Stop DDoS Attacking the Research Community with AI-Generated Survey Papers
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arXiv
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| Format: | Preprint |
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2025
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| author | Lin, Jianghao Shan, Rong Zhu, Jiachen Xi, Yunjia Yu, Yong Zhang, Weinan |
| author_facet | Lin, Jianghao Shan, Rong Zhu, Jiachen Xi, Yunjia Yu, Yong Zhang, Weinan |
| contents | Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i.e., survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09686 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stop DDoS Attacking the Research Community with AI-Generated Survey Papers Lin, Jianghao Shan, Rong Zhu, Jiachen Xi, Yunjia Yu, Yong Zhang, Weinan Computers and Society Artificial Intelligence Computation and Language Information Retrieval Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i.e., survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community. |
| title | Stop DDoS Attacking the Research Community with AI-Generated Survey Papers |
| topic | Computers and Society Artificial Intelligence Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2510.09686 |