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<header id="title-block-header">
<h1 class="title">DraftMark: A Research Roadmap for Local AI Text
Detection</h1>
<p class="subtitle">Verified competitors, low-false-positive evaluation,
H100 training, and device deployment</p>
<p class="author">Prepared for Saksorn (Harry) Ruangtanusak</p>
<p class="date">9 November 2026</p>
</header>
<nav id="TOC" role="doc-toc">
<ul>
<li><a href="#executive-recommendation" id="toc-executive-recommendation"><span class="toc-section-number">1</span> Executive recommendation</a>
<ul>
<li><a href="#decisions-at-a-glance" id="toc-decisions-at-a-glance"><span class="toc-section-number">1.1</span> Decisions at a glance</a></li>
</ul></li>
<li><a href="#scope-and-evidence-protocol" id="toc-scope-and-evidence-protocol"><span class="toc-section-number">2</span> Scope and evidence protocol</a></li>
<li><a href="#what-draftmark-already-achieves" id="toc-what-draftmark-already-achieves"><span class="toc-section-number">3</span> What DraftMark already achieves</a>
<ul>
<li><a href="#model-and-deployment-audit" id="toc-model-and-deployment-audit"><span class="toc-section-number">3.1</span> Model and deployment
audit</a></li>
<li><a href="#published-holdout-meaningful-progress-with-an-explicit-tradeoff" id="toc-published-holdout-meaningful-progress-with-an-explicit-tradeoff"><span class="toc-section-number">3.2</span> Published holdout: meaningful
progress with an explicit tradeoff</a></li>
</ul></li>
<li><a href="#benchmarks-and-exact-scores-to-challenge" id="toc-benchmarks-and-exact-scores-to-challenge"><span class="toc-section-number">4</span> Benchmarks and exact scores to
challenge</a>
<ul>
<li><a href="#official-raid-the-public-english-benchmark-gate" id="toc-official-raid-the-public-english-benchmark-gate"><span class="toc-section-number">4.1</span> Official RAID: the public English
benchmark gate</a></li>
<li><a href="#mage-generation-and-paraphrase-transfer" id="toc-mage-generation-and-paraphrase-transfer"><span class="toc-section-number">4.2</span> MAGE: generation and paraphrase
transfer</a></li>
<li><a href="#detectrl-x-multilingual-and-editing-sensitive-evaluation" id="toc-detectrl-x-multilingual-and-editing-sensitive-evaluation"><span class="toc-section-number">4.3</span> DetectRL-X: multilingual and
editing-sensitive evaluation</a></li>
<li><a href="#wattle-a-harder-test-of-web-rewriting" id="toc-wattle-a-harder-test-of-web-rewriting"><span class="toc-section-number">4.4</span> WATTLE: a harder test of web
rewriting</a></li>
<li><a href="#shared-tasks-and-supporting-benchmarks" id="toc-shared-tasks-and-supporting-benchmarks"><span class="toc-section-number">4.5</span> Shared tasks and supporting
benchmarks</a></li>
</ul></li>
<li><a href="#model-strategy-small-students-relevant-larger-comparators" id="toc-model-strategy-small-students-relevant-larger-comparators"><span class="toc-section-number">5</span> Model strategy: small students,
relevant larger comparators</a>
<ul>
<li><a href="#recommended-model-ladder" id="toc-recommended-model-ladder"><span class="toc-section-number">5.1</span> Recommended model ladder</a></li>
<li><a href="#how-to-compare-larger-models-fairly" id="toc-how-to-compare-larger-models-fairly"><span class="toc-section-number">5.2</span> How to compare larger models
fairly</a></li>
</ul></li>
<li><a href="#improvements-most-likely-to-matter" id="toc-improvements-most-likely-to-matter"><span class="toc-section-number">6</span> Improvements most likely to
matter</a>
<ul>
<li><a href="#prioritize-data-coverage-before-architecture-novelty" id="toc-prioritize-data-coverage-before-architecture-novelty"><span class="toc-section-number">6.1</span> Prioritize data coverage before
architecture novelty</a></li>
<li><a href="#treat-editing-provenance-explicitly" id="toc-treat-editing-provenance-explicitly"><span class="toc-section-number">6.2</span> Treat editing provenance
explicitly</a></li>
<li><a href="#distillation-is-useful-engineering-not-sufficient-novelty" id="toc-distillation-is-useful-engineering-not-sufficient-novelty"><span class="toc-section-number">6.3</span> Distillation is useful
engineering, not sufficient novelty</a></li>
<li><a href="#research-candidate-stability-of-the-human-score-tail-after-export" id="toc-research-candidate-stability-of-the-human-score-tail-after-export"><span class="toc-section-number">6.4</span> Research candidate: stability of
the human-score tail after export</a></li>
</ul></li>
<li><a href="#evaluation-protocol-for-a-publishable-result" id="toc-evaluation-protocol-for-a-publishable-result"><span class="toc-section-number">7</span> Evaluation protocol for a
publishable result</a>
<ul>
<li><a href="#separate-four-roles-for-data" id="toc-separate-four-roles-for-data"><span class="toc-section-number">7.1</span> Separate four roles for
data</a></li>
<li><a href="#primary-metrics" id="toc-primary-metrics"><span class="toc-section-number">7.2</span> Primary metrics</a></li>
<li><a href="#calibration-and-statistical-uncertainty" id="toc-calibration-and-statistical-uncertainty"><span class="toc-section-number">7.3</span> Calibration and statistical
uncertainty</a></li>
<li><a href="#concrete-success-criteria-defined-before-the-next-test" id="toc-concrete-success-criteria-defined-before-the-next-test"><span class="toc-section-number">7.4</span> Concrete success criteria, defined
before the next test</a></li>
</ul></li>
<li><a href="#h100-training-plan" id="toc-h100-training-plan"><span class="toc-section-number">8</span> H100 training plan</a>
<ul>
<li><a href="#start-on-one-gpu" id="toc-start-on-one-gpu"><span class="toc-section-number">8.1</span> Start on one GPU</a></li>
<li><a href="#run-sequence" id="toc-run-sequence"><span class="toc-section-number">8.2</span> Run sequence</a></li>
<li><a href="#warm-start-reconstruction" id="toc-warm-start-reconstruction"><span class="toc-section-number">8.3</span> Warm-start reconstruction</a></li>
<li><a href="#estimate-time-from-a-pilot" id="toc-estimate-time-from-a-pilot"><span class="toc-section-number">8.4</span> Estimate time from a
pilot</a></li>
</ul></li>
<li><a href="#local-app-development" id="toc-local-app-development"><span class="toc-section-number">9</span>
Local app development</a>
<ul>
<li><a href="#build-on-the-existing-browser-product" id="toc-build-on-the-existing-browser-product"><span class="toc-section-number">9.1</span> Build on the existing browser
product</a></li>
<li><a href="#app-rollout-sequence" id="toc-app-rollout-sequence"><span class="toc-section-number">9.2</span> App rollout sequence</a></li>
</ul></li>
<li><a href="#a-focused-thai-english-research-extension" id="toc-a-focused-thai-english-research-extension"><span class="toc-section-number">10</span> A focused Thai-English research
extension</a></li>
<li><a href="#report-and-publication-strategy" id="toc-report-and-publication-strategy"><span class="toc-section-number">11</span> Report and publication strategy</a>
<ul>
<li><a href="#what-can-be-published-now" id="toc-what-can-be-published-now"><span class="toc-section-number">11.1</span> What can be published
now</a></li>
<li><a href="#what-would-justify-a-stronger-paper" id="toc-what-would-justify-a-stronger-paper"><span class="toc-section-number">11.2</span> What would justify a stronger
paper</a></li>
<li><a href="#suggested-paper-outline" id="toc-suggested-paper-outline"><span class="toc-section-number">11.3</span> Suggested paper outline</a></li>
</ul></li>
<li><a href="#prioritized-execution-plan" id="toc-prioritized-execution-plan"><span class="toc-section-number">12</span> Prioritized execution plan</a></li>
<li><a href="#references" id="toc-references"><span class="toc-section-number">13</span> References</a></li>
</ul>
</nav>
<h1 data-number="1" id="executive-recommendation"><span class="header-section-number">1</span> Executive recommendation</h1>
<p><strong>Keep DraftMark’s existing RoBERTa release as the control.
Build the next model around generalization to unseen rewrites, careful
treatment of AI-assisted human writing, and low false-positive rates
measured on the exact local artifact.</strong> A useful first comparison
is a 68M/150M encoder student against the existing 125M model and an
Ettin-1B teacher. Begin with English; add a dedicated Thai-English study
as a second research track, using a multilingual backbone and locally
collected evidence.</p>
<p>The most relevant compact competitor found in this review is GPTTrace
Ettin-150M v5e. Its official RAID submission reaches <strong>98.91% TPR
at 1% FPR</strong> and <strong>99.80% TPR at 5% FPR</strong>, including
attacks. The updated Ettin-1B v8s submission reaches
<strong>99.54%</strong> and <strong>99.93%</strong>, respectively. A
browser implementation, INT8 weights, and teacher distillation already
exist for the 150M competitor. Those features alone do not constitute a
new research contribution. <span class="citation" data-cites="raid150 raid1b gpttrace150">(RAID evaluation maintainers
2026a, 2026b; Nakas 2026a)</span></p>
<p>The more promising question is whether a small exported detector can
preserve useful recall on unfamiliar rewriting processes while keeping
human false flags low across populations and devices. This is both
practically meaningful and less saturated than an aggregate RAID score.
This report defines the comparisons and provides a training/evaluation
starter package; <strong>it does not report a new H100 training run or
new detector benchmark measurements</strong>.</p>
<h2 data-number="1.1" id="decisions-at-a-glance"><span class="header-section-number">1.1</span> Decisions at a glance</h2>
<table>
<colgroup>
<col style="width: 50%" />
<col style="width: 50%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Decision</th>
<th style="text-align: left;">Recommendation</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Immediate model</td>
<td style="text-align: left;">Continue the pinned DraftMark v3
checkpoint on one H100; compare last-four-layer training with full
fine-tuning.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Main compact challenger</td>
<td style="text-align: left;">Ettin-150M; add Ettin-68M to test the
quality-versus-size frontier.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">English teacher</td>
<td style="text-align: left;">Ettin-1B, including a frozen GPTTrace v8s
comparator; teacher quality must be measured on the target data.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Multilingual starting point</td>
<td style="text-align: left;">mDeBERTa-v3-base or XLM-R-base; do not
assume the English checkpoint supports Thai.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Main product metric</td>
<td style="text-align: left;">Recall at a threshold fixed on separate
calibration data, accompanied by observed test FPR and subgroup
confidence intervals.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Public benchmark gate</td>
<td style="text-align: left;">Official RAID protocol, plus MAGE
OOD/paraphrase, DetectRL-X, and WATTLE.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Credible contribution</td>
<td style="text-align: left;">Robust transfer and calibration after
quantization under a specified memory/latency budget.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Initial device budget</td>
<td style="text-align: left;">Preserve the existing &lt;160 MB
model-plus-tokenizer gate; separately measure peak RAM, latency, cold
start, and power.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">First app step</td>
<td style="text-align: left;">Extend the existing local browser app with
an auditable offline package and device benchmark suite.</td>
</tr>
</tbody>
</table>
<h1 data-number="2" id="scope-and-evidence-protocol"><span class="header-section-number">2</span> Scope and evidence protocol</h1>
<p>This is a targeted critical review, not a systematic claim of
exhaustive coverage. The search cutoff is <strong>9 November 2026,
Asia/Bangkok</strong>. The seed is
<code>saksornr/draftmark-roberta-int8</code>. The review followed its
base model, training notes, benchmark dependencies, and nearby detector
literature, then checked recent official benchmark submissions and
primary papers.</p>
<p>Search lanes included the seed/model provenance; references from
RAID, MAGE, DetectRL-X, MELD and WATTLE; later leaderboard updates;
multilingual and mixed-authorship benchmarks; larger
likelihood/representation detectors; and official runtime documentation.
Searches included combinations of AI-generated text detection, local,
browser, INT8, paraphrase, low false-positive rate, Thai, multilingual,
calibration, and H100. Author cards were treated as author reports, not
independent replications.</p>
<p>Four evidence classes are used throughout:</p>
<ol type="1">
<li><strong>Release evidence:</strong> DraftMark’s public manifests and
aggregate report. These were inspected, not independently rerun.</li>
<li><strong>Official benchmark evidence:</strong> RAID CI results at a
pinned repository revision and organizer leaderboards.</li>
<li><strong>Paper or author-harness evidence:</strong> exact reported
datasets, splits and metrics, with their limitations retained.</li>
<li><strong>Proposed experiment or target:</strong> an
engineering/research decision in this report, not an achieved
result.</li>
</ol>
<p>The DraftMark main revision inspected was
<code>068ccc7ba34f134526c9bbc72ac2033f00a40d45</code>. The RAID snapshot
was <code>ac4ce10fe8fe096b0c4a01396f1e437aaf35bd53</code>, dated 5
November 2026. The companion evidence files preserve exact scores,
source URLs and versions. Public artifacts do not include DraftMark’s
complete trainer, row-level evaluation predictions or full split
manifests, so an independent reproduction of its original run is not yet
possible from the release alone. <span class="citation" data-cites="draftmark draftmarkmetrics draftmarkhandoff raidrepo">(DraftMark
project 2026b, 2026c, 2026a; Dugan et al. 2026)</span></p>
<h1 data-number="3" id="what-draftmark-already-achieves"><span class="header-section-number">3</span> What DraftMark already
achieves</h1>
<h2 data-number="3.1" id="model-and-deployment-audit"><span class="header-section-number">3.1</span> Model and deployment audit</h2>
<p>DraftMark v3 is a fine-tuned <strong>RoBERTa-base detector,
approximately 125M total parameters</strong>, initialized from Oxidane’s
TMR checkpoint. It updates the last four encoder layers plus the
classifier: <strong>28,943,618 trainable parameters</strong>. The
published training run used Apple MPS, 6,339 training drafts, 761
calibration/development drafts and 1,120 holdout drafts. The deployed
model and tokenizer occupy <strong>131,110,132 bytes</strong>,
approximately 131.1 MB. <span class="citation" data-cites="draftmark draftmarkmetrics tmr">(DraftMark project 2026b,
2026c; Oxidane 2025)</span></p>
<p>The release is English-only. It accepts 150-2,000 words, with a
20,000-character cap. The worker scores sentence-aligned windows of up
to 512 tokens and aggregates AI scores by content-token count. The label
order is human, then AI. Its production flag threshold is approximately
<strong>0.9991466</strong>, with a lower limited-signal cutoff of 0.25.
These are product policies fitted for this artifact; 0.9991466 is not a
calibrated 99.9% probability of authorship. <span class="citation" data-cites="draftmark">(DraftMark project 2026b)</span></p>
<h2 data-number="3.2" id="published-holdout-meaningful-progress-with-an-explicit-tradeoff"><span class="header-section-number">3.2</span> Published holdout: meaningful
progress with an explicit tradeoff</h2>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Cohort</th>
<th style="text-align: right;">Previous DraftMark Ettin policy</th>
<th style="text-align: right;">Current DraftMark v3</th>
<th style="text-align: right;">Change</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Clean AI recall</td>
<td style="text-align: right;">215/300 = 71.67%</td>
<td style="text-align: right;">277/300 = 92.33%</td>
<td style="text-align: right;">+20.67 points</td>
</tr>
<tr class="even">
<td style="text-align: left;">Decoder-rewritten AI recall</td>
<td style="text-align: right;">18/150 = 12.00%</td>
<td style="text-align: right;">89/150 = 59.33%</td>
<td style="text-align: right;">+47.33 points</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Seq2seq-rewritten AI recall</td>
<td style="text-align: right;">132/150 = 88.00%</td>
<td style="text-align: right;">110/150 = 73.33%</td>
<td style="text-align: right;">-14.67 points</td>
</tr>
<tr class="even">
<td style="text-align: left;">Mean of the two rewrite recalls</td>
<td style="text-align: right;">50.00%</td>
<td style="text-align: right;">66.33%</td>
<td style="text-align: right;">+16.33 points</td>
</tr>
<tr class="odd">
<td style="text-align: left;">General-human false flags</td>
<td style="text-align: right;">10/300 = 3.33%</td>
<td style="text-align: right;">1/300 = 0.33%</td>
<td style="text-align: right;">Lower is better</td>
</tr>
<tr class="even">
<td style="text-align: left;">Learner-essay false flags</td>
<td style="text-align: right;">80/220 = 36.36%</td>
<td style="text-align: right;">0/220 = 0.00%</td>
<td style="text-align: right;">Lower is better</td>
</tr>
</tbody>
</table>
<p>These are the release’s own matched holdout results at each shipped
policy, not a comparison with today’s best public Ettin checkpoint on an
official leaderboard. Its paired 95% interval for mean rewrite
improvement is +10.7 to +22.0 percentage points. The improvement is
substantial on this sample, but the seq2seq regression is equally real.
<span class="citation" data-cites="draftmarkmetrics">(DraftMark project
2026c)</span></p>
<figure>
<img 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" style="width:95.0%" alt="Published DraftMark holdout results at shipped thresholds. Error bars show the release’s Wilson 95% intervals; the two models are evaluated on the same listed cohorts. These are historical release measurements, not new runs." />
<figcaption aria-hidden="true">Published DraftMark holdout results at
shipped thresholds. Error bars show the release’s Wilson 95% intervals;
the two models are evaluated on the same listed cohorts. These are
historical release measurements, not new runs.</figcaption>
</figure>
<h3 data-number="3.2.1" id="what-the-aggregate-numbers-do-not-establish"><span class="header-section-number">3.2.1</span> What the aggregate numbers do
not establish</h3>
<p>First, zero learner false flags on 220 essays is not proof of a zero
or sub-1% population error rate. The release’s two-sided Wilson upper
bound is <strong>1.72%</strong>. For 1/300 general-human false flags,
the interval is approximately <strong>0.059%-1.864%</strong>. A stronger
low-FPR claim needs more independent human source groups.</p>
<p>Second, the release separately reports decoder AUROC 98.51% and
seq2seq AUROC 98.27%, with diagnostic ROC recall around 74.50% and
76.51% at 1% FPR. These diagnostic values exclude out-of-input-limit
cases and use ROC operating points. They must not replace the actual
shipped recalls of 59.33% and 73.33%, whose denominators include the
full cohorts. High ranking quality and a conservative production
threshold can coexist.</p>
<p>Third, the latency result is <strong>1.262 seconds p95</strong> for a
warm 300-word scan on an Apple M4 Pro in Chromium, based on 20 samples.
It does not establish mobile latency, peak RAM, energy, or a complete
offline cold-start experience. <span class="citation" data-cites="draftmarkmetrics">(DraftMark project 2026c)</span></p>
<p>The base TMR model was trained on RAID. DraftMark additionally used
DetectRL-X-derived material and other sources. Its release documents
overlap screening, including exclusions against public training sources,
but acknowledges that unpublished upstream training data cannot be
checked. Treat RAID as an important benchmark-distribution test and use
fresh generators, rewriting methods and sources for genuine transfer
claims. <span class="citation" data-cites="tmr draftmarkmetrics">(Oxidane 2025; DraftMark project
2026c)</span></p>
<h1 data-number="4" id="benchmarks-and-exact-scores-to-challenge"><span class="header-section-number">4</span> Benchmarks and exact scores to
challenge</h1>
<h2 data-number="4.1" id="official-raid-the-public-english-benchmark-gate"><span class="header-section-number">4.1</span> Official RAID: the public
English benchmark gate</h2>
<p>The following rows are from the same pinned official RAID snapshot,
all settings including attacks, covering 672,000 test IDs. Scores are
percentages. These are selected relevant competitors, not a claim that
every model is publicly runnable at the same precision. <span class="citation" data-cites="raidrepo raid150 raid1b meld">(Dugan et al.
2026; RAID evaluation maintainers 2026a, 2026b; C. Li, Wan, and Paetzold
2026)</span></p>
<table>
<thead>
<tr class="header">
<th style="text-align: left;">Detector</th>
<th style="text-align: right;">TPR at 1% FPR</th>
<th style="text-align: right;">TPR at 5% FPR</th>
<th style="text-align: right;">AUROC</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Winston AI, Sep 2026 submission</td>
<td style="text-align: right;">99.89</td>
<td style="text-align: right;">99.98</td>
<td style="text-align: right;">99.89</td>
</tr>
<tr class="even">
<td style="text-align: left;">GPTTrace Ettin-1B v8s, Nov 2026</td>
<td style="text-align: right;"><strong>99.54</strong></td>
<td style="text-align: right;"><strong>99.93</strong></td>
<td style="text-align: right;"><strong>99.94</strong></td>
</tr>
<tr class="odd">
<td style="text-align: left;">XYZ SCIENCE, Oct 31 submission</td>
<td style="text-align: right;">99.35</td>
<td style="text-align: right;">99.91</td>
<td style="text-align: right;">99.94</td>
</tr>
<tr class="even">
<td style="text-align: left;">MELD, Apr 2026 submission</td>
<td style="text-align: right;">99.24</td>
<td style="text-align: right;">99.78</td>
<td style="text-align: right;">99.82</td>
</tr>
<tr class="odd">
<td style="text-align: left;">GPTTrace Ettin-150M v5e</td>
<td style="text-align: right;"><strong>98.91</strong></td>
<td style="text-align: right;"><strong>99.80</strong></td>
<td style="text-align: right;"><strong>99.89</strong></td>
</tr>
<tr class="even">
<td style="text-align: left;">GPTZero, Oct 2025 submission</td>
<td style="text-align: right;">92.93</td>
<td style="text-align: right;">97.24</td>
<td style="text-align: right;">99.25</td>
</tr>
<tr class="odd">
<td style="text-align: left;">TMR RoBERTa, DraftMark’s ancestor</td>
<td style="text-align: right;">90.17</td>
<td style="text-align: right;">95.79</td>
<td style="text-align: right;">99.28</td>
</tr>
<tr class="even">
<td style="text-align: left;">ModernBERT AI Detection</td>
<td style="text-align: right;">88.23</td>
<td style="text-align: right;">94.14</td>
<td style="text-align: right;">97.65</td>
</tr>
<tr class="odd">
<td style="text-align: left;">e5-small-LoRA</td>
<td style="text-align: right;">73.08</td>
<td style="text-align: right;">85.69</td>
<td style="text-align: right;">96.85</td>
</tr>
<tr class="even">
<td style="text-align: left;">BERT-tiny-4M</td>
<td style="text-align: right;">71.90</td>
<td style="text-align: right;">84.18</td>
<td style="text-align: right;">96.71</td>
</tr>
</tbody>
</table>
<p><strong>Practical interpretation:</strong> a comparable compact
detector must challenge 98.91% TPR at 1% FPR, while 99.54% is the
relevant verified open 1B reference. A universal leaderboard claim must
also address the strongest closed submissions. Beating legacy OpenAI
GPT-2 RoBERTa detectors would not establish modern state of the art.</p>
<p>The newer v8s result supersedes older pages quoting 99.42% TPR at 1%
FPR for the 1B model. Avoid copying stale ranks: an October 31
submission also changed the ordering after GPTTrace’s October marketing
table. The exact unrounded metrics are preserved in the evidence CSV.
<span class="citation" data-cites="raid1b raidrepo">(RAID evaluation
maintainers 2026b; Dugan et al. 2026)</span></p>
<h3 data-number="4.1.1" id="a-critical-threshold-distinction"><span class="header-section-number">4.1.1</span> A critical threshold
distinction</h3>
<p>The official evaluator defaults to <strong>per-domain
tuning</strong>. It chooses each domain’s threshold using unattacked
human <strong>test</strong> scores, then applies that threshold to AI
text and pools the resulting counts. This is the official diagnostic
protocol; it is not evidence that one fixed threshold calibrated earlier
will maintain 1% FPR for unknown app users. The paper should report both
official RAID numbers and a second deployment track with independent
calibration. <span class="citation" data-cites="raidrepo">(Dugan et al.
2026)</span></p>
<p>Also, the 150M competitor’s card reports a small-sample INT8 versus
PyTorch discrepancy: AUROC 0.975 versus 0.990 on 300 MAGE texts. Its
official RAID number should not automatically be attributed to the exact
deployed INT8 browser artifact. Re-evaluate that artifact under the same
runtime and input policy as DraftMark. <span class="citation" data-cites="gpttrace150">(Nakas 2026a)</span></p>
<h2 data-number="4.2" id="mage-generation-and-paraphrase-transfer"><span class="header-section-number">4.2</span> MAGE: generation and paraphrase
transfer</h2>
<p>MAGE supplies explicit OOD generator and paraphrase testbeds. Useful
author-reported reference points are below. They are not a centralized
current SOTA ranking; reproduce the exact testbed and scoring pipeline
before asserting a win. <span class="citation" data-cites="mage gpttrace150 gpttrace1b">(Y. Li et al. 2024; Nakas
2026a, 2026b)</span></p>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Testbed</th>
<th style="text-align: right;">Compact GPTTrace 150M</th>
<th style="text-align: right;">Larger reference</th>
<th style="text-align: left;">Proposed interpretation</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Unseen GPT-4 generation, AUROC</td>
<td style="text-align: right;">0.976</td>
<td style="text-align: right;">0.990, earlier 1B teacher</td>
<td style="text-align: left;">A compact generalization gate</td>
</tr>
<tr class="even">
<td style="text-align: left;">OOD paraphrase, AUROC</td>
<td style="text-align: right;"><strong>0.756</strong></td>
<td style="text-align: right;"><strong>0.919</strong>, 1B v8s author
run</td>
<td style="text-align: left;">A more meaningful head-to-head target</td>
</tr>
</tbody>
</table>
<p>DivEye separately reports 0.87 paraphrase AUROC, and its combination
with BiScope reports 0.89. These establish that 0.756 is a useful direct
compact-competitor baseline, not the best score in the literature. <span class="citation" data-cites="diveye">(Basani and Chen 2026)</span></p>
<p><strong>Label policy matters.</strong> MAGE’s original labels use 0
for machine and 1 for human. Its paraphrase testbed includes
machine-rewritten human text under the machine-generated category.
Preserve that definition for official comparability, then separately
measure human-origin writing with light AI assistance. A product that
intends to protect human-origin writing is answering a different
question from detecting any machine intervention. <span class="citation" data-cites="mage">(Y. Li et al. 2024)</span></p>
<h2 data-number="4.3" id="detectrl-x-multilingual-and-editing-sensitive-evaluation"><span class="header-section-number">4.3</span> DetectRL-X: multilingual and
editing-sensitive evaluation</h2>
<p>DetectRL-X evaluates binary and ternary detection over eight transfer
dimensions. Its languages do not include Thai. The table below
reproduces its Table 2 averages. The column labeled <strong>macro-F1 at
1% FPR reports F1, not true-positive rate</strong>. <span class="citation" data-cites="detectrlx">(Wu et al. 2026)</span></p>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Detector</th>
<th style="text-align: right;">Binary best macro-F1</th>
<th style="text-align: right;">Binary macro-F1 at 1% FPR</th>
<th style="text-align: right;">Ternary best macro-F1</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">XLM-R-base classifier</td>
<td style="text-align: right;"><strong>95.58</strong></td>
<td style="text-align: right;">91.31</td>
<td style="text-align: right;">82.78</td>
</tr>
<tr class="even">
<td style="text-align: left;">mDeBERTa-v3-base classifier</td>
<td style="text-align: right;">95.48</td>
<td style="text-align: right;"><strong>93.20</strong></td>
<td style="text-align: right;"><strong>87.68</strong></td>
</tr>
<tr class="odd">
<td style="text-align: left;">BiScope, Qwen2.5-7B-based</td>
<td style="text-align: right;">80.06</td>
<td style="text-align: right;">63.62</td>
<td style="text-align: right;">59.69</td>
</tr>
<tr class="even">
<td style="text-align: left;">Binoculars</td>
<td style="text-align: right;">68.17</td>
<td style="text-align: right;">54.18</td>
<td style="text-align: right;">41.62</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Fast-DetectGPT</td>
<td style="text-align: right;">48.27</td>
<td style="text-align: right;">40.50</td>
<td style="text-align: right;">28.12</td>
</tr>
</tbody>
</table>
<p>The cross-paraphrase binary slice is much harder: XLM-R reaches 77.32
best macro-F1 and 64.13 macro-F1 at 1% FPR. For the ternary task,
mDeBERTa’s macro-F1 at 1% FPR is 81.10. These are useful challenge
scores. Larger decoder-based systems do not automatically win, but the
encoder systems receive supervised training: this is not a controlled
estimate of model-size effects. <span class="citation" data-cites="detectrlx">(Wu et al. 2026)</span></p>
<p>Because DraftMark already draws from DetectRL-X, evaluate only
documented held-out groups for regression and reserve genuinely unseen
generator/rewriter/source combinations for transfer. Do not call the
complete dataset unseen merely because a particular test file was not
used.</p>
<h2 data-number="4.4" id="wattle-a-harder-test-of-web-rewriting"><span class="header-section-number">4.4</span> WATTLE: a harder test of web
rewriting</h2>
<p>WATTLE is a recent source-matched web benchmark. Its results make the
research gap clear: good conventional benchmark scores coexist with poor
detection at fixed low-FPR operating points. Table 4 reports the
following spin/rewrite results; each method uses its own calibrated
operating point. <span class="citation" data-cites="wattle">(Chernikov,
Ptitsyn, and Burlova 2026)</span></p>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Method</th>
<th style="text-align: right;">Rewrite AUROC</th>
<th style="text-align: right;">Rewrite recall at calibrated 1% FPR</th>
<th style="text-align: right;">Frontier rewrite AUROC</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">RoBERTa trained on MAGE</td>
<td style="text-align: right;">73.5</td>
<td style="text-align: right;">6.9%</td>
<td style="text-align: right;">67.3</td>
</tr>
<tr class="even">
<td style="text-align: left;">RADAR</td>
<td style="text-align: right;"><strong>80.3</strong></td>
<td style="text-align: right;">1.5%</td>
<td style="text-align: right;"><strong>75.4</strong></td>
</tr>
<tr class="odd">
<td style="text-align: left;">Binoculars</td>
<td style="text-align: right;">67.9</td>
<td style="text-align: right;">11.7%</td>
<td style="text-align: right;">38.2</td>
</tr>
<tr class="even">
<td style="text-align: left;">PAWN++, two frozen 1B probes</td>
<td style="text-align: right;">70.1</td>
<td style="text-align: right;">8.6%</td>
<td style="text-align: right;">58.1</td>
</tr>
</tbody>
</table>
<p>A useful experiment should challenge the strongest relevant baseline
on each metric, not only the proposed method in the paper. On this
comparison, surpassing PAWN++’s 58.1 frontier AUROC is insufficient
because RADAR already scores 75.4. Re-run current GPTTrace and
MELD/Vanguard comparators here before describing a new result as the
best available. The paper’s clean RAID-train results are a separate
protocol and are deliberately omitted from the official RAID table.
<span class="citation" data-cites="wattle">(Chernikov, Ptitsyn, and
Burlova 2026)</span></p>
<h2 data-number="4.5" id="shared-tasks-and-supporting-benchmarks"><span class="header-section-number">4.5</span> Shared tasks and supporting
benchmarks</h2>
<p>PAN-CLEF 2026 provides an adversarial software-submission setting.
The winning system’s composite score is 0.975, with F1 0.976 and FPR
5.3%; the second-place ModernBERT system has AUROC 0.993 but FPR 10.8%
at its submitted decision rule. Its ranking is a composite, not a
low-FPR guarantee. The task’s data page restricts redistribution and
commercial reuse; use it within its stated research terms. <span class="citation" data-cites="pan2026">(PAN organizers 2026)</span></p>
<p>COLING 2025’s GenAI detection shared task offers additional transfer
checks: 83.07 English test macro-F1 and 79.16 multilingual test macro-F1
are organizer-reported winning scores. These are distinct from
development accuracy. MULTITuDE is useful for broader cross-language
diagnosis but does not include Thai. Legacy HC3, Ghostbuster and
original DetectRL are supporting tests, not sufficient evidence of
current frontier robustness. <span class="citation" data-cites="coling2025 multitude">(Wang et al. 2025; Macko et al.
2023)</span></p>
<h1 data-number="5" id="model-strategy-small-students-relevant-larger-comparators"><span class="header-section-number">5</span> Model strategy: small students,
relevant larger comparators</h1>
<h2 data-number="5.1" id="recommended-model-ladder"><span class="header-section-number">5.1</span> Recommended model ladder</h2>
<table>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Model or family</th>
<th style="text-align: left;">Role</th>
<th style="text-align: left;">Why include it</th>
<th style="text-align: left;">Main limitation</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">DraftMark v3, about 125M</td>
<td style="text-align: left;">Frozen product control</td>
<td style="text-align: left;">Actual existing product, published
baseline and device policy</td>
<td style="text-align: left;">English; rewrite-family imbalance</td>
</tr>
<tr class="even">
<td style="text-align: left;">Same RoBERTa, all layers trainable</td>
<td style="text-align: left;">First controlled candidate</td>
<td style="text-align: left;">Isolates adaptation capacity without
changing tokenizer/export architecture</td>
<td style="text-align: left;">More training freedom can overfit</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Ettin-68M</td>
<td style="text-align: left;">Small local student</td>
<td style="text-align: left;">Tests whether a substantially smaller
encoder retains useful low-FPR recall</td>
<td style="text-align: left;">Quality and export must be measured</td>
</tr>
<tr class="even">
<td style="text-align: left;">Ettin-150M</td>
<td style="text-align: left;">Main local student</td>
<td style="text-align: left;">Direct comparison with a strong
browser-sized competitor</td>
<td style="text-align: left;">Existing distillation prior art</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Ettin-1B / GPTTrace v8s</td>
<td style="text-align: left;">English teacher and larger comparator</td>
<td style="text-align: left;">Strong verified official score and more
recent rewrite training</td>
<td style="text-align: left;">Around 1B weights; unsuitable as the
default low-memory tier</td>
</tr>
<tr class="even">
<td style="text-align: left;">MELD, version pinned</td>
<td style="text-align: left;">Robust training baseline</td>
<td style="text-align: left;">Multi-task, consistency and ranking
recipe</td>
<td style="text-align: left;">Paper’s 396M version and current
repository differ</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Vanguard ModernBERT-large</td>
<td style="text-align: left;">Additional encoder comparator</td>
<td style="text-align: left;">Recent OOD author evaluation and
shared-task lineage</td>
<td style="text-align: left;">Author-harness results need
replication</td>
</tr>
<tr class="even">
<td style="text-align: left;">mDeBERTa-v3-base / XLM-R-base</td>
<td style="text-align: left;">Multilingual teacher and baseline</td>
<td style="text-align: left;">Strong matched DetectRL-X results;
suitable multilingual starting point</td>
<td style="text-align: left;">Large vocabulary affects total size</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Binoculars / Fast-DetectGPT</td>
<td style="text-align: left;">Larger statistical baselines</td>
<td style="text-align: left;">Different inductive bias and training-data
requirements</td>
<td style="text-align: left;">Multiple large forward passes and
distribution sensitivity</td>
</tr>
</tbody>
</table>
<p>Ettin exposes 17M, 32M, 68M, 150M, 400M and 1B encoder scales. Begin
with 68M and 150M rather than running the entire ladder. Microsoft’s
mDeBERTa card counts 86M backbone plus about 190M embedding parameters,
approximately <strong>276M total</strong>, not 86M. Its multilingual
pretraining and Thai evaluation make it a reasonable Thai starting
point, but they do not establish Thai AI-detection accuracy. <span class="citation" data-cites="ettin mdeberta">(JHU CLSP 2025; Microsoft
2021)</span></p>
<p>MELD’s paper describes a 396M trainable Ettin-400M configuration. Its
current model repository differs, so compare a pinned paper-era artifact
or explicitly label a fresh reproduction. Vanguard reports 0.9475 mean
OOD AUROC across seven datasets, but this author-harness mean cannot be
ranked against RAID or DetectRL-X aggregates. <span class="citation" data-cites="meld vanguard">(C. Li, Wan, and Paetzold 2026; Thorat
2026)</span></p>
<h2 data-number="5.2" id="how-to-compare-larger-models-fairly"><span class="header-section-number">5.2</span> How to compare larger models
fairly</h2>
<p>Use two experiments. In the <strong>released-system
comparison</strong>, run each published detector using its documented
preprocessing, then fit operating thresholds on the same permitted
calibration population. In the <strong>controlled architecture
comparison</strong>, train the selected student and larger encoder on
the same groups, labels, sampling distribution and tuning budget. Report
both; they answer different questions.</p>
<p>For a decoder comparator, specify the exact observer/scoring pair,
dtype, sequence limit and feature extraction. Binoculars uses paired
model likelihoods; Fast-DetectGPT’s maintained repository recommends
newer model pairs than its original experiment. Count all resident
models and inference passes, not only the learned classification head. A
2.7M head atop two frozen 1B probes is still roughly a 2B inference
system. <span class="citation" data-cites="binoculars fastdetect wattle">(Hans et al. 2024; Bao et al.
2026; Chernikov, Ptitsyn, and Burlova 2026)</span></p>
<p>Do not compare H100 latency for a large model with phone CPU latency
for the student as though the hardware were matched. Report server
throughput separately from device batch-one latency. For each target
device, list either measured performance or the concrete reason a model
cannot run under the selected memory/runtime budget.</p>
<h1 data-number="6" id="improvements-most-likely-to-matter"><span class="header-section-number">6</span> Improvements most likely to
matter</h1>
<h2 data-number="6.1" id="prioritize-data-coverage-before-architecture-novelty"><span class="header-section-number">6.1</span> Prioritize data coverage before
architecture novelty</h2>
<p>The present failure pattern suggests a targeted first intervention:
expand decoder-style rewriting while retaining enough seq2seq examples
and difficult human negatives to avoid another recall tradeoff. Start
with a controlled 50k-100k-example experiment, then test 200k-300k only
if the held-out transfer curve justifies it. Those counts are proposed
experiment sizes, not requirements or demonstrated optima.</p>
<p>Create matched groups containing the original human source,
independent AI drafts on the same topic, multiple rewrite styles and any
subsequent edits. Hold out entire source families, generator families
and rewriting methods in separate test tracks. Match length, topic and
formatting across classes so the classifier cannot solve the task by
recognizing different corpora.</p>
<p>Human negatives should include learner prose, formal technical
writing, templated business text, short but valid passages, translated
writing and repeated-domain vocabulary. Recruit or license a distinct
learner-writing corpus for final evaluation. Reusing ASAP-derived
patterns in both development and the only learner test gives a narrow
estimate of real-world fairness.</p>
<p>Keep the current v3 holdout as a regression suite. Once its results
guide multiple decisions, it is no longer an untouched final test.
Repeatedly changing thresholds or examples based on that holdout would
make later headline gains less convincing.</p>
<h2 data-number="6.2" id="treat-editing-provenance-explicitly"><span class="header-section-number">6.2</span> Treat editing provenance
explicitly</h2>
<p>Use four documented provenance groups: human originals; human-origin
drafts lightly edited by AI; AI-origin drafts rewritten or human-edited;
and unedited AI drafts. Where available, retain edit operations and
aligned spans. Derive benchmark labels from each benchmark’s stated
definition rather than forcing one universal label mapping.</p>
<p>For product evaluation, separate <strong>origin</strong> from
<strong>assistance</strong>. A detector may recognize machine polishing
without establishing that the underlying ideas or draft came from AI. A
third assisted/uncertain outcome can be useful, but only when trained
and evaluated with those labels. Document-level supervision alone does
not justify presenting sentence highlights as verified machine-authored
spans.</p>
<p>This construct distinction is central to the proposed publication. It
prevents a model from appearing more robust simply because it flags all
polished human writing as AI.</p>
<h2 data-number="6.3" id="distillation-is-useful-engineering-not-sufficient-novelty"><span class="header-section-number">6.3</span> Distillation is useful
engineering, not sufficient novelty</h2>
<p>After measuring the English teacher on training/development groups,
train a compact student with hard labels and cached teacher logits. A
reasonable starting objective is:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>β„’</mi><mrow><mi mathvariant="normal">b</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">s</mi><mi mathvariant="normal">e</mi></mrow></msub><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>βˆ’</mo><mi>Ξ±</mi><mo stretchy="true" form="postfix">)</mo></mrow><mspace width="0.167em"></mspace><mrow><mi mathvariant="normal">C</mi><mi mathvariant="normal">E</mi></mrow><mrow><mo stretchy="true" form="prefix">(</mo><mi>y</mi><mo>,</mo><msub><mi>p</mi><mi>s</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mi>Ξ±</mi><msup><mi>T</mi><mn>2</mn></msup><mspace width="0.167em"></mspace><mrow><mi mathvariant="normal">K</mi><mi mathvariant="normal">L</mi></mrow><mrow><mo stretchy="true" form="prefix">(</mo><msubsup><mi>p</mi><mi>t</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>T</mi><mo stretchy="true" form="postfix">)</mo></mrow></msubsup><mspace width="0.167em"></mspace><mo stretchy="false" form="postfix">βˆ₯</mo><mspace width="0.167em"></mspace><msubsup><mi>p</mi><mi>s</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>T</mi><mo stretchy="true" form="postfix">)</mo></mrow></msubsup><mo stretchy="true" form="postfix">)</mo></mrow><mi>.</mi></mrow><annotation encoding="application/x-tex">
\mathcal{L}_{\mathrm{base}}=(1-\alpha)\,\mathrm{CE}(y,p_s)
+\alpha T^2\,\mathrm{KL}(p_t^{(T)}\,\|\,p_s^{(T)}).
</annotation></semantics></math></p>
<p>Try
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi><mo>=</mo><mn>2</mn></mrow><annotation encoding="application/x-tex">T=2</annotation></semantics></math>
and a small grid for
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>Ξ±</mi><annotation encoding="application/x-tex">\alpha</annotation></semantics></math>,
selected only on development data. Preserve hard provenance labels when
a teacher is confidently wrong. Cache logits with model revision,
tokenizer, text checksum and class mapping. Teacher use of test data is
still training contamination after distillation.</p>
<p>Then ablate one extra mechanism at a time: clean/rewrite consistency
for transformations that preserve the task label; ranking against hard
human negatives; and group-balanced sampling or group-aware validation.
MELD already combines auxiliary heads, an EMA consistency teacher and
hard-negative ranking. Reproducing those components is a strong
baseline, not a new method claim. <span class="citation" data-cites="meld">(C. Li, Wan, and Paetzold 2026)</span></p>
<h2 data-number="6.4" id="research-candidate-stability-of-the-human-score-tail-after-export"><span class="header-section-number">6.4</span> Research candidate: stability
of the human-score tail after export</h2>
<p>The proposed method-level question is whether training and
calibration designed around the <em>exported</em> score distribution
improve low-FPR transfer more than ordinary distillation plus
post-training quantization.</p>
<p>Evaluate FP32/BF16, weight-only INT8, and any candidate
activation-quantized version on identical calibration and test groups.
Track the change in human-tail quantiles, decision flips, worst-group
FPR and rewrite recall. If export hurts tail separation, investigate a
quantization-aware consistency term or selective higher precision for
sensitive layers, keeping training data and tuning budgets matched.</p>
<p>This is a hypothesis to test, not an established first. A negative
result is informative: post-export calibration alone may be sufficient,
or a different student architecture may be more effective. Quantization
is not lossless, and runtime documentation explicitly recommends
inspecting where numerical changes arise. <span class="citation" data-cites="ortquant">(ONNX Runtime contributors 2026c)</span></p>
<p>Temperature scaling can improve probability calibration on a matching
population; it cannot create missing class separation or guarantee FPR
under distribution shift. Use it as a calibration baseline, not as the
central explanation for OOD gains.</p>
<h1 data-number="7" id="evaluation-protocol-for-a-publishable-result"><span class="header-section-number">7</span> Evaluation protocol for a
publishable result</h1>
<h2 data-number="7.1" id="separate-four-roles-for-data"><span class="header-section-number">7.1</span> Separate four roles for
data</h2>
<p>Maintain distinct <strong>training</strong>, <strong>model-selection
development</strong>, <strong>threshold calibration</strong>, and
<strong>final test</strong> partitions. Split before augmentation,
grouping originals, descendants, near duplicates and author/source
families when available. Keep generator/rewriter holdouts distinct from
ordinary random source-group splits.</p>
<p>Each row should include a stable ID, source-group ID, text checksum,
language, domain, provenance label, generator and revision, edit method,
generation date, source/license information and split. Manifest joins
must require a prediction or an explicit invalid result for every
expected ID. Missing outputs must not silently improve recall or
false-positive estimates.</p>
<p>Report training-data exposure for every released comparator. A
teacher or baseline may have seen benchmark training sources or
unavailable private data. Separate verified disjoint tests from tests
where exposure is unknown; do not claim that one model’s data advantage
demonstrates an architectural advantage.</p>
<h2 data-number="7.2" id="primary-metrics"><span class="header-section-number">7.2</span> Primary metrics</h2>
<p>The main deployment table should report, per dataset and
subgroup:</p>
<ul>
<li>TPR/recall at the calibration-selected fixed threshold and the
<strong>actual test FPR</strong>.</li>
<li>Human false-flag counts, denominators and 95% confidence
intervals.</li>
<li>AUROC and partial AUROC over the low-FPR region as
threshold-independent diagnostics.</li>
<li>Precision at declared base rates, not only the balanced test-set
precision.</li>
<li>Abstention/unsupported-input rates and risk-versus-coverage
curves.</li>
<li>Model bytes, peak runtime memory, cold/warm p50/p95 latency and
supported runtime/device.</li>
</ul>
<p>If 1% of checked documents are actually AI-origin, a detector with
90% recall and 1% FPR has illustrative positive predictive value:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mfrac><mrow><mn>0.90</mn><mo>Γ—</mo><mn>0.01</mn></mrow><mrow><mn>0.90</mn><mo>Γ—</mo><mn>0.01</mn><mo>+</mo><mn>0.01</mn><mo>Γ—</mo><mn>0.99</mn></mrow></mfrac><mo>=</mo><mn>47.6</mn><mi>%</mi><mi>.</mi></mrow><annotation encoding="application/x-tex">\frac{0.90\times0.01}{0.90\times0.01+0.01\times0.99}=47.6\%.</annotation></semantics></math></p>
<p>This is a base-rate calculation, not a measured DraftMark deployment
result. It explains why a seemingly small FPR still matters for a
writing tool.</p>
<h2 data-number="7.3" id="calibration-and-statistical-uncertainty"><span class="header-section-number">7.3</span> Calibration and statistical
uncertainty</h2>
<p>Select a threshold on calibration human scores, with a documented
strict/non-strict tie rule. Freeze it before test evaluation. For
multiple supported populations, predeclare whether the policy uses one
global threshold or observable language/length buckets. Do not silently
select a more favorable threshold after observing a test domain.</p>
<p>For initial development, collect several thousand independent human
source groups. For a credible 1% deployment claim, aim for roughly
<strong>10,000 human examples per priority population</strong> where
practical, reporting both sample and independent-group counts. At a true
1% rate, a simple independent-binomial standard error with 10,000 cases
is about 0.10 percentage points; correlated documents reduce effective
information. For a 0.1% target, the necessary human test scale is
substantially larger.</p>
<p>Zero events provide a useful sanity check: the approximate one-sided
95% rule-of-three upper bound is
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>3</mn><mi>/</mi><mi>n</mi></mrow><annotation encoding="application/x-tex">3/n</annotation></semantics></math>.
With 220 essays, that is about 1.36%; the two-sided Wilson interval used
by the release is wider. State the interval convention rather than
mixing bounds.</p>
<p>Use paired source-group bootstrap intervals for differences between
models on the same examples. Report seed variation separately from test
sampling uncertainty. Frozen-calibrator test bootstrap intervals are
conditional on the chosen threshold; if making a stronger claim about
calibration stability, also resample the calibration groups and refit
thresholds. When many subgroup constraints are asserted simultaneously,
account for multiple comparisons or label intervals as exploratory.</p>
<h2 data-number="7.4" id="concrete-success-criteria-defined-before-the-next-test"><span class="header-section-number">7.4</span> Concrete success criteria,
defined before the next test</h2>
<table>
<colgroup>
<col style="width: 50%" />
<col style="width: 50%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Goal</th>
<th style="text-align: left;">Proposed gate, not an achieved result</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Existing product regression</td>
<td style="text-align: left;">Improve both rewrite families; target at
least 80% recall per family while retaining approximately 92% clean
recall.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Human false flags</td>
<td style="text-align: left;">Target &lt;=1% observed FPR on independent
general and learner sets; publish intervals. Use an
upper-confidence-bound criterion for a statistically supported bound on
the evaluated population, with independence/clustering assumptions
stated.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Compact public benchmark</td>
<td style="text-align: left;">Match or exceed 98.91% official RAID TPR
at 1% FPR, with the exact export evaluated separately.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Paraphrase transfer</td>
<td style="text-align: left;">Beat the compact comparator on identical
MAGE inputs; aim toward the 1B reference of 0.919 AUROC while improving
fixed-threshold recall.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Hard web rewriting</td>
<td style="text-align: left;">Beat the strongest rerun comparator on
WATTLE at matched actual FPR; published RADAR 80.3/75.4 AUROC values are
starting references.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Device size</td>
<td style="text-align: left;">Model plus tokenizer below 160 MB;
separately publish measured peak memory.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Desktop latency</td>
<td style="text-align: left;">Preserve or improve the current app’s warm
p95 on the same device and text distribution.</td>
</tr>
<tr class="even">
<td style="text-align: left;">Mobile</td>
<td style="text-align: left;">Establish a measured budget on named
iPhone and Android models; do not infer it from M4 Pro results.</td>
</tr>
</tbody>
</table>
<h1 data-number="8" id="h100-training-plan"><span class="header-section-number">8</span> H100 training plan</h1>
<h2 data-number="8.1" id="start-on-one-gpu"><span class="header-section-number">8.1</span> Start on one GPU</h2>
<p>The current 125M detector is small enough that a single H100 is the
appropriate first allocation. Training with BF16 autocast while
retaining FP32 optimizer states is a straightforward starting point. A
rough full-Adam parameter-state budget of 16 bytes per parameter implies
about 2 GB for 125M parameters and 16 GB for 1B, before activations,
temporary buffers and framework overhead. These are arithmetic memory
estimates, not measured allocations; the exact software stack and
batch/sequence sizes determine actual usage. <span class="citation" data-cites="torchamp">(PyTorch contributors 2026)</span></p>
<p>Do not begin by distributing the original 6,339-example experiment
across four H100s. First establish correctness and throughput on one.
Multi-GPU training becomes useful for larger data, teacher sweeps or
long sequences after profiling.</p>
<h2 data-number="8.2" id="run-sequence"><span class="header-section-number">8.2</span> Run sequence</h2>
<table>
<colgroup>
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Run</th>
<th style="text-align: left;">Change</th>
<th style="text-align: left;">Purpose</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">R0</td>
<td style="text-align: left;">Frozen released INT8 artifact</td>
<td style="text-align: left;">Establish the application baseline and
score policy.</td>
</tr>
<tr class="even">
<td style="text-align: left;">R1</td>
<td style="text-align: left;">Pinned TMR base, original four-layer
recipe</td>
<td style="text-align: left;">Reproduce the original adaptation as
closely as available source data permit.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R2</td>
<td style="text-align: left;">DraftMark v3 warm start, added
rewrite/human groups</td>
<td style="text-align: left;">Measure the value of better data without
changing architecture.</td>
</tr>
<tr class="even">
<td style="text-align: left;">R3</td>
<td style="text-align: left;">Same data, all layers trainable</td>
<td style="text-align: left;">Isolate adaptation capacity.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R4</td>
<td style="text-align: left;">Same data, 384 versus 512 training
tokens</td>
<td style="text-align: left;">Test the train/inference context
mismatch.</td>
</tr>
<tr class="even">
<td style="text-align: left;">R5</td>
<td style="text-align: left;">Same data, Ettin-150M and selected larger
teacher</td>
<td style="text-align: left;">Establish a controlled architecture
comparison.</td>
</tr>
<tr class="odd">
<td style="text-align: left;">R6</td>
<td style="text-align: left;">Distilled 150M and 68M students</td>
<td style="text-align: left;">Measure the quality-size frontier.</td>
</tr>
<tr class="even">
<td style="text-align: left;">R7</td>
<td style="text-align: left;">Export/calibration ablations</td>
<td style="text-align: left;">Test whether tail stability survives
quantization and runtime changes.</td>
</tr>
</tbody>
</table>
<p>The original published recipe is last four layers plus classifier,
384-token training cap, global batch 16, AdamW learning rate
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>2</mn><mo>Γ—</mo><msup><mn>10</mn><mrow><mi>βˆ’</mi><mn>5</mn></mrow></msup></mrow><annotation encoding="application/x-tex">2\times10^{-5}</annotation></semantics></math>,
weight decay 0.01, clipping 1.0, three epochs and seed 36513. Its cohort
weights are learner 1.45, decoder 1.30, seq2seq 0.90 and encoder 1.0.
Match these first; increasing batch size is a new experiment, not a
transparent acceleration. <span class="citation" data-cites="draftmarkhandoff">(DraftMark project 2026a)</span></p>
<p>For the expanded-data run, profile sequence lengths and try a small
development-only grid: learning rates
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>4</mn><mo>Γ—</mo><msup><mn>10</mn><mrow><mi>βˆ’</mi><mn>5</mn></mrow></msup></mrow><annotation encoding="application/x-tex">1,2,4\times10^{-5}</annotation></semantics></math>,
384/512 tokens, and one to three epochs. Use microbatches and
accumulation to reach a specified effective batch without changing it
accidentally. Record all decisions before final-test access.</p>
<h2 data-number="8.3" id="warm-start-reconstruction"><span class="header-section-number">8.3</span> Warm-start reconstruction</h2>
<p>The public training branch contains replacement tensors for the
trained layers. They must <strong>replace</strong> the corresponding
tensors in the pinned TMR base, not be added as a LoRA delta. Validate
the expected key set, label order, parameter count and SHA-256, then
initialize a new optimizer for continued training. The supplied training
kit implements this reconstruction and a new single-GPU entry point. It
is not the unpublished original trainer and cannot promise bitwise
reproduction. <span class="citation" data-cites="draftmarkhandoff">(DraftMark project 2026a)</span></p>
<p>The kit supports source-grouped local JSONL inputs, weighted loss,
optional cached teacher logits, checkpointing/resume and run provenance.
The separate evaluator operates on prediction files from the <em>actual
deployment runtime</em>, so native training validation cannot
accidentally substitute for exported-browser evaluation.</p>
<h2 data-number="8.4" id="estimate-time-from-a-pilot"><span class="header-section-number">8.4</span> Estimate time from a pilot</h2>
<p>Measure examples/second and tokens/second after warm-up on the
intended batch and sequence distribution. Estimate training time as:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>t</mi><mrow><mi mathvariant="normal">h</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">u</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">s</mi></mrow></msub><mo>β‰ˆ</mo><mfrac><mrow><msub><mi>N</mi><mrow><mi mathvariant="normal">e</mi><mi mathvariant="normal">x</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">m</mi><mi mathvariant="normal">p</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">s</mi></mrow></msub><mspace width="0.167em"></mspace><mi>E</mi></mrow><mrow><mn>3600</mn><mspace width="0.167em"></mspace><msub><mi>r</mi><mrow><mi mathvariant="normal">e</mi><mi mathvariant="normal">x</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">m</mi><mi mathvariant="normal">p</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">s</mi><mi mathvariant="normal">/</mi><mi mathvariant="normal">s</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">c</mi></mrow></msub></mrow></mfrac><mo>+</mo><msub><mi>t</mi><mrow><mi mathvariant="normal">e</mi><mi mathvariant="normal">v</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">u</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">t</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">n</mi></mrow></msub><mo>+</mo><msub><mi>t</mi><mrow><mi mathvariant="normal">e</mi><mi mathvariant="normal">x</mi><mi mathvariant="normal">p</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">t</mi></mrow></msub><mi>.</mi></mrow><annotation encoding="application/x-tex">t_{\mathrm{hours}}\approx\frac{N_{\mathrm{examples}}\,E}{3600\,r_{\mathrm{examples/sec}}}+t_{\mathrm{evaluation}}+t_{\mathrm{export}}.</annotation></semantics></math></p>
<p>For illustration only, 300,000 examples across three epochs at 50
examples/second would require five hours of training computation before
evaluation/export. The throughput is a hypothetical input, not an H100
benchmark. Teacher scoring, dataset generation and repeated app
evaluation may dominate the overall research schedule. Avoid committing
to an H100-hour budget until the pilot is measured.</p>
<h1 data-number="9" id="local-app-development"><span class="header-section-number">9</span> Local app development</h1>
<h2 data-number="9.1" id="build-on-the-existing-browser-product"><span class="header-section-number">9.1</span> Build on the existing browser
product</h2>
<p>DraftMark already has a local browser application. The next app
milestone should make the model lifecycle and offline behavior reliable:
package the runtime, tokenizer, model and decision policy under
versioned hashes; cache them; verify a full reload and scan in airplane
mode after installation; and keep processing off the UI thread.
Downloading assets once is compatible with local inference, but it is
different from working offline before the first installation. <span class="citation" data-cites="draftmark ortweb">(DraftMark project 2026b;
ONNX Runtime contributors 2026b)</span></p>
<p>Keep the existing weight-only INT8 path as the compatibility
baseline. The handoff states that activation-quantized trials produced
browser/native score drift. Do not replace it with generic dynamic
quantization just because another model uses that format. After any
export or runtime change, refit calibration on the shipped graph and
inspect threshold flips, especially among high-scoring human texts.
<span class="citation" data-cites="draftmarkhandoff ortquant">(DraftMark
project 2026a; ONNX Runtime contributors 2026c)</span></p>
<p>ONNX Runtime Web can use WASM and other execution providers.
Multithreaded WASM requires the browser’s support and cross-origin
isolation. WebGPU should be an optional measured backend, with operator
compatibility and parity verified, rather than a promised speedup.
Runtime-native mobile providers also have device-specific behavior and
may fall back or partition graphs. <span class="citation" data-cites="ortflags ortmobile">(ONNX Runtime contributors 2026d,
2026a)</span></p>
<h2 data-number="9.2" id="app-rollout-sequence"><span class="header-section-number">9.2</span> App rollout sequence</h2>
<table>
<colgroup>
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Stage</th>
<th style="text-align: left;">Deliverable</th>
<th style="text-align: left;">Acceptance evidence</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Browser/PWA</td>
<td style="text-align: left;">Existing UI plus versioned offline
assets</td>
<td style="text-align: left;">Reload and inference without network; no
text-upload requests; safe cache update/recovery</td>
</tr>
<tr class="even">
<td style="text-align: left;">Desktop</td>
<td style="text-align: left;">Lightweight wrapper or native ONNX
runner</td>
<td style="text-align: left;">Same tokenizer/windowing/policy; offline
installer; memory and latency measurements</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Mobile</td>
<td style="text-align: left;">iOS/Android packaging after browser
profiling</td>
<td style="text-align: left;">Device-specific memory, thermal and power
results; foreground/background recovery</td>
</tr>
<tr class="even">
<td style="text-align: left;">Integration</td>
<td style="text-align: left;">Selection/share extension or local
CLI</td>
<td style="text-align: left;">Explicit user action; versioned reports;
no hidden inference API fallback</td>
</tr>
</tbody>
</table>
<p>The minimal user flow is paste/import, check locally, and review a
cautious evidence result. Use states such as strong AI-like signal,
inconclusive and outside the supported range. A detector score is not
proof of misconduct. Show model version and supported-language/length
information where useful; do not present an uncalibrated softmax as an
authorship percentage.</p>
<p>Measure at least short, medium and long supported drafts. Report
download/install separately from cold model load and warm inference,
plus UI responsiveness, batch-one latency, peak RAM and repeated-scan
thermal behavior. Model file size alone is not RAM usage. On-device
optimization should minimize cost at matched error rates; a smaller
graph that sharply increases learner false flags is not a successful
product optimization. <span class="citation" data-cites="ortmobile">(ONNX Runtime contributors 2026a)</span></p>
<h1 data-number="10" id="a-focused-thai-english-research-extension"><span class="header-section-number">10</span> A focused Thai-English research
extension</h1>
<p>This extension fits a multilingual research program, but it should
not delay the first rigorous English release. DetectRL-X and MULTITuDE
leave Thai outside their language sets. That is a coverage gap in those
benchmarks, not proof that no Thai detector or dataset exists. <span class="citation" data-cites="detectrlx multitude">(Wu et al. 2026; Macko
et al. 2023)</span></p>
<p>Start from a Thai-capable multilingual teacher. Build a
provenance-controlled paired set with Thai originals, English originals
and naturally code-switched text. Include locally written academic,
professional and everyday prose; translated text; Thai speakers’
English; grammar assistance; heavier rewriting; and mixed human/AI
drafts. Preserve authors, sources and rewriting lineage across
splits.</p>
<p>Thai should not use an English whitespace word-count gate. Specify
character or tokenizer-based length buckets and audit tokenization
efficiency, sentence segmentation, Unicode normalization and code
switching. Avoid normalizers that erase meaningful Thai marks. Test
whether a compact shared student, separate language models, or a
language-routed design gives the best quality/memory tradeoff.
Calibration buckets must be based on observable supported language
information and tested under code switching.</p>
<p>A useful publication claim would be: <strong>a provenance-controlled
evaluation of compact local detectors under Thai-English editing and
generator shift, with uncertainty and device cost measured
together</strong>. Claiming the first Thai AI detector would require a
dedicated novelty search beyond this review.</p>
<h1 data-number="11" id="report-and-publication-strategy"><span class="header-section-number">11</span> Report and publication
strategy</h1>
<h2 data-number="11.1" id="what-can-be-published-now"><span class="header-section-number">11.1</span> What can be published now</h2>
<p>The current public model supports a transparent engineering report:
its training provenance, exact shipped artifact, cohort-level regression
results, confidence intervals and reference-device latency. The report
should acknowledge the seq2seq recall loss and limited human-test scale.
Its defensible claim is improved performance over the previous DraftMark
policy on that holdout, not state of the art across AI-text detection.
<span class="citation" data-cites="draftmarkmetrics">(DraftMark project
2026c)</span></p>
<p>This document can accompany that release as a research roadmap and
benchmark audit. It intentionally distinguishes reported evidence from
experiments still to run.</p>
<h2 data-number="11.2" id="what-would-justify-a-stronger-paper"><span class="header-section-number">11.2</span> What would justify a stronger
paper</h2>
<p>Frame the next paper around one main hypothesis: <strong>quantized
compact encoders can retain useful low-FPR generalization under unseen
rewriting and editing while reducing device cost relative to larger
detectors</strong>. A bilingual extension can test whether this survives
under language and code-switching shifts.</p>
<p>The minimum convincing experiment set is:</p>
<ol type="1">
<li>A matched comparison of current released detectors on public
testbeds and a fresh untouched transfer test.</li>
<li>A same-data student/teacher study separating architecture,
additional data and distillation effects.</li>
<li>Ablations of rewrite balancing, hard human negatives and the
proposed export-aware method.</li>
<li>Calibration on separate data, with subgroup FPR counts/intervals and
a clearly defined abstention policy.</li>
<li>Native-versus-exported quality, browser/device latency and
peak-memory measurements using identical documents.</li>
<li>Transparent limitations, source-exposure audit, task-label mappings,
and enough manifests/predictions to reproduce the findings where
licenses allow.</li>
</ol>
<p>Report paired intervals for the principal improvements and three
training seeds for the final selected recipe when feasible. Choose the
primary benchmark and metric before searching many variants. A 0.1-point
gain on a saturated aggregate with a weaker human-error tail is not the
intended success.</p>
<p>Potential formats include a technical report first, then an NLP
system-demonstration or applied/industry paper if deployment is the main
contribution, or a methods/benchmark paper if transfer and the
Thai-English study provide substantial new evidence. Venue eligibility,
deadlines and disclosure requirements must be checked when a submission
is selected; none is assumed here.</p>
<h2 data-number="11.3" id="suggested-paper-outline"><span class="header-section-number">11.3</span> Suggested paper outline</h2>
<p>Use an introduction grounded in real local-writing use cases,
followed by task/provenance definitions, related work, data/splits,
method, calibration protocol, main comparisons, quantization/device
analysis and error analysis. The main results table should include both
quality and measured cost. Place exact benchmark adapters, prompts,
source licensing and checkpoint/runtime hashes in the appendix and
repository.</p>
<p>The release package should contain model weights, tokenizer, label
mapping, runtime/policy versions, calibration provenance, a benchmark
table with evidence status, reproducible commands and device results.
Where raw text cannot be redistributed, release permitted IDs/hashes and
reconstruction instructions instead of pretending an aggregate JSON
alone is fully reproducible.</p>
<h1 data-number="12" id="prioritized-execution-plan"><span class="header-section-number">12</span> Prioritized execution plan</h1>
<p><strong>First, reproduce the current product’s behavior and establish
the comparison harness.</strong> Recover the original source checkout
and dataset manifests; restore the pinned model; generate
browser-runtime predictions; rerun the compact and 1B competitors on the
same independent calibration/test sets. This determines whether the
biggest deficit is model separation, threshold transfer, input handling
or the chosen text populations.</p>
<p><strong>Second, run the one-H100 controlled adaptation.</strong>
Expand decoder rewrites and difficult human negatives, retain seq2seq
coverage, compare partial/full unfreezing, and evaluate with a fresh
final set. Only advance to a new encoder after the data and calibration
baselines are understood.</p>
<p><strong>Third, train the compact student and validate the
export.</strong> Distill into 150M/68M candidates, compare export
formats, freeze calibration, and measure on the target desktop and
mobile devices. Use these results to decide which model belongs in each
local tier.</p>
<p><strong>Fourth, publish the verified result and extend the
study.</strong> Release the English evidence and app update when the
gates are met. Develop the Thai-English provenance-controlled benchmark
as a distinct research contribution, keeping the public paper’s claims
tied to the actual measured populations and devices.</p>
<h1 data-number="13" id="references"><span class="header-section-number">13</span> References</h1>
<div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
<div id="ref-fastdetect" class="csl-entry" role="listitem">
Bao, Guangsheng et al. 2026. <span>β€œ<span class="nocase">Fast-DetectGPT:
Official Implementation and Model-Pair Updates</span>.”</span> <a href="https://github.com/baoguangsheng/fast-detect-gpt">https://github.com/baoguangsheng/fast-detect-gpt</a>.
</div>
<div id="ref-diveye" class="csl-entry" role="listitem">
Basani, Advik Raj, and Pin-Yu Chen. 2026. <span>β€œ<span>Diversity Boosts
AI-Generated Text Detection</span>.”</span> <em>arXiv Preprint
arXiv:2509.18880v3</em>. <a href="https://arxiv.org/html/2509.18880v3">https://arxiv.org/html/2509.18880v3</a>.
</div>
<div id="ref-wattle" class="csl-entry" role="listitem">
Chernikov, Oleg, Evgenii Ptitsyn, and Albina Burlova. 2026. <span>β€œ<span class="nocase">PAWN++ in the Wild: Evaluating AI-Generated Text
Detection on the Web</span>.”</span> In <em>Proceedings of the 13th
Web-as-Corpus Workshop</em>, 282–96. <a href="https://doi.org/10.18653/2026.wac-1.25">https://doi.org/10.18653/2026.wac-1.25</a>.
</div>
<div id="ref-draftmarkhandoff" class="csl-entry" role="listitem">
DraftMark project. 2026a. <span>β€œ<span>DraftMark GPU Training
Handoff</span>.”</span> <a href="https://huggingface.co/saksornr/draftmark-roberta-int8/blob/068ccc7ba34f134526c9bbc72ac2033f00a40d45/handoff.md">https://huggingface.co/saksornr/draftmark-roberta-int8/blob/068ccc7ba34f134526c9bbc72ac2033f00a40d45/handoff.md</a>.
</div>
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β€”β€”β€”. 2026b. <span>β€œ<span class="nocase">DraftMark RoBERTa FT INT8: Model
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