{"slug": "protegrity-pii-technical-report", "title": "Protegrity-PII Technical Report", "summary": "A technical report submitted to arXiv on 21 October 2026 presents Protegrity-PII, a new PII decision model for structured and unstructured text that achieves a typed recall of 0.915 with a precision of 0.907 on PII-SCOPE, a new benchmark the authors propose. The model processes text at 0.49 seconds per million characters at $0.0005 per million characters on a single RTX 6000 using batched inference, and the authors report superior out-of-distribution generalization against a selection of state-of-the-art models and datasets, attributed in part to the expansive entity type taxonomy used in training.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 21 Oct 2026]\n\n# Title:Protegrity-PII Technical Report\n\n[View PDF](https://arxiv.org/pdf/2610.30518)\n\n[HTML (experimental)](https://arxiv.org/html/2610.30518v1)\n\nAbstract:We present Protegrity-PII, a new PII decision model for structured and unstructured text. Protegrity-PII achieves a typed recall of 0.915 with a precision of 0.907 on a new benchmark we propose, PII-SCOPE. Our model processes text in 0.49 seconds per million characters at \\$0.0005 per million characters on a single RTX 6000 using batched inference. Due in part to the expansive nature of the entity type taxonomy used in training, Protegrity-PII achieves superior out-of-distribution generalization when benchmarked against a selection of state of the art models and datasets.\n    \n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/protegrity-pii-technical-report", "canonical_source": "https://arxiv.org/abs/2610.30518", "published_at": "2026-10-23 04:00:00+00:00", "updated_at": "2026-10-23 04:13:00.985995+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "natural-language-processing", "ai-research"], "entities": ["Protegrity-PII", "PII-SCOPE", "arXiv", "RTX 6000"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/protegrity-pii-technical-report", "markdown": "https://wpnews.pro/news/protegrity-pii-technical-report.md", "text": "https://wpnews.pro/news/protegrity-pii-technical-report.txt", "jsonld": "https://wpnews.pro/news/protegrity-pii-technical-report.jsonld"}}