The paper introduces a discrete-class framework for detecting human‑AI co‑authorship, distinguishing between superficial AI polishing and deeper AI‑driven discourse changes. It shows that adversarial paraphrasing can obscure detection signals, but augmenting training data with such paraphrases improves detector robustness across multiple datasets and generation models. The study demonstrates that principled data augmentation and a fine‑grained classification approach are essential for reliable co‑authorship detection.
arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.
arXiv:2610.07365v1 Announce Type: new Abstract: As LLMs increasingly assist scientific writing and peer review, detecting who wrote the text is no longer sufficient: we need to determine who contribu...
arXiv:2606. 00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals.
arXiv:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?
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:2504.11952v4 Announce Type: replace-cross Abstract: An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existe...
The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.
SharP is a text‑generation framework that uses both hard and soft prompts to guide large language models in creating task‑specific synthetic news samples for low‑resource fake news detection. The method combines structured factual constraints with learned semantic patterns, and employs a boundary‑aware strategy that focuses on classifier‑uncertain areas to refine decision boundaries. Dual objective optimization balances semantic alignment with source data and classifier feedback, aiming to improve robustness and generalization when labeled data are scarce.
arXiv:2608. 03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review.
The paper introduces ATLAS, a likelihood‑based method for model authorship attribution (MAA) that learns how each source family generates text and attributes new text by comparing token‑level likelihoods. It also presents MAABench, a benchmark for evaluating MAA under out‑of‑distribution scenarios such as unseen domains and future model versions. Experiments show ATLAS outperforms existing baselines, achieving 74.27% accuracy on the hardest MAABench setting and correctly identifying teacher models in distillation experiments.
The paper introduces MOSAIC, a large adversarial benchmark for detecting AI-generated text, and presents NeuroStat, a new framework that combines token‑level probabilistic logits with deep semantic hidden states from a single language model. NeuroStat fuses these signals via Macro‑State Residual Modulation and uses orthogonal and contrastive losses to learn complementary representations. Experiments show that NeuroStat outperforms existing methods on MOSAIC, achieving superior robustness against adversarial attacks.
arXiv:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.