DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection
arXiv:2511.01192v2 Announce Type: replace Abstract: Detecting machine-generated text has become a critical challenge amid the rapid advancement of LLMs, yet existing detectors degrade severely under domain shift. Through systematic pilot studies, we trace this vulnerability to two fundamental flaws in current generalization strategies, namely the incomplete preservation of domain-specific knowledge during multi-domain training and the misalignment between knowledge retrieval and the detection objective at inference. To address these gaps, we propose DEER, a Disentangled mixturE-of-ExpeRts fram
The rapid advancement of LLMs has made the detection of machine-generated text a critical and increasingly challenging problem due to its implications for information integrity and trust.
Sophisticated readers should care because effective detection of machine-generated text is vital for maintaining credible information environments and preventing misuse of powerful AI models.
This research introduces a new framework, DEER, which aims to improve the generalizability of machine-generated text detectors across different domains, addressing a key limitation of current methods.
- · AI Safety Researchers
- · Information Integrity Platforms
- · Social Media Platforms
- · Academic Research
- · Malicious LLM Users
- · Misinformation Propagators
Improved detection capabilities will make it harder to pass off AI-generated content as human-written, potentially increasing public trust in online information.
The necessity for more robust detection methods could spur further innovation in adversarial AI, creating an ongoing arms race between generation and detection.
Enhanced detection could force developers of legitimate LLMs to incorporate detection-resistant features, subtly influencing the future architecture and deployment of these models.
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