EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors

Abstract:AI-generated text (AIGT) detection can be sensitive to the decoding choices of the source large language model (LLM). We observe that perturbing next-token logits or adjusting sampling temperature can reduce detection performance, providing a clear signal of detector vulnerability to decoding-time distribution changes. Building on this observation, we propose EASE (Entropy-Adaptive Distribution Shaping for Evasion), a training-free and detector-agnostic framework for evading AIGT detectors. EASE computes predictive entropy directly from the source LLM's next-token distribution and uses it to adapt both logit perturbation and sampling temperature, without detector feedback or model fine-tuning. Experiments across three source LLMs and multiple detectors demonstrate consistent reductions in detection performance, with negligible degradation in text quality and negligible inference overhead.

Computation and Language (cs.CL)

arXiv:2610.09976 [cs.CL]

(or arXiv:2610.09976v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.09976 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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