{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:00:16Z","timestamp":1787673616835,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Engineering Research Center for Intelligent Computing and Information Innovation","award":["FYKFKT24049"],"award-info":[{"award-number":["FYKFKT24049"]}]},{"name":"Anhui Engineering Research Center for Intelligent Computing and Information Innovation","award":["ICII202508"],"award-info":[{"award-number":["ICII202508"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62403412"],"award-info":[{"award-number":["62403412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Language Commission of China","award":["ZDI145-71"],"award-info":[{"award-number":["ZDI145-71"]}]},{"name":"Key Research and Development Program of Jiangsu Province in China","award":["BE2023315"],"award-info":[{"award-number":["BE2023315"]}]},{"name":"Anhui Provincial Science and Technology Fortification Plan","award":["202423k09020015"],"award-info":[{"award-number":["202423k09020015"]}]},{"name":"Open Project Program of Key Laboratory of Knowledge"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/informatics13040062","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:14:46Z","timestamp":1776334486000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Collaborative Multi-Agent Method for Zero-Shot LLM-Generated Text Detection"],"prefix":"10.3390","volume":"13","author":[{"given":"Gang","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Anhui Engineering Research Center for Intelligent Computing and Information Innovation, Fuyang Normal University, Fuyang 236037, China"},{"name":"Department of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8036-9550","authenticated-orcid":false,"given":"Jipeng","family":"Qiang","sequence":"additional","affiliation":[{"name":"Department of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, B., Yuan, L., Cui, G., Chen, Y., Fu, C., He, B., Deng, Y., Liu, Z., Sun, M., and Gu, M. 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