mirror README.md from gshevchenko/contentos-preprint

README.md

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