Z. Zhang

AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models

This work proposes AURORA, a novel hallucination detection framework that shifts the focus from static representations to the weight-gradient dynamics of LLMs, and achieves strong hallucination detection performance across four model families and four benchmark datasets.