---
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).