Author

Y. Qian

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Preprint Jul 2026

DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models

A D-LLM hallucination detection framework that formulates the Denoising trajectories as Multivariate Time Series over learnable latent variables (DeMTS for short) that outperforms existing hallucination detection methods while maintaining strong robustness, efficiency, and cross-task transferability.

Xin Zhang, Yili Wang, Yue Tan et al. · 0 citations

Preprint Jun 2026

TRE: Training-Free Hallucination Detection for Diffusion Language Models

TRE is a parameter-free and single-run metric that estimates hallucination risk directly from the entropy signals of a single generation, without requiring any detector training or repeated sampling, and enjoys strong generalizability, efficiency, and robustness.

Pengcheng Weng, Y. Qian, Yue Tan et al. · 0 citations