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add README landing page

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  ---
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- language: [en, ru]
 
 
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  license: mit
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- tags: [ai-text-detection, reproducibility, bilingual, adversarial-robustness, calibration, mirror]
 
 
 
 
 
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  ---
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- # ContentOS — Reproducible Bilingual AI-Text-Detection Ensemble (mirror)
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- > **Canonical hosting:** https://huggingface.co/datasets/Humanswith-ai/contentos-preprint
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- >
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- > This personal mirror is kept in sync but the Humanswith.ai org dataset is
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- > the primary source. Please cite the org URL.
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- **Pre-print v1.0 (2026-04-27)** · Author: Gregory Shevchenko (Humanswith.ai)
 
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- All files (paper.pdf, paper.html, paper.md, REPRODUCIBILITY.md, figures/) are
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- identical to the canonical dataset. See the canonical README for details, code
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- repository roadmap, and citation.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Cite as
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  ```bibtex
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  @misc{contentos2026,
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  title={ContentOS: A Reproducible Bilingual AI-Text-Detection Ensemble with Adversarial Robustness Evaluation},
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- author={Shevchenko, Gregory and Humanswith.ai team},
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  year={2026},
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- url={https://huggingface.co/datasets/Humanswith-ai/contentos-preprint},
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  }
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  ```
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ - ru
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  license: mit
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+ tags:
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+ - ai-text-detection
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+ - reproducibility
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+ - bilingual
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+ - adversarial-robustness
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+ - calibration
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  ---
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+ # ContentOS — Reproducible Bilingual AI-Text-Detection Ensemble
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+ **Pre-print v1.0 (2026-04-27)**
 
 
 
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+ This repository contains the open pre-print and supporting artifacts for
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+ ContentOS, a reproducible English+Russian AI-text-detection ensemble.
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+ ## Authors and affiliation
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+
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+ - **Gregory Shevchenko** — author, Humanswith.ai (founder)
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+ - **Humanswith.ai team** — methodology, calibration, evaluation infrastructure
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+
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+ ContentOS is a Humanswith.ai product. This preprint is published under
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+ the author's personal HuggingFace account; the supporting code repository
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+ is maintained under the organization account (see "Code repository" below).
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+
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+ - Author profile: https://huggingface.co/gshevchenko
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+ - Organization: https://humanswith.ai
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+ - Contact for collaboration: open a Discussion on this dataset
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+
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+ ## Code repository
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+
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+ The evaluation scripts, regression test suite, and atomic-swap deploy
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+ tooling will be released at:
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+
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+ - **`github.com/humanswith-ai/contentos-benchmark`** (public, planned within ~2 weeks following the v1.12 RU recalibration chain)
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+
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+ Until then, the methodology in `REPRODUCIBILITY.md` is sufficient for
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+ independent re-implementation. For early access, please open a Discussion
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+ on this dataset.
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+
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+ ## Headline numbers (v1.11 production calibration)
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+
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+ | Metric | EN | RU |
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+ |---|---|---|
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+ | OOD AUROC (44-text smoke) | **0.821** | **0.837** |
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+ | Wrong-rate | 4% | 9% |
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+ | p50 latency (EN ensemble) | **1.2 s** | — |
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+ | Adversarial AUROC (n=300, OOD) | **0.998** | — |
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+
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+ ## Files
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+
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+ - `paper.pdf` — full pre-print (~6,000 words, 9 sections + 5 appendices)
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+ - `paper.html` — self-contained HTML version with embedded figures
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+ - `paper.md` — source markdown
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+ - `figures/` — 4 figures (PNG + SVG)
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+ - `REPRODUCIBILITY.md` — open methodology, how to reproduce in 90 minutes
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+
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+ ## Reproducibility
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+
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+ The full methodology and calibration corpus description are documented in
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+ `REPRODUCIBILITY.md`, which is sufficient for independent re-implementation
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+ of the ensemble.
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+
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+ A public mirror with the evaluation scripts (`eval_ensemble_corpus.py`,
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+ 8 pinned regression tests, atomic-swap deploy with 30-second rollback)
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+ will be released within ~2 weeks following the v1.12 RU recalibration
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+ chain. Target reproduction infrastructure: Hetzner CX43 (8 vCPU, no GPU,
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+ ~€14/month) or equivalent.
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+
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+ For early access before the public mirror, please open a discussion on
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+ this dataset.
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  ## Cite as
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  ```bibtex
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  @misc{contentos2026,
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  title={ContentOS: A Reproducible Bilingual AI-Text-Detection Ensemble with Adversarial Robustness Evaluation},
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+ author={Humanswith.ai team},
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  year={2026},
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+ url={https://huggingface.co/datasets/gshevchenko/contentos-preprint},
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  }
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  ```
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+
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+ ## License
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+
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+ MIT for code, methodology, and corpus aggregation. Underlying data sources retain their original licenses (HC3, AINL-Eval-2025, ai-text-detection-pile).