arXiv:2606. 02184v1 Announce Type: cross Abstract: These names do not exist.
By Micha{\l} Brzozowski, Neo Christopher Chung
arXiv:2609.15369v1 Announce Type: new
Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
By Jochen Madler (Sitefire)
arXiv:2608. 05157v1 Announce Type: cross Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias.
By Bulambo Mwendelwa Gloire, Prasenjit Mitra
The study examined biomedical research articles from 2022 to March 2026 that employed large language models (LLMs). It found that 42% of the most frequently used models were already retired or scheduled to retire within two years of publication, with a median retirement interval of 538 days. This high rate of model deprecation threatens the reproducibility of biomedical AI research.
By Nathan Wolfrath, Meghan Conroy, Thomas Kosten, Dave Bell, Bhabishya Neupane, Jonah Kindel, Anjishnu Banerjee, Priya Deshpande, Bradley Taylor, Anai N. Kothari
arXiv:2606. 11105v1 Announce Type: cross Abstract: Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them.
By Haeji Jung, Hila Gonen
The paper audits a 366‑day autobiographical book generated by a large language model (LLM) against an independent verification corpus. Using a four‑level rubric, 354 of the 366 days (96.7%) failed verification, with only 12 days containing corroborated scenes and 19 days containing actively contradicted claims. Regenerating the same days with current models yielded 100% verification failure, while grounding the generation in the subject’s own corpus improved the rate to 83.3% but still left substantial residual failure.
By Heather Renze