{"slug": "pangram-4-technical-report", "title": "Pangram 4 Technical Report", "summary": "Pangram Labs released Pangram 4, a deep-learning AI-text classification model achieving an AUROC of 0.9916 with a false positive rate of 0.0041% and a false negative rate of 0.3396%. The model shows improved accuracy over Pangram 3, superior out-of-distribution generalization, robustness to adversarial attacks, and state-of-the-art performance on AI text detection benchmarks.", "body_md": "arXiv:2607.27183v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/pangram-4-technical-report", "canonical_source": "https://www.machinebrief.com/news/pangram-4-technical-report-npf6", "published_at": "2026-07-30 04:00:00+00:00", "updated_at": "2026-07-30 05:34:22.019474+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-products"], "entities": ["Pangram Labs", "Pangram 4", "Pangram 3"], "alternates": {"html": "https://wpnews.pro/news/pangram-4-technical-report", "markdown": "https://wpnews.pro/news/pangram-4-technical-report.md", "text": "https://wpnews.pro/news/pangram-4-technical-report.txt", "jsonld": "https://wpnews.pro/news/pangram-4-technical-report.jsonld"}}