Humanswith-ai/contentos-preprint

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.

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

@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).