Fast when confident. Deeper when uncertain.
A sole-author research artifact for uncertainty-aware, evidence-assisted AI-text detection.

Overview

DTD-LRC is a two-stage cascade for open-world machine-generated text detection:

• DTD performs fast, lightweight first-stage detection from lexical, stylistic, syntactic, punctuation, and repetition features.
• MS-LRC is activated only inside an uncertainty band and examines cross-family, cross-scale language-model evidence.
• The system returns not only a decision, but also uncertainty and diagnostic evidence intended for human review.

The workshop manuscript, “DTD-LRC: Uncertainty-Gated Evidence Escalation for Open-World Machine-Generated Text Detection,” has been submitted to UncertaiNLP 2026 @ EMNLP through OpenReview. Submission does not imply acceptance.

Research contribution

The project studies a practical question:

Can a detector preserve low-cost inference for clear cases while escalating only ambiguous samples to deeper, more interpretable evidence?

DTD-LRC contributes:

1. Uncertainty-gated routing rather than always-on expensive inference.
2. Cross-family and cross-scale evidence through NLL-per-byte response patterns.
3. Ladder response curves and a family-scale matrix for structured Stage-2 evidence.
4. A reviewer-facing artifact with a live demo, API, precomputed evidence cards, full local pipeline, manuscript files, and explicit claim boundaries.

System architecture

flowchart LR
    A[Input text] --> B[Stage 1: DTD]
    B --> C{AI probability inside<br/>uncertainty band [0.41, 0.61]?}
    C -- No --> D[Return Stage-1 decision]
    C -- Yes --> E[Stage 2: MS-LRC]
    E --> F[NLL / byte across<br/>model families and scales]
    F --> G[Ladder response curves<br/>and family-scale matrix]
    G --> H[Escalated decision<br/>with interpretable evidence]

Loading

Headline validation results

Component	Metric	Value
Stage 1 — DTD	AUC	0.9899
Stage 1 — DTD	F1	0.9600
Stage 1 — DTD	Precision / Recall	0.96 / 0.96
Stage 1 — DTD	Average latency	38 ms
Routing gate	Uncertainty band	[0.41, 0.61]
Stage 2 — MS-LRC smoke evaluation	AUC	0.9622
Stage 2 — MS-LRC smoke evaluation	Accuracy	0.9333
Stage 2 — MS-LRC smoke evaluation	F1	0.9375
Cascade smoke evaluation	Stage-2 usage	31 / 120

In the 120-example cascade smoke evaluation, 74.2% of samples were resolved at Stage 1, while 25.8% were escalated.

These are internal artifact-validation results. They are not universal performance guarantees, and external comparisons require matched datasets, thresholds, preprocessing, and evaluation protocols. See Reproducibility and Claim Boundaries.

Artifact links

Artifact	Link
Interactive demo	Hugging Face Space
Direct application	Deployed app
Demonstration video	YouTube
Manuscript files	paper/DTD-LRC_UncertaiNLP_2026/
Artifact manifest	ARTIFACT_LINKS.md

Quick start

Lightweight reviewer mode

This mode runs interactive DTD inference and displays precomputed MS-LRC evidence without loading transformer models during startup.

git clone https://github.com/haveanicedaymydear/AI-Text-Cascade-Detect.git
cd AI-Text-Cascade-Detect
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python app.py

Windows PowerShell:

.\.venv\Scripts\Activate.ps1

Open:

http://127.0.0.1:5000

Full local cascade

pip install -r requirements-full.txt
python src/cascade_demo_app.py

The full MS-LRC path requires transformer model downloads or cache and more memory than the lightweight artifact.

API

Health check:

curl http://127.0.0.1:5000/api/health

Detection request:

curl -X POST http://127.0.0.1:5000/api/detect \
  -H "Content-Type: application/json" \
  -d '{"text":"The implementation of artificial intelligence systems requires careful evaluation of reliability."}'

Precomputed Stage-2 examples:

curl http://127.0.0.1:5000/api/mslrc_examples
curl http://127.0.0.1:5000/api/mslrc_examples?id=uncertain_sample

Repository map

.
├── app.py                          # Reviewer-facing Flask/API entry point
├── src/
│   ├── web_app.py                  # Original lightweight DTD application
│   └── cascade_demo_app.py         # Full local cascade
├── models/                         # Public model artifact(s)
├── examples/                       # Human, AI, uncertain, and MS-LRC examples
├── paper/DTD-LRC_UncertaiNLP_2026/ # Submitted manuscript files
├── templates/                      # Web interfaces
├── tests/                          # Project tests
├── docs/                           # Reproducibility notes and visual assets
├── requirements.txt                # Lightweight demo dependencies
└── requirements-full.txt           # Full local pipeline dependencies

Demo modes

Mode	Command	Purpose
Reviewer artifact	python app.py	Interactive DTD + precomputed MS-LRC evidence
Original DTD app	python src/web_app.py	Original trained DTD web/API application
Full local cascade	python src/cascade_demo_app.py	Transformer-backed Stage-2 execution

Limitations and responsible use

DTD-LRC is a probabilistic aid, not an authority. It should not be used as the sole basis for punitive academic, employment, authorship, moderation, or disciplinary decisions.

Known limitations include:

• false positives on short, formulaic, translated, heavily edited, creative, or stylistically unusual human writing;
• domain shift and generator shift;
• calibration drift under unseen distributions;
• reduced reliability on multilingual or code-mixed text unless separately evaluated;
• higher compute cost for full MS-LRC inference;
• vulnerability to future generators and deliberate evasion strategies.

Recommended use is evidence-assisted review: combine the output with contextual evidence, provenance, human judgment, and an appeal or correction process.

Citation

Citation metadata is available in CITATION.cff. Until a final archival publication exists, cite the repository and submitted manuscript as a research artifact:

@misc{zhou2026dtdlrc,
  author       = {Livan Zhou},
  title        = {DTD-LRC: Uncertainty-Gated Evidence Escalation for Open-World Machine-Generated Text Detection},
  year         = {2026},
  howpublished = {Sole-author manuscript submitted to UncertaiNLP 2026 @ EMNLP and open-source research artifact},
  url          = {https://github.com/haveanicedaymydear/AI-Text-Cascade-Detect}
}

Author

Livan Zhou
First Class Honours BSc in Computing Science
GitHub · Email

License

Released under the MIT License.