Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specific fingerprints. In this respect, we hypothesize that while large language models excel at local semantic consistency, their autoregressive nature results in a specific kind of structural fragility compared to human writing. We propose Luminol-AIDetect, a novel, zero-shot statistical approach that exposes this fragility through coherence disruption. By applying a simple randomized text-shuffling procedure, we demonstrate that the resulting shift in perplexity serves as a principled, model-agnostic discriminant, as MGT displays a characteristic dispersion in perplexity-under-shuffling that differs markedly from the more stable structural variability of human-written text. Luminol-AIDetect leverages this distinction to inform its decision process, where a handful of perplexity-based scalar features are extracted from an input text and its shuffled version, then detection is performed via density estimation and ensemble-based prediction. Evaluated across 8 content domains, 11 adversarial attack types, and 18 languages, Luminol-AIDetect demonstrates state-of-the-art performance, with gains up to 17x lower FPR while being cheaper than prior methods.

翻译：机器生成文本检测需要识别跨生成模型的结构不变信号，而非依赖模型特异性指纹。基于此，我们假设虽然大语言模型擅长局部语义一致性，但其自回归特性导致其相较于人类写作存在特定类型的结构脆弱性。我们提出Luminol-AIDetect，一种通过连贯性破坏暴露该脆弱性的新型零样本统计方法。通过应用简单的随机文本混洗流程，我们证明由此产生的困惑度偏移可作为原理性、模型无关的判别指标——机器生成文本在混洗后呈现独特的困惑度离散特征，与人类写作更稳定的结构变异性存在显著差异。Luminol-AIDetect利用这一差异指导决策过程：从输入文本及其混洗版本中提取少量基于困惑度的标量特征，通过密度估计和集成学习进行检测。在8个内容领域、11种对抗攻击类型及18种语言上的评估表明，Luminol-AIDetect达到了最先进的性能，假阳性率降低高达17倍，同时成本低于现有方法。