[
    {
        "id": "osp-17460",
        "type": "article-journal",
        "title": "EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors",
        "author": [
            {
                "family": "Zhou",
                "given": "Jicheng"
            },
            {
                "family": "Wong",
                "given": "Kahim"
            },
            {
                "family": "Wang",
                "given": "Jialong"
            },
            {
                "family": "Zhou",
                "given": "Jiantao"
            }
        ],
        "URL": "https://omanscience.com/en/articles/ease-entropy-adaptive-distribution-shaping-for-evading-ai-generated-text-detectors",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]