The paper introduces Robust Weighted Profile-Loss Change Point Detection (RWCP), a method for locating authorship transitions in mixed human‑LLM documents using detector scores with varying reliability. RWCP combines capped reliability weights, Huber profile gains, and a narrowest‑over‑threshold search to express the population gap as a merge cost, enabling recovery of change points without a closed‑form nonlinear center. Experiments on five benchmark families show that core RWCP reduces WindowDiff by 17.6% compared to weighted change‑point detection, and an extended variant RWCP‑R further improves performance, especially for isolated changes.
By Wan Tian, Zhongyi Li, Yawen Li, Rui Zhang, Yijie Peng, Fuzhen Zhuang
arXiv:2509. 21160v2 Announce Type: replace-cross Abstract: With the growing use of large language models, concerns over content authenticity have spurred a variety of watermarking schemes.
By Soham Bonnerjee, Subhrajyoty Roy, Sayar Karmakar
arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.
By Yangjun Lu, Hongyi Zhou, Fabian Spill, Kai Ye, Chengchun Shi, Jin Zhu
LLMTrace is a new large‑scale bilingual (English and Russian) corpus designed to improve AI‑written text detection. It contains character‑level annotations that enable precise localization of AI‑generated segments, supporting both full‑text binary classification and interval detection tasks. The dataset is built from a diverse set of modern proprietary and open‑source LLMs to address gaps in existing resources, such as outdated models, limited language coverage, and lack of mixed human‑AI authorship data.
By Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Maksim Kuprashevich
The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents. Existing methods for detecting LLM-generated text mainly focus on document-level classification and cannot identify which parts of the text are generated by LLMs.
arXiv:2606. 06481v1 Announce Type: cross Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing.
By Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tianjun Yao, Xinyi Shang, Yi Tang, Jiacheng Cui, Ahmed Elhagry, Salwa K. Al Khatib, Hao Li, Salman Khan, Zhiqiang Shen
skchange is an open‑source Python library that provides fast and flexible algorithms for detecting structural changes in time series. It offers modular, composable methods based on cost minimisation and statistical tests, and includes features such as anomalous segment detection, high‑dimensional data support, automatic penalty calibration, and a wide range of built‑in costs and tests. The library follows scikit‑learn conventions and uses Numba for high computational performance, with source code and documentation available on GitHub.
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...
The paper introduces DETECT-REMASK-REPAIR, a diffusion-based method for updating outdated spans in existing summaries while keeping supported content intact. It identifies, masks, and repairs only the changed regions using masked diffusion language models. Experiments on DialogSum and a new StreamSum benchmark show that this localized repair improves faithfulness, reduces repair time to under half a second, and offers trade‑offs between faithfulness, speed, and preservation of the original summary.
By Hao Zou, Zachary Horvitz, Chandhru Karthick, Zhou Yu, Kathleen McKeown
arXiv:2608. 19767v1 Announce Type: cross Abstract: Skchange is an open-source Python library for detecting structural changes in time series.
By Martin Tveten, Johannes Voll Kolst{\o}, Per August Jarval Moen
Large language models (LLMs) are trained on massive and largely undisclosed corpora that may contain copyrighted or privacy-sensitive content. Data contamination detection (DCD) therefore aims to determine whether a given text is a member of the pre-training corpus of a target LLM.
arXiv:2609.24278v1 Announce Type: new
Abstract: Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial m...
By Sven Jacob, Bardh Prenkaj, Weijia Shao, Gjergji Kasneci