Learning Sequential–Structural Discrepancy: Label-Free Pretraining for Domain-Robust LLM-Generated Text Detection

The rapid progress of large language models (LLMs) has made it increasingly difficult to distinguish human-written from LLM-generated text, particularly under domain shift. Existing training-based detectors typically learn sequential or syntactic cues under human/LLM provenance supervision, while approaches combining the two often rely on direct feature concatenation, leaving their relationship underexplored and potentially capturing domain-specific artifacts. We empirically find a systematic difference between human-written and LLM-generated text in the discrepancy between sequential and structural representations: human-written text tends to show a larger cross-view discrepancy, whereas LLM-generated text exhibits stronger cross-view consistency. Importantly, this distributional gap persists across domains even when the two views are learned without human/LLM provenance labels. Based on this finding, we propose the **Sequential–Structural Discrepancy (SSD)** framework. SSD first pretrains sequential and structural encoders on unlabeled text through masked representation prediction and subsequently freezes both encoders for detection. The resulting token-level discrepancy plays two complementary roles: it controls the adaptive fusion of the sequential and structural views and is retained as an explicit feature for downstream classification. Supervised training with human/LLM provenance labels updates the downstream fusion and classification modules, while both pretrained view encoders remain frozen. Experiments on in-domain and out-of-domain benchmarks demonstrate that SSD consistently outperforms strong baselines and maintains robust performance under adversarial perturbations.