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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
Public benchmark + evaluation scripts:
**https://github.com/humanswith-ai/contentos-benchmark**
The repo includes regression test suite (8 pinned baselines, 0.05s),
streaming-CSV eval scripts (partial-tolerant), per-genre AUROC
analyzer, and the calibration JSON shape for v1.11 production state.
## Headline numbers (v1.11 production, 2026-04-29 measurement)
| Metric | EN | RU |
|---|---|---|
| OOD AUROC (176-sample expanded smoke) | **0.864** | **0.846** |
| Wrong-rate | 4% | 9% |
| p50 latency (EN ensemble) | **1.2 s** | — |
| Adversarial AUROC (n=300, OOD-paired) | **0.998** | — |
Earlier v1.0 paper reported 0.802 / 0.847 on the original 44-text
smoke battery; the 4× expanded battery with class balance per
(lang, genre) cell stabilized numbers upward. Per-genre details in
the [companion repo](https://github.com/humanswith-ai/contentos-benchmark).
## 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).
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