Core Insights - Quark, Alibaba's AI flagship application, has generated over 10 million professional-level college application reports for students and parents as of July 1, showcasing its significant impact in the college application process in China [1] Group 1: Technology and Functionality - The "Application Report" Agent utilizes deep research technology with capabilities in task planning, execution, checking, and reflection, making it the largest application of such technology in the country [1] - The Agent is supported by Quark's college application model and a specialized knowledge base, providing decision-making capabilities close to that of experts [4] - The system dynamically optimizes application plans through a multi-round mechanism of tool invocation and reflection, adapting to user preferences and constraints [4][17] Group 2: Data Accuracy and Sources - Quark emphasizes the importance of professional and accurate data, having curated a specialized knowledge base from over 8,000 sites, covering approximately 2 billion data points, with 99% from authoritative sources [9][10] - The company employs a combination of algorithmic alignment and manual review to ensure the accuracy of admission plans and historical score lines, leveraging seven years of accumulated data [11] Group 3: User Interaction and Experience - The application process involves a three-part product: a general search capability, a college application tool, and a free application report, aimed at guiding users through the complexities of college admissions [8] - Users input their information, which the system translates into actionable instructions for filling out application forms, ensuring that their preferences are prioritized [15][16] Group 4: Predictive Analytics - The system predicts application trends based on historical data and current admission plans, assessing fluctuations in admission rates and providing users with a dynamic distribution of their application options [13] - The predictive model incorporates various factors, including new programs and schools, to enhance the accuracy of recommendations [13] Group 5: Expert Involvement and Continuous Improvement - The development of the Agent involved training with real-world data from one-on-one interactions between experts and students, ensuring that the model reflects practical decision-making processes [19] - Continuous feedback from users and expert evaluations are used to refine the model, enhancing its ability to provide personalized recommendations [19]