Architecture: ModernBERT-large with a single classification head (binary, sigmoid output)
Output: A single scalar P(AI) in [0, 1]; a decision threshold of 0.5 is used by default, where P(AI) > 0.5 indicates AI-generated text
Language: English
License: MIT License
Training Data
This model was trained on approximately 1.1 million texts from three datasets: DACTYL 2.0, LLMTrace, and MAGA-Bench.
Evaluation
The model was evaluated against two leading open-source AI text detectors, Fakespot and Desklib, across ten benchmark datasets. Three of these (dactyl-v2.0, llm-trace-eng, maga) are in-distribution with respect to this model's training data; the remaining seven (beemo, coconuts, detectrl, dolly-cosmopedia, originalityai, realdet, uchicago) are out-of-distribution and were not seen during training.
All F1 scores are macro-averaged and computed at a decision threshold of P(AI) = 0.5.
Fakespot and Desklib use a two-head softmax architecture and were evaluated using their native argmax decision rule, which is mathematically equivalent to thresholding P(AI) at 0.5.
This model outperforms both baselines on most out-of-distribution datasets, with the exception of coconuts (Desklib) and originalityai (Fakespot), where the baselines hold an edge.
In-distribution performance is substantially higher than out-of-distribution performance, which is expected and should be taken into account when interpreting the headline averages; OOD results are more representative of expected real-world generalization.
Limitations and Out-of-Scope Use
This model should not be used as the sole basis for high-stakes decisions such as academic penalties or employment actions, given the false positive/negative rates documented below. Performance also degrades on text distributions not represented in training data; see evaluation results.
Citation
@article{thorat2026panclef,
title={Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection},
author={Thorat, Shantanu},
journal={Working Notes of CLEF},
year={2026}
}
