ExplorerComputational LinguisticsNLP
Research PaperResearchia:202607.30010

Pangram 4 Technical Report

Ben Glickenhaus

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-...

Submitted: July 30, 2026Subjects: NLP; Computational Linguistics

Description / Details

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.


Source: arXiv:2607.27183v1 - http://arxiv.org/abs/2607.27183v1 PDF: https://arxiv.org/pdf/2607.27183v1 Original Link: http://arxiv.org/abs/2607.27183v1

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Submission Info
Date:
Jul 30, 2026
Topic:
Computational Linguistics
Area:
NLP
Comments:
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