EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors
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Computer Science > Computation and Language
arXiv:2610.09976 (cs)
[Submitted on 7 Oct 2026]
Title:EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors
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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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.09976 [cs.CL] |
| (or arXiv:2610.09976v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09976
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors, by Jicheng Zhou and 3 other authors
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