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.
arXiv:2605. 03723v2 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust.
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: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.
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.
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.
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).
arXiv:2605.19309v4 Announce Type: replace Abstract: Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answeri...
The paper investigates training‑free detection of machine‑generated text using spectral analysis. It shows that spectral energy correlates with variance in token probability trajectories and that human writing produces characteristic fluctuations, termed "generative vitality." The authors find that spectral signals are strongest for long, continuous, constrained generations, while shorter or mixed texts require additional confidence‑based metrics.
The paper introduces a controlled benchmark for evaluating large language models (LLMs) on key‑value pair extraction from documents with varying levels of OCR noise. It tests 136 configurations across five instruction‑tuned open‑weight LLMs, three datasets, and four text‑quality conditions, using deterministic decoding to generate 17,688 document‑level inferences. The study finds that clean‑text performance does not reliably predict real‑world robustness, model rankings can reverse under noisy conditions, and few‑shot demonstrations do not always improve accuracy, highlighting reliability risks in OCR‑to‑LLM pipelines.
arXiv:2607. 03680v1 Announce Type: new Abstract: Recent AI-generated text detection work often introduces a new benchmark together with a specialized detector tailored to it.
MultiGhostBench is a multilingual benchmark for authorship attribution of long-form text generated by large language models. It contains 928 books produced by five recent LLMs in six languages and three scripts, each averaging about 59,000 words, and is designed to test attribution methods under domain, author, and language shifts. Experiments show that no single attribution method dominates across all settings, with performance generally dropping under distribution shifts, and that transformer-based detectors retain generator information across languages while statistical and fingerprint-based detectors are more language‑dependent.
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.