canonical README + Authors

README.md

@@ -1,48 +1,21 @@
 ---
-language:
-  - en
-  - ru
+language: [en, ru]
 license: mit
-tags:
-  - ai-text-detection
-  - reproducibility
-  - bilingual
-  - adversarial-robustness
-  - calibration
+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.
+**Pre-print v1.0 (2026-04-27)** · Canonical hosting under Humanswith.ai org.
 
 ## Authors and affiliation
 
-- **Gregory Shevchenko** — author, Humanswith.ai (founder)
+- **Gregory Shevchenko** — author, founder of Humanswith.ai
 - **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:
+Personal mirror: https://huggingface.co/datasets/gshevchenko/contentos-preprint
 
-- **`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)
+## Headline numbers (v1.11 production)
 
 | Metric | EN | RU |
 |---|---|---|
@@ -53,38 +26,31 @@ on this dataset.
 
 ## 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
+- `paper.pdf` / `paper.html` / `paper.md` — pre-print (~6,000 words, 9 sections + 5 appendices)
 - `figures/` — 4 figures (PNG + SVG)
-- `REPRODUCIBILITY.md` — open methodology, how to reproduce in 90 minutes
+- `REPRODUCIBILITY.md` — methodology, sufficient for independent re-implementation
 
-## Reproducibility
-
-The full methodology and calibration corpus description are documented in
-`REPRODUCIBILITY.md`, which is sufficient for independent re-implementation
-of the ensemble.
+## Code repository
 
-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.
+The evaluation scripts and regression test suite will be released at
+**`github.com/humanswith-ai/contentos-benchmark`** within ~2 weeks following
+the v1.12 RU recalibration chain. Until then, `REPRODUCIBILITY.md` is the
+canonical methodology reference.
 
-For early access before the public mirror, please open a discussion on
-this dataset.
+For early access: 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},
+  author={Shevchenko, Gregory and Humanswith.ai team},
   year={2026},
-  url={https://huggingface.co/datasets/gshevchenko/contentos-preprint},
+  url={https://huggingface.co/datasets/Humanswith-ai/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).
+MIT for code, methodology, and corpus aggregation. Underlying data sources
+retain their original licenses (HC3, AINL-Eval-2025, ai-text-detection-pile).