Pangram 4 Technical Report
Abstract
We present Pangram 4, the latest deep-learning-based AI-text classification model from Pangram Labs. We achieve an AUROC of 0.9916 with a false positive rate of 0.0041% and a false negative rate of 0.3396%. In addition to its increased overall accuracy compared with Pangram 3, Pangram 4 exhibits superior out-of-distribution generalization and robustness to adversarial attacks. Another novel contribution of Pangram 4 is its improved ability to distinguish fine-grained edits and mixed AI-human co-authored text. We demonstrate improvements to both boundary detection tasks and the detection of interleaved AI assistance. Finally, we report metrics on standard AI detection benchmarks showing that Pangram 4 achieves state-of-the-art performance on the AI text detection task across a wide variety of settings and domains.
Cite
@article{arxiv.2607.27183,
title = {Pangram 4 Technical Report},
author = {Ben Glickenhaus and Katherine Thai and Jenna Russell and Elyas Masrour and Yue Han and Max Spero and Bradley Emi},
journal= {arXiv preprint arXiv:2607.27183},
year = {2026}
}