DivEye at PAN 2025: Diversity Boosts AI-Generated Text Detection

Detecting AI-generated text is increasingly important to prevent misuse in education, journalism, and social media, where synthetic fluency can obscure misinformation. This paper presents our solution for the Generative AI Authorship Verification Task at PAN 2025, where the objective is to distinguish machine-generated text from human-written content. We propose DivEye, a novel detection framework that leverages surprisal-based features to capture fluctuations in lexical and structural unpredictability, a signal more prominent in human-authored text. Our method performs competitively across diverse text domains and models, especially on challenging cases where model-generated text closely resembles human writing, and also outperforms the four official baselines of the PAN 2025 task.