Core Insights - The article highlights the systemic risk of information pollution in AI models, particularly in the context of county-level AI applications in China [1][4]. Group 1: Information Pollution Mechanisms - The first stage of pollution occurs during pre-training, where AI models absorb vast amounts of internet data, which often contains biases and misinformation [2]. - The second stage involves post-training, where intentional "cognitive poisoning" can occur through the introduction of biased data to enhance AI performance on specific tasks [2][3]. - The third stage is real-time searching, where AI retrieves information from a polluted internet, further perpetuating the cycle of misinformation [3][4]. Group 2: Consequences and Solutions - The lack of high-quality, reliable information sources on the Chinese internet exacerbates the issue, as many platforms promote low-quality content for traffic [4][5]. - A feedback loop is forming where AI-generated content containing factual errors is cited by other AI, amplifying and solidifying misinformation [4]. - To combat this, the establishment of a high-quality "clean corpus" is essential, with initiatives already proposed by Chinese authorities to create a national key corpus by 2027 [5].