DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection

• URL: http://arxiv.org/abs/2511.01192v1
• Date: Mon, 03 Nov 2025 03:36:48 GMT
• Title: DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection
• Authors: Guoxin Ma, Xiaoming Liu, Zhanhan Zhang, Chengzhengxu Li, Shengchao Liu, Yu Lan,
• Abstract summary: We propose a novel framework designed to capture both domain-specific and domain-general MGT patterns.<n>We introduce a mixture-of-experts module, in which domain-specific experts learn fine-grained, domain-local distinctions between human and machine-generated text.<n>We also design a reinforcement learning-based routing mechanism that dynamically selects the appropriate experts for each input instance.
• Score: 21.449323711668487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting machine-generated text (MGT) has emerged as a critical challenge, driven by the rapid advancement of large language models (LLMs) capable of producing highly realistic, human-like content. However, the performance of current approaches often degrades significantly under domain shift. To address this challenge, we propose a novel framework designed to capture both domain-specific and domain-general MGT patterns through a two-stage Disentangled mixturE-of-ExpeRts (DEER) architecture. First, we introduce a disentangled mixture-of-experts module, in which domain-specific experts learn fine-grained, domain-local distinctions between human and machine-generated text, while shared experts extract transferable, cross-domain features. Second, to mitigate the practical limitation of unavailable domain labels during inference, we design a reinforcement learning-based routing mechanism that dynamically selects the appropriate experts for each input instance, effectively bridging the train-inference gap caused by domain uncertainty. Extensive experiments on five in-domain and five out-of-domain benchmark datasets demonstrate that DEER consistently outperforms state-of-the-art methods, achieving average F1-score improvements of 1.39% and 5.32% on in-domain and out-of-domain datasets respectively, along with accuracy gains of 1.35% and 3.61% respectively. Ablation studies confirm the critical contributions of both disentangled expert specialization and adaptive routing to model performance.

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