The widespread deployment of Large Language Models (LLMs) has spurred significant progress in the detection of LLM-generated text.However, existing detection methods often rely on statistical features that are insufficient for reliable detection; for example, even though LLM-generated and humanwritten texts exhibit different probability distributions in surrogate models, they can produce nearly identical entropy values, thereby conflating the two types of text.In this paper, we propose that modulating the decoding temperature and monitoring how the probability distributions respond can better probe the intrinsic discrepancies between two types of text.Building upon this insight, we introduce a new feature termed Temperature Sensitivity (TS) and demonstrate that LLM-generated text tends to exhibit higher TS than humanwritten text.Finally, we propose NTS, a novel and simple zero-shot detector built upon normalized temperature sensitivity.Extensive experiments across three datasets, multiple domains, and various source models demonstrate the superior effectiveness and robustness of our proposed approach.Code avaliable at

The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLMgenerated text.Zero-shot detectors, due to their training-free nature, have received considerable attention and notable success.In this paper, we identify a new feature, token cohesiveness, that is useful for zero-shot detection, and we demonstrate that LLM-generated text tends to exhibit higher token cohesiveness than human-written text.Based on this observation, we devise TOC-SIN, a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors.To calculate token cohesiveness, TOCSIN only requires a few rounds of random token deletion and semantic difference measurement, making it particularly suitable for a practical black-box setting where the source model used for generation is not accessible.Extensive experiments with four state-of-the-art base detectors on various datasets, source models, and evaluation settings demonstrate the effectiveness and generality of the proposed approach.Code available at: https://github.com/Shixuan-Ma/TOCSIN.