Zero-shot detection methods for AI-generated text typically aggregate token-level statistics across entire sequences, overlooking the temporal dynamics inherent to autoregressive generation. We analyze over 120k text samples and reveal Late-Stage Volatility Decay: AI-generated text exhibits rapidly stabilizing log probability fluctuations as generation progresses, while human writing maintains higher variability throughout. This divergence peaks in the second half of sequences, where AI-generated text shows 24--32\% lower volatility. Based on this finding, we propose two simple features: Derivative Dispersion and Local Volatility, which computed exclusively from late-stage statistics. Without perturbation sampling or additional model access, our method achieves state-of-the-art performance on EvoBench and MAGE benchmarks and demonstrates strong complementarity with existing global methods.

翻译：AI生成文本的零样本检测方法通常在整个序列上聚合词元级统计量，忽略了自回归生成固有的时间动态特性。我们分析了超过12万个文本样本，揭示了晚期波动性衰减现象：随着生成过程的推进，AI生成文本的对数概率波动会迅速趋于稳定，而人类写作则始终保持较高的变异性。这种差异在序列的后半段达到峰值，其中AI生成文本的波动性降低了24%至32%。基于这一发现，我们提出了两个简单特征：导数离散度和局部波动性，这两个特征仅通过晚期统计量计算得出。无需扰动采样或额外模型访问，我们的方法在EvoBench和MAGE基准测试中实现了最先进的性能，并展现出与现有全局方法的强大互补性。