Core Viewpoint - The article discusses a significant update from arXiv, requiring all review and position papers in the computer science category to undergo peer review before submission, primarily due to the overwhelming influx of AI-generated content [2][8]. Group 1: The Crisis of AI-Generated Papers - The term "Survey Paper DDoS attack" is introduced to describe the overwhelming number of low-quality AI-generated survey papers flooding the academic community [5][20]. - The increase in AI-generated content has led to a situation where valuable insights are obscured, akin to a denial-of-service attack, making it difficult for researchers to access meaningful academic contributions [7][21]. Group 2: Quantitative Evidence of the Surge - A study analyzed 10,063 survey papers from arXiv between 2020 and 2024, revealing a significant spike in submissions post-2022, coinciding with the rise of generative AI tools like ChatGPT [10][12]. - The average AI-generated score has more than doubled, indicating that AI is a primary driver of this growth [13]. - There has been a notable increase in suspicious publishing behavior, with authors publishing multiple papers in a short time frame, suggesting AI-assisted bulk production [14]. Group 3: Detrimental Effects on Academic Integrity - AI-generated reviews are not merely noise; they pose a serious threat to the academic ecosystem by introducing low-quality, redundant content [16][19]. - Traditional expert-written reviews provide critical insights, whereas AI-generated reviews often lack structure, innovative classification, and can contain inaccuracies [17][18]. - The phenomenon of "literature poisoning" occurs when new researchers rely on flawed AI-generated reviews, potentially embedding incorrect academic foundations [19]. Group 4: Proposed Solutions - The article suggests that arXiv's new regulations are a necessary but reactive measure against the crisis [23][25]. - The authors propose a shift towards "Dynamic Live Surveys" (DLS), which would create a community-maintained online knowledge base, allowing for real-time updates and reducing redundancy [24]. - Recommendations include stricter review processes, transparency in AI usage, and incentivizing high-quality reviews to combat the influx of low-quality submissions [26].