Tide: Zero-Shot LLM-Generated Text Detection via Directional Generation Asymmetry

With the rapid advance and wide use of large language models (LLMs), the text they generate is flooding into classrooms, publications, online forums and beyond. Telling it apart from human writing has become a practical necessity as concerns over misuse and attribution grow. Many existing detectors measure how predictable a text is to a surrogate LLM, reading it from left to right (i.e., in forward order), the order in which it was produced. However, we observe that adding a reading in the opposite direction (i.e., in reverse order) reveals a complementary and even stronger signal for detection. Specifically, reversal makes every text harder for the surrogate to predict, but LLM-generated text loses disproportionately more predictability than human text. We call this effect *directional generation asymmetry* and build our detector, Tide, on it. Tide computes the cross-entropy losses of a text and its reversal in one batched call to the frozen surrogate and combines the two losses into a single directional asymmetry score. On our new dataset of 225,000 documents spanning five domains, five frontier LLMs, six languages and six adversarial attacks, Tide outperforms nine state-of-the-art zero-shot baselines, achieving an average AUROC of 0.978, 4.7% higher than the best baseline. At a practical operating point of a 0.1% false-positive rate, Tide nearly doubles the true-positive rate of the best baseline, from 41% to 79%.