CAMF: Collaborative Adversarial Multi-agent Framework for Machine Generated Text Detection

• URL: http://arxiv.org/abs/2508.11933v1
• Date: Sat, 16 Aug 2025 06:25:27 GMT
• Title: CAMF: Collaborative Adversarial Multi-agent Framework for Machine Generated Text Detection
• Authors: Yue Wang, Liesheng Wei, Yuxiang Wang,
• Abstract summary: Existing zero-shot detection paradigms often exhibit significant deficiencies.<n>We introduce textbfCAMF, a novel architecture using multiple LLM-based agents.<n>This structured collaborative-adversarial process enables a deep analysis of subtle, cross-dimensional textual incongruities indicative of non-human origin.
• Score: 16.113113157328662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting machine-generated text (MGT) from contemporary Large Language Models (LLMs) is increasingly crucial amid risks like disinformation and threats to academic integrity. Existing zero-shot detection paradigms, despite their practicality, often exhibit significant deficiencies. Key challenges include: (1) superficial analyses focused on limited textual attributes, and (2) a lack of investigation into consistency across linguistic dimensions such as style, semantics, and logic. To address these challenges, we introduce the \textbf{C}ollaborative \textbf{A}dversarial \textbf{M}ulti-agent \textbf{F}ramework (\textbf{CAMF}), a novel architecture using multiple LLM-based agents. CAMF employs specialized agents in a synergistic three-phase process: \emph{Multi-dimensional Linguistic Feature Extraction}, \emph{Adversarial Consistency Probing}, and \emph{Synthesized Judgment Aggregation}. This structured collaborative-adversarial process enables a deep analysis of subtle, cross-dimensional textual incongruities indicative of non-human origin. Empirical evaluations demonstrate CAMF's significant superiority over state-of-the-art zero-shot MGT detection techniques.

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