§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:
+-
+
- 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).
- 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). +out-of-distribution smoke battery selected for known failure modes +(formal AI, journalistic human, paraphrased AI).
- 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. -
- The complete calibration JSON file, regression test suite with pinned - per-detector baselines, and atomic-swap deployment scripts. +paraphrasing (gemini-2.5-flash, groq-llama-3.3-70b, +cerebras-llama-3.1-8b, gpt-4o-mini), enabling reproducible adversarial +AUROC measurement. +
- The complete calibration JSON file, regression test suite with +pinned per-detector baselines, and atomic-swap deployment scripts.
- All training, evaluation, and threshold-tuning scripts.
Our headline numbers, reproducible end-to-end on Hetzner CX43-class hardware -($25/month) within 90 minutes:
+Our headline numbers, reproducible end-to-end on Hetzner CX43-class +hardware ($25/month) within 90 minutes:
-
-
- English ensemble OOD AUROC: 0.802 (44-text smoke, post-Gap-7-tuning) -
- Russian ensemble OOD AUROC: 0.847 -
- English ensemble adversarial AUROC: 0.984 on 300-sample paraphrase-paired set -
- English ensemble p50 latency: 1.2 seconds (8-core CPU, no GPU) +
- 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: 0.984 on +300-sample paraphrase-paired set +
- 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.
+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.
+§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
+§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 | @@ -165,7 +416,8 @@ where possible.EN | 1,411 | 2022-23 | -ChatGPT vs human Q&A across 5 domains (reddit_eli5, finance, medicine, open_qa, wiki_csai) | +ChatGPT vs human Q&A across 5 domains (reddit_eli5, finance, +medicine, open_qa, wiki_csai) |
|---|---|---|---|---|---|
| d0rj/HC3-ru | @@ -179,7 +431,8 @@ where possible.RU | 1,381 | 2024-25 | -Multi-model RU detection task; AI side covers GPT-4, Gemma, Llama 3 | +Multi-model RU detection task; AI side covers GPT-4, Gemma, Llama +3 |
| artem9k/ai-text-detection-pile (shards 0+6) | @@ -193,14 +446,16 @@ where possible.RU | 696 | 2010-22 | -Pre-LLM journalism (lenta.ru, ria.ru) + curation-corpus + editorial RU | +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 | +Internal generation: gemini-2.5-flash + groq-llama 3.3 70B at temp +0.7-0.9 |
| LiteLLM RU gen | @@ -225,54 +480,70 @@ where possible.
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:
+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
+ ('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
+ 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. +
- 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
+
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.
+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
+§4. Detection Pipeline
+4.1 Detectors
The ensemble combines four independently published detectors plus a text-level structural feature head:
| Detector | @@ -320,62 +591,65 @@ text-level structural feature head:
|---|
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:
+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:
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:
+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:
+ = "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:
-
+RU: thr_low = 0.45, thr_high = 0.65
+
- Sentence-length burstiness (coefficient of variation)
- Paragraph-length uniformity
- N-gram repetition ratio @@ -384,14 +658,15 @@ on whole-text statistics rather than chunk windows:
- Section uniformity
- Sentence-starter repetition
- EN: OK 47%, Uncertain 43%, Wrong 8% (n=23)
- RU: OK 61%, Uncertain 28%, Wrong 9% (n=21)
- 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. -
- 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. -
- 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. +
- 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. +
- 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. +
- 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.
- 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)
- Added LiteLLM EN+RU gen + GPT-4o EN gen (4 sources, +3000 samples) -
- Tuned ensemble weights AUROC-proportional (desklib-dominant on EN) +
- 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) +
- 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
- Added genre-targeted EN AI generation (200 samples × 4 weak genres) -
- Recalibrated ai_detect + desklib on expanded 8,540 train samples +
- 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)
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:
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.
+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)
+§5. Evaluation
+5.1 In-distribution +AUROC (n=750 cal_test split)
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.
+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.
Ensemble verdict breakdown after threshold tuning (lo=0.45, hi=0.55 EN; -lo=0.45, hi=0.65 RU):
+Ensemble verdict breakdown after threshold tuning (lo=0.45, hi=0.55 +EN; lo=0.45, hi=0.65 RU):
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).
+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):
| Vendor | @@ -601,11 +887,11 @@ comparison on identical inputs:
|---|
We report Sapling AI AUROC on identical inputs in Appendix B. We do not -publish comparison numbers for non-API-accessible vendors; their +
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
+5.5 Latency benchmarks
Single-sample latency on Hetzner CX43 (8 vCPU, 16GB RAM, no GPU):
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.
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:
§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:
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.
+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:
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"
+fiThis 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
+§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
+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.
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
-
+
+
+
+
+
+
+
+-
§8. Reproducibility Statement
+§8. Reproducibility Statement
We provide complete reproducibility artifacts:
-8.1 Code
+8.1 Code
All source under MIT license at:
github.com/humanswith-ai/greg-personal-claude
└ services/ml-services-hwai/
@@ -772,91 +1101,105 @@ explicitly handled. Production callers must language-detect upstream.
├ 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
+ │ └ 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
+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
+
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:
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.
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:
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 --fullShould 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.
+§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.
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 | @@ -870,7 +1213,7 @@ genre, text). Sentence count below per text.EN human reddit | casual | 73 | -Conversational; tests "AI = formal" failure mode | +Conversational; tests “AI = formal” failure mode |
|---|---|---|---|---|---|
| EN human chat | @@ -1149,19 +1492,21 @@ genre, text). Sentence count below per text.
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.
+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:
-export SAPLING_API_KEY="..."
-python3 services/ml-services-hwai/scripts/bench_competitors.py --detector sapling
-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:
+export SAPLING_API_KEY="..."
+python3 services/ml-services-hwai/scripts/bench_competitors.py --detector saplingOutput table (n=44, identical smoke battery):
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.
+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:
{
- "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 C. +Per-detector calibration parameters
+For each (detector, language) pair, calibration.json
+v1.11 contains:
{
+ "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
+Appendix D. Compute timing
| Stage | @@ -1276,32 +1631,40 @@ observation about the verifiability gap in commercial AI detection.
|---|
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.
+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)
+Appendix E. Release +notes (v1.9 → v1.10 → v1.11)
+v1.9 (baseline, 2026-04-22)
v1.10 (2026-04-24)
+v1.10 (2026-04-24)
-
-
v1.11 (this release, 2026-04-26)
+v1.11 (this release, +2026-04-26)
-
-