Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection
Year: 2026
Type: conference-paper
Abstract: The rapid advancement of large language models has increasingly blurred the boundary between human-written and AI-generated text, raising societal risks such as misinformation dissemination, authorship ambiguity, and threats to intellectual property rights.These concerns highlight the urgent need for effective and reliable detection methods.While existing training-free approaches often achieve strong performance by aggregating token-level signals into a global score, they typically assume uniform token contributions, making them less robust under short sequences or localized token modifications.To address these limitations, we propose Exons-Detect, a training-free method for AI-generated text detection based on an exon-aware token reweighting perspective.Exons-Detect identifies and amplifies informative exonic tokens by measuring hiddenstate discrepancy under a dual-model setting, and computes an interpretable translation score from the resulting importance-weighted token sequence.Empirical evaluations demonstrate that Exons-Detect achieves state-of-the-art detection performance and exhibits strong robustness to adversarial attacks and varying input lengths.In particular, it attains a 2.2% relative improvement in average AUROC over the strongest prior baseline on DetectRL.Code and data are available at https://github.com/ Xiaoweizhu57/Exons-Detect.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5077752208
OpenAlex ID: W7166838834