arXiv:2605.12890v2 Announce Type: replace-cross
Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...
By Luxu Liang, Xiang Li
arXiv:2607. 29539v1 Announce Type: cross Abstract: Standard AI-text detection benchmarks compare human-written text against text generated directly by large language models (LLMs).
By Gaetano Perrone, Simon Pietro Romano
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) a...
arXiv:2608.29903v1 Announce Type: cross
Abstract: The rapid advancement of large language models (LLMs) has made AI-generated text detection increasingly critical. Existing zero-shot detectors assume...
By Xiaoyang Han, Lvxiaowei Xu, Ming Cai
arXiv:2606. 06315v1 Announce Type: new Abstract: Recent advances in interpretability suggest that large language models (LLMs) implicitly encode signals in their generated text that enable self-recognition of their outputs.
By Thibaud Ardoin, Jonas Sch\"afer, Gerhard Wunder
The paper investigates how to detect text generated by large language models (LLMs) when the data has been edited or contaminated. By modeling human and machine text as finite-order Markov processes with Huber contamination, the authors derive an exact boundary that determines when reliable detection is possible. They show that a clipped likelihood-ratio test can achieve vanishing worst‑case errors below this boundary and that clipping improves robustness across several detectors and datasets, yielding significant gains in true‑positive rates at a fixed false‑positive rate.
By Jiaxun Li, Saptarshi Chakraborty, Ambuj Tewari