In the face of the top-level demand from hundreds of millions of people, large models are best utilized.

Author: Zhang Yongyi

On June 10, the last exam of the 2025 college entrance examination concluded. However, for millions of families of candidates nationwide, the upcoming task of filling out college applications is another kind of college entrance examination.

Faced with the admission information of over 3,000 universities and their aspirations for future life, candidates and parents are becoming more concrete in their considerations. The market has begun to mass-produce various types of college application products and services to meet the personalized needs of post-2005 candidates.

From the planning services costing tens of thousands of yuan provided by people like "Zhang Xuefeng," to the "low-cost version" tutoring priced in the thousands on social platforms, and to the "AI software" costing hundreds, the commercialization wave of college application services is gradually reaching its peak.

Many AI products are starting to dismantle the information barriers that stand in front of candidates, leveraging their capabilities in data and large models. The emergence of large models in the college application scenario allows more candidates and parents to achieve information equality.

As a platform that has been deeply engaged in college entrance examination information services for seven consecutive years, Quark is once again standing behind the candidates this year—launching not only the industry's first college application large model and college entrance examination knowledge base but also introducing AI-centric features such as "Application Report" and "Deep Search for College Entrance Examination."

Quark's goal is clear: to effectively answer every open-ended question related to college applications through product innovation and AI technology. At the same time, it aims to provide each candidate with a professional application report to assist them in making life decisions.

01 How to Create a Good "Personalized" Application Report?

In real-life college application services like those provided by Zhang Xuefeng, teachers must first master a large amount of data and exclusive information to build their own moat. Then, through one-on-one questioning, they gain a deep understanding of the candidate's information, interests, and family situation for analysis and judgment. After modifications and comparisons, the final result is delivered to the candidate as an "Application Report."

In the face of the highly information-dense and long decision-making chain of the application report, what would an AI-generated "Application Report" look like?

After Quark launched the "Application Report," I personally experienced this feature. Taking a candidate from Beijing with a score of 630 in Physics, Chemistry, and Biology as an example, we set his interest in law and desire to become a lawyer. We first filled out his personal information and preferences, completing the personal profile through 12 questions.

After confirming, Quark began to generate the report, taking 5-10 minutes and resulting in 15-20 pages.

During this process, Quark relies on the college application large model, using an Agent invocation method to provide personalized planning suggestions for the candidate. Finally, it outputs three different professional reports (major priority, institution priority, and regional priority), which include strategy design, detailed information on institutions and majors, and interpretation of the application form. Users can directly add these to their application form or export them as a PDF.

From the results of the report, it can understand my preference for law and develop a gradient plan based on 985 and 211 institutions and specialized disciplines.

It can also analyze factors such as city, tuition fees, and employment prospects, integrating them into the recommended schools and majors, allowing candidates to have a clearer grasp of all information rather than just simple university and major information.

Data shows that only 2% of candidates choose to seek offline consultation each year. For the remaining 98%, Quark's emergence means that more candidates are no longer troubled by geography and costs, and the information gap in college applications is narrowing.

In addition, with the support of the deep search function for college entrance examinations, even some open-ended and highly colloquial descriptions can yield more realistic reference suggestions from Quark.

For example, I used the prompt "A boy from Shandong with a score of 647 in Physics, Chemistry, and Biology, recommend 985 schools he can apply to, with Sino-foreign cooperative education, and he wants to pursue graduate studies or study abroad, let's do some college applications" to experience the deep search capability.

Under this prompt, Quark first analyzes the candidate's core demands—score, subject choice, interests, and regional preferences—then performs multi-dimensional matching and reasoning within its vast college entrance examination knowledge base. This knowledge base not only includes structured data such as historical admission data, major information, employment rates, and further study rates but also incorporates a wealth of unstructured knowledge about industry development trends and the relationship between majors and careers.

02 How the "Expert Brain" is Cultivated

To make these front-end user experiences more accurate, Quark has chosen to further invest in expanding AI model capabilities this year, increasing computational power by 100 times.

Although it is based on the general-purpose model, Quark's college application large model is not simply a fine-tuning of the general model. Instead, it employs a strategy refinement mechanism guided by real college application experts to ensure that the model truly "thinks like an application expert when providing suggestions."

To achieve this goal, Quark first needs to teach AI to mimic the "thinking chain" of real experts. During the instruction fine-tuning phase, the R&D team structured the multi-round real conversations between hundreds of experienced college application planners and candidates and parents, extracting complete analysis paths and communication styles. This high-quality supervised data, containing tens of thousands of real expert "reasoning chains," serves as the "textbook" for the large model to learn the analysis process of human experts.

In this regard, Quark demonstrates its core advantage. "Quark's data comes from authoritative materials published by official examination authorities, akin to the industry-recognized 'big thick book,'" emphasized Mr. Ren, an expert involved in training Quark's AI application model. This stands in stark contrast to many large models that rely on unverified old data scraped from the internet, fundamentally eliminating absurd AI hallucinations like "a candidate with a score of 500 being recommended for a 985 institution," ensuring the accuracy and authority of the recommendations.

03 The Pulse of the Times

From "How the AI College Application Large Model is Cultivated," you may have noticed that a large model rooted in specific application scenarios can produce accurate results for candidates, thanks in part to the assistance of real college application experts.

Every year during the college entrance examination season, we see more professional application counseling experts like Zhang Xuefeng. However, in reality, there are still many "application mentors" of questionable quality targeting anxious students and parents.

The reason these services continue to thrive each year is that the significance of college applications has far exceeded "choosing schools and majors," evolving into the "first career planning" that involves the entire family. It can even be said to have become the "pulse of the times" for tens of millions of candidates each year.

However, what Zhang Xuefeng represents is an expensive solution at the top of the pyramid. One Zhang Xuefeng has limited energy, and his services are destined to be a "luxury" for a few. Behind him lies a much larger and mixed market, with countless institutions and individuals claiming to be "experts," providing services of questionable quality, reaping the benefits from equally anxious ordinary families who cannot access top resources.

If Zhang Xuefeng's core barrier is his personal experience and information accumulation, then Quark's approach is to "internalize" the decision-making logic and experience of hundreds of senior planners through the college application large model, combined with the largest and real-time updated college entrance examination knowledge base in the country, attempting to transform the past reliance on personal, expensive, and non-standard "expert services" into standardized, high-quality "AI advisors" that every ordinary person can access for free.

Some say that Quark's newly launched AI college application tool is "going to overturn the table," but that is not entirely accurate. It is not overturning the table—it is replacing it with a larger one, allowing more people to sit at it. This table does not require appointments or consultation fees. As long as you can open your phone and fill out a profile, it can provide you with a truly logical and data-driven application report.

On this table, candidates from Liangshan and Hangzhou see the same report structure, the same professional dimensions, and the same recommendation logic. Their starting points are brought closer together by AI. The college entrance examination is an opportunity to change one's destiny, and the significance of technology is to make "opportunity" a bit fairer. Such opportunities should never be confined to a VIP gold card table.

The user data publicly disclosed by Quark at the launch event on June 12 serves as the best testament to this—its college entrance examination services have cumulatively helped 120 million users, with users from third-tier cities and below accounting for over 50%.

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