Author

Z. Zhang

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Jun 2026

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.

Z. Zhang, Hainan Zhang, Zhiming Zheng · arXiv.org · 0 citations