Core Insights - The launch of the college entrance examination application model "KaoPu AI 3.0" by KaoPu AI in collaboration with Peking University marks a significant technological breakthrough in the college application industry, improving admission prediction accuracy by an average of 30% compared to traditional software [1][5]. Group 1: Model Performance - KaoPu AI 3.0 demonstrated impressive predictive performance, with over 90% of 985 and 211 universities showing an absolute score error of within 3 points when comparing predicted scores to actual scores from 2024 [3][6]. - For public undergraduate institutions (excluding 985 and 211), over 90% had an absolute score error of within 5.5 points, significantly outperforming traditional application software [3][6]. Group 2: Innovative Methodology - The new prediction method, "Graph Embedding," developed by KaoPu AI and Peking University, utilizes historical admission data to uncover deeper connections and patterns among universities and majors, enhancing the prediction of admission probabilities [6][8]. - This method combines graph embedding technology with probability estimation, providing substantial improvements in admission prediction accuracy compared to traditional methods [8]. Group 3: Expanded Options for Students - The model broadens the range of recommended universities and majors by calculating the similarity between different programs based on admission difficulty, helping students discover overlooked options that align with their profiles [9]. - This is particularly beneficial in competitive fields or when enrollment plans are reduced, allowing students to consider alternative choices [9]. Group 4: Enhanced Strategy Setting - The model allows for a more scientific and granular approach to setting application strategies, enabling students and parents to assess the risk levels of different choices based on estimated admission probability distributions [10]. - It facilitates the construction of a more reasonable application gradient by correlating target ranks with strategies such as "reach," "steady," and "safety" [10]. Group 5: Visualizing Competition - The model provides visualizations of the distribution of universities and majors in the embedding space, helping students and parents understand the competitive landscape within specific score ranges [11]. - This analysis aids in identifying clusters of similar programs, enhancing the understanding of overall competition dynamics [11]. Group 6: Availability - The "Graph Embedding" method has been fully integrated into the KaoPu AI app and PC version, allowing parents and students to experience its features at any time [12].