GravText: A Robust Framework for Detecting LLM-Generated Text Using Triplet Contrastive Learning with Gravitational Factor

The growing abilities of large language models (LLMs) have introduced new challenges in reliably distinguishing LLM-generated texts from human-written content, particularly when paraphrasing techniques are used to evade detection. This paper proposes GravText, a detection framework designed to address this robustness gap by targeting paraphrase-invariant semantic features. GravText integrates triplet contrastive learning with a dynamic anchor switching strategy to better model inter-class separability under paraphrasing. Additionally, it introduces a physics-inspired gravitational factor based on cross-attention mechanisms, which enhances the discriminative power of learned embeddings by simulating semantic attraction and repulsion. Experimental results on the HC3 Chinese dataset demonstrate GravText’s superior robustness against paraphrasing. Crucially, further cross-lingual evaluation on an English essay dataset confirms the framework’s strong generalization ability and language-agnostic properties. These findings point to a promising direction for building more reliable AI-text detectors resilient to paraphrasing-based evasion.