With the rapid proliferation of large language models (LLMs), distinguishing machine-generated text from human-authored content has become increasingly critical for ensuring content authenticity, academic integrity, and trust in information systems. However, detecting text generated by LLMs remains a challenging problem, particularly in zero-shot settings where labeled data and domain-specific tuning are unavailable. To address this challenge, in this paper, we propose a novel Collaborative Multi-Agent Zero-Shot Detection framework (CMA-ZSD). In contrast to existing methods based on watermarking, statistical heuristics, or neural classifiers, our CMA-ZSD employs three functionally heterogeneous agents that perform differentiated perturbations of the input text. By jointly modeling semantic consistency, grammatical normalization, and feature-level reconstruction, our method captures intrinsic asymmetries between human-authored and LLM-generated text. A semantic similarity evaluation mechanism, combined with majority voting, enables robust and interpretable detection decisions that balance individual agent autonomy with collective consensus. Extensive experiments across 11 domains demonstrate the effectiveness of our method, with its zero-shot detection achieving accuracy comparable to domain-finetuned models in specific domains such as Finance and Reddit-dli5.
Sun et al. (2026) studied this question.