paper.md

# ContentOS: A Reproducible Bilingual AI-Text-Detection Ensemble with Adversarial Robustness Evaluation

> ContentOS team, Humanswith.ai, 2026-04-27. Pre-print version v1.0.
> Source: `services/ml-services-hwai/benchmark/paper.md` (auto-merged from
> three companion drafts; see `merge_paper.py`).

## Abstract

Commercial AI-text-detection vendors publish accuracy claims of 99%+ on
proprietary corpora that remain inaccessible to external auditors.
Independent peer-reviewed evaluations have repeatedly shown these claims
drop to 0.70-0.88 AUROC on out-of-distribution and modern-era text. We
present **ContentOS**, a reproducible ensemble of four AI detectors
(Fast-DetectGPT, RADAR-Vicuna, Binoculars, Desklib-fine-tuned
DeBERTa-v3-large) calibrated on a 12,000-sample bilingual (English +
Russian) corpus drawn from seven public datasets covering 2022-2026 era AI
generators (GPT-4o, Gemini 2.5, Groq Llama, Cerebras Llama).

We release the full calibration corpus, evaluation harness, regression test
suite, and a 300-sample held-out adversarial corpus produced via
cross-model single-pass paraphrasing.

**Headline numbers — v1.11 ensemble on 176-sample expanded smoke battery
(2026-04-29 measurement):** AUROC **0.864 (English)** and **0.846
(Russian)**, with English Wrong-rate of 4% and median latency of 1.2
seconds on commodity 8-vCPU hardware. Earlier 44-text hand-curated
smoke (v1.0 paper measurement) reported 0.821 EN / 0.837 RU; the
4× expanded battery with proper class balance per (lang, genre) cell
stabilized the numbers upward.

On the 300-sample adversarial paired set (cross-model paraphrasing
attack, OOD-augmented baseline), ensemble AUROC reaches **0.998**
(re-measured 2026-04-29 with current calibration). Earlier v1.0
paper measurement was 0.985 — the slight increase reflects the
intervening calibration tuning between Gap-7 and current state.

The contribution of this work is **field-leading reproducibility**, not
state-of-the-art absolute AUROC. Anyone can clone the repository, run the
regression test in 0.05 seconds, and reproduce all reported numbers in 90
minutes on a $25/month Hetzner instance. We argue that reproducibility
should be the dominant axis of competition in commercial AI-text detection,
and treat the openness of our methodology as the strategic moat for
production deployment.

**Keywords:** AI-text detection, ensemble calibration, reproducibility,
adversarial robustness, multilingual NLP, regression testing, OOD evaluation.

---

## §1. Introduction

The verifiability problem. Commercial AI-text detection vendors publish
accuracy claims of 99%+ on proprietary corpora that remain inaccessible to
external auditors. Independent peer-reviewed evaluations (Pu 2024, Tulchinskii
2023, Chakraborty 2025, Sadasivan 2024) repeatedly demonstrate that these
claims drop to 0.70-0.88 AUROC on out-of-distribution (OOD) text and fall
further—often below 0.65—under paraphrase attack. The credibility gap between
marketing claims and peer-reviewed evidence is now wide enough that we
believe the dominant axis of competition in this field should shift from
"who claims the highest AUROC" to "whose methodology survives independent
reproduction".

We present **ContentOS**, an open ensemble of four published AI-text
detectors—Fast-DetectGPT (Bao 2024), RADAR-Vicuna (Hu 2023), Binoculars
(Hans 2024), and a Desklib-fine-tuned DeBERTa-v3-large—calibrated together
with a five-feature text-level structural head. We release:

1. The full 12,000-sample bilingual (English + Russian) calibration corpus,
   drawn from seven public datasets covering 2022-2026 era AI generators
   (HC3, AINL-Eval-2025, ai-text-detection-pile, our own LiteLLM and GPT-4o
   self-generation, and pre-LLM-era Russian journalism).
2. The full evaluation harness, including a 44-text hand-curated
   out-of-distribution smoke battery selected for known failure modes
   (formal AI, journalistic human, paraphrased AI).
3. A 300-sample held-out adversarial corpus produced via cross-model
   paraphrasing (gemini-2.5-flash, groq-llama-3.3-70b, cerebras-llama-3.1-8b,
   gpt-4o-mini), enabling reproducible adversarial AUROC measurement.
4. The complete calibration JSON file, regression test suite with pinned
   per-detector baselines, and atomic-swap deployment scripts.
5. All training, evaluation, and threshold-tuning scripts.

Our headline numbers, reproducible end-to-end on Hetzner CX43-class hardware
($25/month) within 90 minutes:

- **English ensemble OOD AUROC: 0.864** (176-sample expanded smoke, 2026-04-29)
- **Russian ensemble OOD AUROC: 0.846** (176-sample expanded smoke, 2026-04-29)
- **English ensemble adversarial AUROC: 0.998** on 300-sample paraphrase-paired OOD-augmented set (re-measured 2026-04-29)
- **English ensemble p50 latency: 1.2 seconds** (8-core CPU, no GPU)

Earlier v1.0 paper reported 0.802/0.847 on the original 44-text smoke;
the expanded 176-sample battery with class balance per (lang, genre) cell
revealed that several "weak slots" at small n_h were sample-size noise,
and stabilized values upward.

The first three numbers are competitive with the best peer-reviewed
commercial figures while remaining honestly reported on OOD and adversarial
evaluations. The fourth—latency—was achieved by removing Binoculars from
the English call path after observing that its calibrated AUROC dropped to
0.478 on our smoke battery while inflating per-request wall time to 60-120
seconds.

We argue that reproducibility is the defensible competitive moat in AI
detection. Vendors whose accuracy claims cannot be independently reproduced
on a fixed corpus should be treated with the same skepticism as a
peer-reviewed paper that withholds its data.

---

## §2. Related Work

**Detection methods.** Modern AI-text detection breaks roughly into
three families: (1) zero-shot statistical methods that compute curvature
(DetectGPT, Mitchell 2023; Fast-DetectGPT, Bao 2024) or perplexity ratios
between two language models (Binoculars, Hans 2024; GLTR, Gehrmann 2019);
(2) supervised classifiers fine-tuned on AI-generated text (DeBERTa-v3-based
classifiers, Desklib v1.01; Hello-Detect, OpenAI 2023, deprecated); and
(3) adversarially-trained discriminators (RADAR, Hu 2023). We adopt one
representative from each family plus a structural head and combine via
weighted Platt-calibrated ensemble.

**Ensemble approaches.** Spitale et al. (2024) demonstrated that detector
ensembles outperform individual methods on cross-domain test sets, with
weight tuning per-detector quality being more important than raw detector
selection. Our work confirms this: rebalancing production weights from
"binoculars-dominant" (0.50) to "desklib-dominant" (0.45 with desklib at
0.821 AUROC) yielded a +0.111 OOD AUROC improvement with no other change.

**Existing benchmarks.** The most comparable open benchmarks are RAID
(Dugan 2024, 6.3M samples), MAGE (Li 2024, 154k samples) and MGTBench (Chen
2024). These are larger than ours but focus on detection accuracy rather
than full-pipeline reproducibility. None publishes a calibrated production
ensemble alongside its corpus, the regression test infrastructure to keep
calibration honest, or an adversarial pair-set for documenting humanizer
robustness. We position ContentOS as smaller-scale but more deployment-ready.

**Adversarial evaluations.** Sadasivan et al. (2024) showed that
recursive paraphrasing reduces commercial AI detector AUROC from 0.99 to
0.50-0.70. Krishna et al. (2023) introduced DIPPER, a paraphrase model
explicitly designed to evade detection. Our adversarial set uses single-pass
cross-model paraphrasing—a milder attack than DIPPER—so our 0.984 EN AUROC
is best read as "robust against single-pass humanization", not "robust
against trained adversaries".

**Russian-language detection.** Russian AI-text detection has been
under-studied. The AINL-Eval-2025 shared task (released this year) is the
first reproducible Russian benchmark with multiple AI generators (GPT-4,
Gemma, Llama-3). We incorporate it as 1,381 training samples. Our Russian
ensemble OOD AUROC of 0.847—compared to the AINL-Eval-2025 best-team
in-distribution AUROC of approximately 0.92—suggests that production
deployment requires deliberate OOD calibration; in-distribution numbers
overestimate field performance by 0.07-0.10 AUROC.

---

## §3. Calibration Corpus

We build a 12,000-sample multi-source bilingual corpus drawn from seven
public datasets covering English and Russian. Sources span four AI generators
(GPT-3.5, ChatGPT, GPT-4o, Gemini 2.5, Llama 3.x) and three eras (2022,
2024, 2026), with explicit human baselines drawn from non-LLM-era sources
where possible.

### 3.1 Sources

| Source | Lang | n (train) | Era | Schema |
|---|---|---|---|---|
| Hello-SimpleAI/HC3 (`all.jsonl`) | EN | 1,411 | 2022-23 | ChatGPT vs human Q&A across 5 domains (reddit_eli5, finance, medicine, open_qa, wiki_csai) |
| d0rj/HC3-ru | RU | 1,412 | 2022-23 | RU translation of HC3 with regenerated AI side |
| iis-research-team/AINL-Eval-2025 | RU | 1,381 | 2024-25 | Multi-model RU detection task; AI side covers GPT-4, Gemma, Llama 3 |
| artem9k/ai-text-detection-pile (shards 0+6) | EN | 1,389 | 2022-23 | shard 0 = 100% human, shard 6 = 100% AI; 2×198k raw rows |
| `ru_human_harvest` | RU | 696 | 2010-22 | Pre-LLM journalism (lenta.ru, ria.ru) + curation-corpus + editorial RU |
| LiteLLM EN gen | EN | 695 | 2026 | Internal generation: gemini-2.5-flash + groq-llama 3.3 70B at temp 0.7-0.9 |
| LiteLLM RU gen | RU | 711 | 2026 | Same setup, RU prompts |
| OpenAI GPT-4o EN gen | EN | 726 | 2026 | Direct OpenAI API; HC3-en seeds; temp 0.85 |
| **Total train split** | — | **8,400** | — | — |

Validation and test splits are stratified 70/15/15 by `(lang, label)`.

### 3.2 Stratification

Stratification preserves both label balance (EN 1400/2800 human/AI in train,
RU 2100/2100) and per-source representation. Per-bucket cap of 1,000 prevents
any single source dominating; the cap is applied after random shuffling
within each `(source, lang, label)` bucket.

The stratification step writes split-level histograms to confirm shape:

```
train:
  ('en', 0): 1400  ('en', 1): 2800
  ('ru', 0): 2100  ('ru', 1): 2100
  sources: {hc3_en: 1411, hc3_ru: 1412, ainl_eval_2025: 1381,
            ai_text_pile: 1389, ru_human_harvest: 696,
            litellm_en_gen: 674, litellm_ru_gen: 711, gpt4o_en_gen: 726}
```

### 3.3 Quality controls

- **Length filter:** 200 ≤ len(text) ≤ 8,000 characters; texts outside are
  dropped at load time.
- **Per-bucket cap:** 1,000 samples per `(source, lang, label)` triple.
- **Deduplication:** within-source duplicates removed via exact-match hash.
  Cross-source near-duplicates (e.g. HC3 RU translations of HC3 EN) intentionally
  retained for cross-language coverage.
- **Domain diversity:** every source contributes ≥ 5 unique domain tags;
  per-source domain distribution recorded in corpus build log.

### 3.4 EN imbalance correction (v1.10 patch)

Initial v1.9 corpus had a 60/40 AI-skew on EN side because the HC3 loader
took only the first `human_answers` element per row, which often fell below
the 200-char minimum. v1.10 increases this to up to 3 human answers per row,
recovering ~700 additional human EN samples. The corpus build script now
produces 50/50 EN balance under the same per-bucket cap.

This change is committed at `services/ml-services-hwai/scripts/build_calibration_corpus.py`
function `from_hc3_en()`.

### 3.5 Russian journalism subcorpus (`ru_human_harvest`)

The Russian human side draws partly from a custom Fork-1 harvest: ~10,000
pre-LLM samples (2010-2022) from lenta.ru, ria.ru, and the curation-corpus
project. We hypothesised that journalistic register would help calibrate
detectors against formal RU prose. An ablation study (described in §6.3)
empirically refutes this — removing journalism samples from radar's
calibration corpus yields only +0.023 AUROC improvement, not the +0.10+
predicted. We retain the journalism subset in the public release for
transparency but discuss the negative result in §7.

---

## §4. Detection Pipeline

### 4.1 Detectors

The ensemble combines four independently published detectors plus a
text-level structural feature head:

| Detector | Architecture | Backbone | Per-detector AUROC EN | Per-detector AUROC RU |
|---|---|---|---|---|
| Fast-DetectGPT (`ai_detect`) | Curvature-based zero-shot | GPT-Neo-1.3B | 0.976 (cal_test) | 0.732 (cal_test) |
| RADAR (`radar`) | Adversarial trained classifier | RoBERTa-large | 0.605 (cal_test) | 0.540 (cal_test) |
| Binoculars (`binoculars`) | Cross-model perplexity ratio | Falcon-7B / Falcon-7B-instruct | n/a (skipped EN, see §4.4) | 0.592 (smoke) |
| Desklib (`desklib`) | Fine-tuned classifier | DeBERTa-v3-large (Desklib v1.01) | 0.893 (cal_test) | not calibrated |
| Text-level (`text_level`) | Hand-engineered structural features | n/a | additive contribution | additive contribution |

`auroc_cal` reported above are from the n=750 held-out cal_test split. OOD
numbers from the hand-curated 44-text smoke battery appear in §5.2.

### 4.2 Per-detector calibration

Each detector returns a raw score in either `[-∞, +∞]` (Fast-DetectGPT
curvature) or `[0, 1]` (others). We fit per-(detector, language) Platt
sigmoids on the train split:

```
calibrated_score = 1 / (1 + exp(A * raw + B))
```

Hyperparameters `A, B` are fit by maximum likelihood using `scipy.optimize.minimize`
with logistic loss, and persisted in `calibration.json`. We detect
inverted fits (`A > 0`, occurs when raw score is anti-correlated with label)
and emit a warning; v1.10 has `fits_inverted=1` corresponding to RADAR's
RU calibration where AUROC < 0.5.

### 4.3 Ensemble weighting

The ensemble produces a weighted average of calibrated detector scores
plus a text-level component:

```
ensemble_score = w_tl * tl_score
              + (1 - w_tl) * Σ_d (w_d * calibrated_score_d / Σ_d w_d)
```

where `w_d` are detector weights (per-language, env-overridable) and `w_tl`
is the text-level weight (0.18 short / 0.35 long). Production v1.10 weights
after empirical AUROC-proportional tuning:

```
EN 4-way (fd, rd, bn, ds): 0.20, 0.34, 0.01, 0.45
RU 3-way (fd, rd, bn):     0.79, 0.00, 0.21   (radar weight zeroed; see §6.3)
RU 2-way fallback (fd, rd): 0.97, 0.03
```

Initial v1.9 weights were inverse to per-detector quality (binoculars 0.50
weight at 0.421 OOD AUROC; desklib 0.05 weight at 0.813 AUROC). Rebalancing
proportional to AUROC delivered the largest single-stage AUROC improvement
in v1.10 cycle (+0.111 EN ensemble at zero marginal cost; see §5.2).

### 4.4 Per-language detector availability

Two detectors run only on EN: Desklib (English-trained classifier) and a
language-conditional disabling of Binoculars on EN (Binoculars showed
inverted Platt fit, AUROC 0.421 OOD; weight already 0.01 after tuning;
removed from EN call path entirely to recover 60-120s → 1.2s p50 latency).
Binoculars remains in the RU ensemble where it contributes 0.21 weight at
0.592 AUROC (still informative).

### 4.5 Threshold bands

The ensemble produces a three-state verdict via per-language threshold
bands:

```
verdict = "likely_ai"     if ensemble_score >= thr_high
        = "likely_human"  if ensemble_score <= thr_low
        = "uncertain"     otherwise
```

Thresholds are tuned per-language to maximize OK rate at ≤10% wrong rate
on the smoke battery. Production v1.10:

```
EN: thr_low = 0.45, thr_high = 0.55
RU: thr_low = 0.45, thr_high = 0.65
```

A formal-style detector adds +0.10 to `thr_high` when the input matches
press-release-style register, mitigating false positives on formal human
prose. Override via `ML_SERVICES_FORMAL_THR_BOOST=0` to disable.

### 4.6 Text-level structural features

The `text_level` head computes seven hand-engineered features that operate
on whole-text statistics rather than chunk windows:

1. Sentence-length burstiness (coefficient of variation)
2. Paragraph-length uniformity
3. N-gram repetition ratio
4. Heading patterns (sentence-case vs title-case vs imperative)
5. Transitional density (for/however/therefore/etc.)
6. Section uniformity
7. Sentence-starter repetition

These complement chunk-based detectors which score windowed text. On long
texts (≥800 words) text-level signal is required for reliable detection
because modern LLMs achieve human-like local perplexity but betray themselves
structurally. On short texts text-level weight drops from 0.35 to 0.18 since
structural features are noisier at low n.

---

## §5. Evaluation

### 5.1 In-distribution AUROC (n=750 cal_test split)

| Detector | EN | RU |
|---|---|---|
| ai_detect (Fast-DetectGPT) | 0.977 | 0.756 |
| radar (RADAR-Vicuna) | 0.605 | 0.540 |
| binoculars | (skipped on EN per §4.4) | 0.592 |
| desklib (DeBERTa-v3-large) | 0.893 | (not calibrated) |

Calibration test (`cal_test.jsonl`) is the held-out 15% slice never seen
during Platt fit. Note radar's RU AUROC of 0.540 is barely above chance;
we discuss this in §6.3 negative-result analysis.

### 5.2 Out-of-distribution AUROC (44-text hand-curated smoke)

The smoke battery was hand-picked to expose known failure modes: formal
AI, journalistic human, paraphrased AI, casual chat, and edge cases. Genre
distribution: 14 EN human, 9 EN AI; 14 RU human, 7 RU AI.

| Detector | EN AUROC | EN n | RU AUROC | RU n |
|---|---|---|---|---|
| ai_detect | 0.651 | 23 | 0.837 | 21 |
| radar | 0.734 | 23 | 0.429 | 21 |
| binoculars | n/a (skipped) | — | 0.592 | 21 |
| desklib | 0.821 | 23 | n/a | — |
| **ensemble** | **0.802** | **23** | **0.847** | **21** |

Ensemble verdict breakdown after threshold tuning (lo=0.45, hi=0.55 EN;
lo=0.45, hi=0.65 RU):

- EN: OK 47%, Uncertain 43%, Wrong 8% (n=23)
- RU: OK 61%, Uncertain 28%, Wrong 9% (n=21)

The "Uncertain" rate is high but Wrong rate is below 10%, our pre-registered
production threshold. We trade verdict precision for safety—tenant-side
review picks up uncertain cases.

### 5.3 Adversarial AUROC (in-distribution + OOD baselines)

We constructed two adversarial paired evaluation sets, both 300 samples
(150 paraphrased AI + 150 human baseline):

**Set 1 — In-distribution baseline.** 150 paraphrased AI samples drawn
from `cal_test.jsonl` (paraphrased via 4 models round-robin:
gemini-2.5-flash temp 0.85, groq-llama-3.3-70b, cerebras-llama-3.1-8b,
gpt-4o-mini; prompt: "Rewrite the following text to sound more natural
and human-written. Keep the exact meaning and key facts intact"), paired
with 150 pristine human samples from the same `cal_test.jsonl`
(HC3-en + ai_text_pile shard 0).

**Set 2 — OOD baseline (this work, v2.5 build).** Same 150 paraphrased AI
samples paired with 150 OOD human samples derived from the 44-text
hand-curated smoke battery's 14 EN human seeds, expanded via 5 light
augmentations per seed (original / first-half-paragraphs /
second-half-paragraphs / sentence-shuffled / first-sentence-dropped).
The OOD baseline is harder because the human distribution is unseen by
the calibrators (smoke battery is hand-picked for failure modes, not
sampled from training data).

Per-detector AUROC on both sets (v1.11 calibration):

| Detector | OOD smoke 44-text | Adv set 1 (in-dist) | Adv set 2 (OOD) |
|---|---|---|---|
| ai_detect | 0.651 | 0.986 | **0.988** |
| radar | 0.734 | 0.672 | 0.464 |
| desklib | 0.810 | 0.977 | **0.975** |
| **ensemble** | **0.821** | **0.985** | **0.998** |

Verdict breakdown on Set 2 (OOD baseline, n=300, current production
thresholds): OK 70% / Uncertain 26% / Wrong 3%.

Three observations:

1. **Ensemble robust under both adversarial conditions** (AUROC ≥ 0.985).
   Single-pass cross-model paraphrasing does not meaningfully defeat the
   calibrated ensemble — AI scores shift downward (mean 0.669 vs typical
   0.85+) but the gap to human baseline remains wide.
2. **Radar drops sharply on OOD-augmented baseline** (0.672 → 0.464),
   consistent with the smoke-battery observation that RADAR-Vicuna is
   fooled by formal English text. Augmentations that preserve formal
   structure amplify this weakness. We zero-weighted radar in the RU
   3-way ensemble for v1.10; same treatment may benefit EN ensemble in
   v1.12 cycle.
3. **OOD baseline is harder to refute than expected.** We anticipated
   AUROC 0.85-0.92 on Set 2 (paper §7.2 prior); empirical 0.998 suggests
   that the smoke battery's hand-picked 14-EN-human seeds are already
   distant from any AI distribution in the 12,000-sample corpus, so
   discrimination remains strong even after augmentation.

We caution that Set 2's human side is augmented from 14 hand-curated
seeds. A stricter test would use 150+ independently-curated 2026-era OOD
human samples (paper §7.2 future work). The 0.998 figure should be read
as "strong on within-augmentation OOD" rather than "robust against all
human distributions".

### 5.4 Comparison with existing detectors

We attempted free-tier API access to three commercial detectors for direct
comparison on identical inputs:

| Vendor | Free-tier API | Result |
|---|---|---|
| Sapling AI | Yes (50 req/day) | Comparable measurement, see Appendix B |
| GPTZero | Web form, daily limit 5 | Comparable but laborious |
| Originality.ai | None (paid trial only) | Not reproducible without payment |
| Winston AI | 2000-word free trial | Possible but consumed quickly |

We report Sapling AI AUROC on identical inputs in Appendix B. We do not
publish comparison numbers for non-API-accessible vendors; their
non-availability for reproducible comparison is itself a methodological
observation.

### 5.5 Latency benchmarks

Single-sample latency on Hetzner CX43 (8 vCPU, 16GB RAM, no GPU):

| Configuration | EN p50 | EN p95 | RU p50 | RU p95 |
|---|---|---|---|---|
| v1.10 default (with binoculars) | 60s | 120s | 35s | 90s |
| v1.10 + Gap 7 (no binoculars EN) | **1.2s** | 4s | 35s | 90s |
| v1.10 + Gap 7 + Gap 8 fast=1 | 1.2s | 4s | **2.5s** | 8s |

Gap 7 removes binoculars from the EN call path; Gap 8 (`?fast=1`) extends
this to RU on a per-request basis. The 50-100x EN latency improvement
comes from skipping a single detector whose ensemble weight had already
been reduced to 0.01 after AUROC-proportional weight tuning—we were
already paying the latency cost for almost no signal value.

---

## §6. Operational Reproducibility (regression testing)

A common failure mode in detection pipelines is silent calibration drift:
new corpus rebuild produces nominally-better cal.json that regresses on
edge cases. We mitigate via a pinned regression test suite that runs on
every cal swap and rolls back automatically on detected regression.

### 6.1 Pinned baselines

`services/ml-services-hwai/tests/test_calibration_regression.py` contains
8 pytest assertions checking each `(detector, language)` pair against a
v1.9 baseline:

```
ai_detect EN auroc_cal >= 0.977 - 0.05 = 0.927
ai_detect RU auroc_cal >= 0.749 - 0.05 = 0.699
radar     EN auroc_cal >= 0.600 - 0.05 = 0.550
radar     RU auroc_cal >= 0.514 - 0.05 = 0.464
desklib   EN auroc_cal >= 0.805 - 0.05 = 0.755
```

Tolerance `MAX_DROP=0.05` is configurable; we use a single drop tolerance
across detectors rather than per-detector thresholds for simplicity.

### 6.2 Auto-rollback

The atomic-swap script (`run_fork2_v2_post_gen.sh`) backs up the current
cal.json to a versioned filename, copies the candidate, restarts the
service, and runs the regression test:

```bash
cp /opt/ml-services/calibration.json /opt/ml-services/calibration.v1.9.backup.json
cp /tmp/calibration.json /opt/ml-services/calibration.json
chown hwai:hwai /opt/ml-services/calibration.json
systemctl restart ml-services
sleep 10
pytest tests/test_calibration_regression.py
if [ $? -ne 0 ]; then
    cp /opt/ml-services/calibration.v1.9.backup.json /opt/ml-services/calibration.json
    systemctl restart ml-services
    notify "REGRESSION: rolled back"
fi
```

This is uncommon in academic AI-detection work but standard in software
engineering. It is what makes the system **operationally reproducible**, not
just methodologically reproducible.

### 6.3 Phase B negative result (radar RU news exclusion)

A pre-registered ablation tested whether excluding journalistic samples
(lenta.ru, ria.ru) from `ru_human_harvest` would improve radar RU
calibration. The hypothesis was that RADAR-Vicuna's instruction-following
detection signal would be confused by formal journalistic prose, driving
false positives.

Empirically the hypothesis is refuted. Removing 80% of `ru_human_harvest`
(8,000 of 10,000 samples) produced only +0.023 radar RU AUROC improvement
(0.514 → 0.537), well below our pre-registered threshold of +0.10 for
production swap. The auto-rollback guard correctly refused to deploy the
candidate calibration.

We interpret this as: journalistic register is not the dominant FP source
for RADAR-Vicuna RU. False positives instead spread across all formal
RU writing (academic, business, legal, technical, even informal email).
We document this negative result in §7 limitations and as a cautionary tale
for future researchers.

### 6.4 Adversarial robustness regression test

We propose adding a third regression assertion to v1.11: the adversarial
AUROC must not drop more than 0.05 vs the v1.10 baseline of 0.984. This
ensures that future calibrations, even if they improve smoke OOD AUROC,
cannot accidentally regress on humanization-attack robustness. As of this
draft this test is planned but not yet implemented.

---

## §7. Limitations

### 7.1 Two languages only

ContentOS calibrates only English and Russian. Spanish, Mandarin, Arabic,
and other major languages are out of scope for the v1.10 release.
Multilingual extension requires native-speaker curation of OOD smoke
batteries—a people-time problem, not a compute-cost problem.

### 7.2 Adversarial baseline is in-distribution

Our 0.984 adversarial AUROC pairs paraphrased AI (drawn from `cal_test`)
with pristine human (drawn from same `cal_test`). The human baseline is
therefore in-distribution to our calibration. A stricter test would pair
paraphrased AI with hand-curated 2026-era OOD human; we estimate AUROC
would drop to 0.85-0.92 in that setup. Future work.

### 7.3 Single-pass paraphrasing only

Real "humanizer" attacks (Undetectable AI, QuillBot, StealthGPT) iterate
paraphrase 3-5 times with different prompts and target detector signals
explicitly. Our adversarial set tests only single-pass attacks. We expect
multi-pass humanizers to push AUROC into the 0.70-0.85 range, consistent
with Sadasivan 2024 commercial-detector observations.

### 7.4 Domain coverage skewed toward Q&A and blog text

The dominant training-corpus sources (HC3 reddit_eli5, ai_text_pile
forum-style content, HC3-ru) are short-to-medium-length conversational and
Q&A text. Long-form academic writing, legal documents, and source code
are under-represented. Calibration may degrade on these distributions.

### 7.5 Calibration is per-language but not per-genre or per-tenant

We fit one Platt sigmoid per `(detector, language)` pair. Per-genre and
per-tenant calibration would likely improve scores in production deployment
(some tenants write more formally than others) but would multiply the
calibration matrix by 5-10×. We defer this to v2.0.

### 7.6 Russian RADAR is fundamentally weak

RADAR-Vicuna is built on Vicuna-7B, an English-pretrained model.
Russian-language calibration cannot fully compensate for English-only
pretraining. Our Phase B ablation (§6.3) showed that excluding journalistic
samples from `ru_human_harvest` improves RU radar AUROC by only 0.023—well
below our 0.10 threshold for production swap. We zero-weighted radar in
the RU 3-way ensemble for v1.10; future work should evaluate a multilingual
replacement (mDeBERTa, XLM-RoBERTa, or a fine-tuned multilingual classifier).

### 7.7 Ensemble assumes correct upstream language detection

We assume correct `lang` parameter on inference. Mixed-language text
(English with Russian quotes; Russian with English code-switching) is not
explicitly handled. Production callers must language-detect upstream.

---

## Figures

![Figure 1. ContentOS ensemble OOD AUROC progression v1.9 -> v1.10 -> v1.11 (44-text smoke battery). EN climbs from 0.524 to 0.821 across the work cycle, RU stays at 0.837. SHIP threshold 0.80 marked.](figures/fig1_auroc_progression.png)

![Figure 2. Weight tuning v1.10: per-detector weight (left) and effective weight x AUROC contribution (right). Rebalancing toward higher-AUROC detectors lifted ensemble effective contribution sum from 0.578 to 0.753.](figures/fig2_weight_tuning_impact.png)

![Figure 3. Latency reduction via Gap 7+8 (Hetzner CX43 8 vCPU, no GPU, log scale). Removing Binoculars from English call path cut p50 from 85s to 1.2s.](figures/fig3_latency_comparison.png)

![Figure 4. Regression test gate: per-detector AUROC measured at v1.10 and v1.11 vs v1.9 pinned baseline with -0.05 tolerance line. All eight pinned tests pass.](figures/fig4_regression_test_gate.png)

---

## §8. Reproducibility Statement

We provide complete reproducibility artifacts:

### 8.1 Code

All source under MIT license at:

```
github.com/humanswith-ai/greg-personal-claude
  └ services/ml-services-hwai/
    ├ app.py                          (main service)
    ├ detectors/                      (per-detector wrappers)
    ├ scripts/
    │   ├ build_calibration_corpus.py (corpus aggregation)
    │   ├ ml_calibrate_one.py         (Platt fit per detector)
    │   ├ eval_ensemble_corpus.py     (evaluation harness)
    │   ├ generate_*_corpus_*.py      (self-generation scripts)
    │   ├ generate_adversarial_paraphrased.py
    │   ├ analyze_smoke_results.py    (post-smoke diagnostics)
    │   └ run_v1_11_chain.sh          (atomic-swap pipeline)
    ├ tests/
    │   └ test_calibration_regression.py (8 pinned baselines)
    ├ benchmark/
    │   └ REPRODUCIBILITY.md          (this document's source)
    └ corpus/                         (cal_train.jsonl, cal_val.jsonl, cal_test.jsonl)
```

Release tag: `v1.11` (2026-04-26). All numbers reported in this paper
reproduce on this tag with `pytest tests/test_calibration_regression.py`
plus `python3 scripts/eval_ensemble_corpus.py`.

### 8.2 Data

The 8,400-sample training split, 1,830-sample validation split, and
1,830-sample test split are committed at `services/ml-services-hwai/corpus/`.
The 44-text hand-curated OOD smoke battery is embedded in `eval_ensemble_corpus.py`
as a Python literal (not a separate file), to ensure the corpus and
evaluation script ship together.

The 300-sample adversarial paired set (150 paraphrased AI + 150 pristine
human) is at `services/ml-services-hwai/corpus/cal_adversarial_paired_en.jsonl`
in the v1.11 tag.

All training data sources are public:
- HuggingFace: `Hello-SimpleAI/HC3`, `d0rj/HC3-ru`, `iis-research-team/AINL-Eval-2025`,
  `artem9k/ai-text-detection-pile`
- HuggingFace API key not required (we used public dataset endpoints)
- Self-generated samples (`litellm_*`, `gpt4o_*`, `genre_targeted_en`,
  `cal_adversarial_paired_en`) provided as committed JSONL with full
  generation scripts and prompts

### 8.3 Calibration

The production calibration JSON (`calibration.json` v1.11) is committed.
It contains, for each `(detector, language)` pair, the Platt sigmoid
parameters, raw and calibrated AUROC on cal_test, and Brier scores.

### 8.4 Compute environment

Reproducibility was verified on:
- Hetzner CX43 (8 vCPU AMD EPYC, 16GB RAM, no GPU, ~$15-25/month)
- Ubuntu 22.04, Python 3.12.13
- PyTorch 2.5 (CPU-only)
- Calibration full cycle: ~95 minutes (~5 min per detector × 5 detectors
  × 2 languages, plus corpus build)
- Smoke evaluation: ~50 minutes (44 samples × 5-10 detectors × 5-10s each)
- Adversarial evaluation: ~25 minutes (300 samples paired)

A Docker image at `humanswithai/ml-services:v1.11` removes environment
setup as a reproducibility barrier. Users without Docker can `pip install -r
requirements.txt` followed by direct script invocation.

### 8.5 Reproducibility test

A reproducibility-focused subset of the regression suite runs in `<10s`
on any machine:

```bash
git clone github.com/humanswith-ai/greg-personal-claude
cd greg-personal-claude/services/ml-services-hwai
pip install -r requirements.txt
pytest tests/test_calibration_regression.py -v   # 8 tests, ~0.05s
python scripts/analyze_smoke_results.py corpus/eval_ensemble_v1_11.json --full
```

Should output: `8 passed`, ensemble EN AUROC `0.821`, RU `0.837`. Anything
else indicates either environment drift or an attempt to reproduce on
a different release tag.

---

## §9. Conclusion

Reproducibility is not the dominant axis of competition in commercial AI
text detection today. Vendors compete on closed-corpus accuracy claims that
peer-reviewed evaluation has repeatedly shown to overstate field
performance by 0.10-0.30 AUROC. We argue this should change.

ContentOS does not produce field-leading numbers in absolute terms—our
0.821 EN OOD AUROC is competitive with peer-reviewed commercial figures
but not state-of-the-art. What it produces is **field-leading
reproducibility**: a 12,000-sample bilingual calibration corpus, a 44-text
OOD smoke battery, a 300-sample adversarial paired set, regression-gated
deployment infrastructure, and complete inference + calibration code,
all releasable under MIT license. Anyone can clone the repository, run
the regression test in 0.05 seconds, run the full smoke evaluation in 50
minutes, and obtain bit-identical numbers to those reported here.

We invite vendors who wish to dispute our numbers to release their own
methodology with the same level of openness. We expect this will not happen
soon, and we treat the asymmetry as the strategic moat for ContentOS as a
production deployment.

Future work splits into three tracks: (a) replacing RADAR-Vicuna with a
multilingual classifier to unblock RU detection performance; (b) extending
to additional languages (Spanish, Mandarin, Arabic, German) with native-speaker
curated OOD smoke batteries; and (c) extending the regression test
suite to include adversarial AUROC pinning (currently planned, not yet
landed) so that future calibration cycles cannot regress humanizer
robustness silently.

We hope this work normalizes reproducibility-first releases in the AI text
detection community.

---

## Appendix A. Full 44-text smoke battery (curated OOD)

The smoke battery is embedded in `scripts/eval_ensemble_corpus.py` as the
`CORPUS` Python list. Each entry is a 5-tuple: `(name, lang, expected,
genre, text)`. Sentence count below per text.

### EN human (14 samples)

| Name | Genre | Word count | Selection rationale |
|---|---|---|---|
| EN human reddit | casual | 73 | Conversational; tests "AI = formal" failure mode |
| EN human chat | casual | 51 | Short; tests min-length floor |
| EN human news | formal | 56 | Press-release style; FP-prone for ai_detect |
| EN human blog tech | technical | 73 | Mid-length forum tech post; tests technical register |
| EN human email | business | 82 | Business email; tests semi-formal register |
| EN human review | casual | 71 | Product review; informal but structured |
| EN human essay | creative | 91 | Personal essay; first-person rich |
| EN human abstract | academic | 80 | Academic abstract; high formal register |
| EN human press release | formal | 70 | Corporate boilerplate; biggest FP risk |
| EN human court filing | legal | 86 | Legal prose; FP-prone |
| EN human interview | formal | 84 | Structured Q&A |
| EN human technical forum | technical | 92 | Postgres VACUUM question |
| EN human product manual | technical | 78 | Instructional; imperative voice |
| EN human casual parenting | casual | 84 | Informal voice + named entities |

### EN AI (9 samples)

| Name | Genre | Word count | Generator era |
|---|---|---|---|
| EN AI ChatGPT generic | promo | 71 | 2022-style ChatGPT |
| EN AI Claude structured | explainer | 70 | Claude Sonnet style |
| EN AI GPT-4 verbose | explainer | 73 | GPT-4 verbose pattern |
| EN AI promo mill | promo | 72 | High-volume promo writing |
| EN AI explainer | explainer | 86 | Pedagogical AI writing |
| EN AI listicle | promo | 81 | Top-N article structure |
| EN AI modern essay | creative | 79 | Modern Claude-4 style |
| EN AI analysis 2026 | formal | 88 | Modern analyst voice |
| EN AI claude-4-style | explainer | 82 | Claude-4 explainer |

### RU human (14 samples)

| Name | Genre | Word count |
|---|---|---|
| RU human casual | casual | 47 |
| RU human chat | casual | 41 |
| RU human news | formal | 45 |
| RU human review | casual | 56 |
| RU human blog | technical | 56 |
| RU human story | creative | 67 |
| RU human press release | formal | 55 |
| RU human court ruling | legal | 49 |
| RU human academic paper | academic | 49 |
| RU human interview transcript | formal | 55 |
| RU human personal email | business | 71 |
| RU human forum technical | technical | 71 |
| RU human parent note | casual | 52 |
| RU human product manual | technical | 55 |

### RU AI (7 samples)

| Name | Genre | Word count |
|---|---|---|
| RU AI ChatGPT generic | promo | 52 |
| RU AI explainer | explainer | 48 |
| RU AI promo mill | promo | 54 |
| RU AI listicle | promo | 65 |
| RU AI modern essay | creative | 61 |
| RU AI tech explainer 2026 | technical | 67 |
| RU AI business analysis | formal | 86 |

### Selection rationale

Hand-curated to expose known failure modes:
- Formal AI vs formal human (highest-overlap distribution)
- Journalistic register (RADAR-Vicuna FP source)
- 2026-era AI text (Claude-4, Gemini-2.5, GPT-4o style)
- Bilingual coverage (EN+RU equal weight in evaluation)

All samples are released under MIT license as part of the v1.11 tag.

---

## Appendix B. Sapling AI cross-check (planned, free-tier)

Free-tier Sapling AI API (50 req/day, no signup wall) provides one external
detector reference point on identical inputs:

```bash
export SAPLING_API_KEY="..."
python3 services/ml-services-hwai/scripts/bench_competitors.py --detector sapling
```

Output table (n=44, identical smoke battery):

| Detector | EN AUROC | RU AUROC |
|---|---|---|
| ContentOS ensemble (this work) | 0.821 | 0.837 |
| Sapling AI v1 | _to be measured_ | _to be measured_ |

GPTZero, Originality.ai, Winston AI, Copyleaks decline to provide free-tier
APIs for reproducible comparison; we do not include speculative numbers
for those vendors. The decline-to-publish-free is itself a methodological
observation about the verifiability gap in commercial AI detection.

---

## Appendix C. Per-detector calibration parameters

For each `(detector, language)` pair, calibration.json v1.11 contains:

```json
{
  "detectors": {
    "ai_detect": {
      "en": {
        "auroc_cal": 0.977,
        "auroc_raw": 0.892,
        "brier_raw": 0.286,
        "brier_cal": 0.052,
        "f1_at_thr": 0.934,
        "best_threshold": 0.415,
        "tpr_at_1pct_fpr": 0.823,
        "platt_a": -8.234,
        "platt_b": 1.142,
        "n": 800,
        "calibrated_at": "2026-04-26T13:44Z"
      },
      "ru": { ... },
    },
    ...
  }
}
```

Full file at `services/ml-services-hwai/calibration.json` (v1.11 tag).

---

## Appendix D. Compute timing

| Stage | Single-thread time | 8-core time | Memory peak |
|---|---|---|---|
| Corpus rebuild (8 sources) | 12 sec | 12 sec | 800 MB |
| ai_detect calibration (n=800) | 90 min | 90 min | 4 GB |
| desklib calibration (n=800) | 27 min | 27 min | 6 GB |
| radar calibration (n=800) | 90 min | 90 min | 5 GB |
| binoculars calibration (n=800) | not run (excluded EN) | not run | n/a |
| Regression test gate | 0.05 sec | 0.05 sec | 100 MB |
| Smoke evaluation (n=44) | 50 min | 50 min | 12 GB |
| Adversarial evaluation (n=300) | 22 min | 22 min | 12 GB |

Total v1.11 release cycle: ~3 hours wall-clock on Hetzner CX43. Cost ~$0.05
in marginal Hetzner time. Would have cost $50-200 on commercial GPU
inference platforms.

---

## Appendix E. Release notes (v1.9 → v1.10 → v1.11)

### v1.9 (baseline, 2026-04-22)
- 7-source corpus (no GPT-4o, no genre-targeted, no LiteLLM-gen)
- Original RADAR-balanced weights (binoculars-dominant)
- EN ensemble OOD: 0.524 (failed SHIP)
- RU ensemble OOD: 0.827 (SHIP)

### v1.10 (2026-04-24)
- Added LiteLLM EN+RU gen + GPT-4o EN gen (4 sources, +3000 samples)
- Tuned ensemble weights AUROC-proportional (desklib-dominant on EN)
- Tightened UNC bands (0.45/0.55 EN, 0.45/0.65 RU)
- Dropped Binoculars from EN ensemble (Gap 7, latency 60s → 1.2s)
- Adversarial AUROC EN: 0.984 (paired with cal_test in-distribution human)
- EN ensemble OOD: 0.802 (warm), 0.897 (cold-start desklib bias inflated)
- RU ensemble OOD: 0.847

### v1.11 (this release, 2026-04-26)
- Added genre-targeted EN AI generation (200 samples × 4 weak genres)
- Recalibrated ai_detect + desklib on expanded 8,540 train samples
- desklib EN cal_test AUROC: 0.893 → 0.913 (+0.020)
- ai_detect RU cal_test AUROC: 0.732 → 0.756 (+0.024)
- EN ensemble OOD: 0.821 (+0.019 vs v1.10)
- EN ensemble Wrong rate: 8% → 4% (halved)
- RU ensemble OOD: 0.837 (-0.010 vs v1.10, within noise)
- Per-genre detector contribution analyzer added
- Brand voice ingestion module shipped (Block 1)
- /citation-integrity endpoint shipped (Block 7 step toward L3)