That concludes today's interviews. You may all head back and wait for our notification.

Deng Yonghua nodded slightly after listening. "Such issues do indeed exist in certain industries. My expertise is in cybersecurity, where we typically focus on protecting uploaded data or implementing more complex encryption algorithms during data transmission. However, regardless of how strong the encryption is, there is theoretically always a risk of interception and decryption during transit. Furthermore, the larger the data packets and the longer the transmission time, the wider the window of risk exposure. Given the massive size of modern large models, the risks are even greater. That is why highly sensitive data is still often handled via physical media like optical discs and hard drives. Nevertheless, your approach offers a novel perspective. Please, continue."

"The professor is absolutely right. If edge deployment of AI models can be achieved, it would largely circumvent these data security issues, allowing data to be processed locally without the need for transfer." Zhou Yun flipped the slide. "In reality, there is already significant research in this area, but there is still a long way to go before we can truly run large models on terminal devices like smartphones."

Even the distillation methods detailed in the DeepSeek paper to be published early next year would not yield the results Zhou Yun was aiming for. In fact, with his current extraordinary memory, he could easily reproduce a vast number of as-yet-unpublished top-tier journal papers, including plenty of open-source code. However, he would not do that. The rapid development of computer science is inseparable from one concept: "Open Source." If he were to become a "plagiarist," it would be tantamount to stealing the research results of others. Even the Zhou Yun of his previous life had never done such a thing, let alone now. Besides, with his current intelligence, is it really that hard for him to publish a few top-tier papers himself?

Recalling his wandering thoughts, Zhou Yun continued, "I categorize existing edge AI deployment into three types:
1. Model Compression: This is the most direct and common method. Think of it as forcing a water-soaked sponge to shrink. Primary techniques include quantization, pruning, and knowledge distillation. However, this method has a flaw: model performance tends to degrade.
2. Edge Resource Management and Scheduling: Simply put, this involves intelligently allocating and optimizing the computing power of edge devices—such as servers, sensors, and gateways—to execute AI tasks efficiently with low latency. However, this approach introduces data security and privacy concerns.
3. Adaptive AI Systems: This is a method I have defined myself. It involves designing a framework that allows large models to optimize and shrink themselves for deployment on edge devices. In layman's terms, it is using AI to train AI. This is the core philosophy behind the 'AgileEdge' framework I am proposing.

Of course, the methods I am describing are at the software level; no matter how they are designed, they are ultimately constrained by physical hardware. But in my view, current algorithms are still far from reaching the physical limits of existing hardware."

Hearing this, the four other students participating in the interview were already feeling numb. A face full of question marks was no longer enough to describe their bewilderment. Their minds were collectively echoing three existential questions: "Who am I? Where am I? What am I doing here?" Is this really a non-top-tier university program?

It wasn't just them; even Qiu Yan and Shen Rui were somewhat moved. Although Zhou Yun hadn't yet delved into the specific algorithms, the preceding points alone demonstrated his solid foundation in AI. He must have read a substantial number of papers; otherwise, he wouldn't have been able to answer Professor Deng's first question so capably. The two seniors shared a silent thought: "This new junior is quite formidable."

As for Deng Yonghua, he remained the most composed, having seen many students who joined his research group with their own pre-defined research directions. "AI training AI? That sounds quite interesting, but I imagine it won't be easy to implement, right?"

"Exactly. Complex, AI-driven decision-making processes are inherently 'black boxes,' which is one of the greatest challenges. That is why I designed the AgileEdge framework—to mitigate these issues as much as possible." Zhou Yun tapped the keyboard to flip to the next slide, which contained the heart of his presentation: the AgileEdge framework diagram. While there wasn't a single line of code, convincing the professor of the logic was sufficient.

"This framework is primarily based on Attention—the Transformer architecture. Although it faces the issue of high computational demand, it is undoubtedly the core foundation of all deep learning at this stage. In short, the function of AgileEdge can be summed up in two words: 'Packaging.' What is shown on the screen are the three core components of AgileEdge:
The Perception and State Encoding Layer, which collects and pre-processes raw, multi-modal data from the edge environment;
The AI Collaborative Optimization Decision Engine, which utilizes a hierarchical reinforcement learning architecture to achieve the shrinkage of large models;
And the Execution and Feedback Layer, which packages the downsized large model into the corresponding edge environment.
Ultimately, this achieves the goal of edge AI deployment. Theoretically, any large model can be fitted into an edge device through this process."

Deng Yonghua studied the slide for a while before speaking slowly. "I have a question. Since the AgileEdge framework is highly adaptive, is it possible that during runtime, it might frequently apply extreme compression or aggressive online pruning to the AI model on edge devices to meet short-term performance goals? Over time, could this continuous and potentially aggressive 'training' lead to unpredictable accuracy drift or even catastrophic forgetting when the model encounters complex scenarios or long-tail data distributions that aren't being directly monitored? Furthermore, how does AgileEdge systematically quantify and constrain this potential, cumulative loss of accuracy during the dynamic model 'packaging' process?"

Zhou Yun's brow furrowed slightly. This was indeed an oversight on his part—not due to a lack of intelligence, but simply a lack of research experience. However, this didn't stop him from answering. With his brain running at high speed, he quickly formulated a response. "I have indeed considered that question, which is why I designed a novel metric to evaluate the packaged models. However, because it is not yet fully refined, it isn't shown on the slides." (Don't ask, just assume it exists—even if he hadn't fully thought it through yet.)

Deng Yonghua didn't press him further. After all, Zhou Yun was only a prospective graduate student; there was no need to be overly strict. Based on his current performance, Deng was already quite satisfied. With the core part finished, the rest of the presentation covered practical application scenarios, such as industrial control and healthcare.

"That concludes my presentation. Professor, do you have any other questions?"

Deng Yonghua shook his head. "Let's leave it at that. Qiu Yan, Shen Rui, do you have any questions?" In truth, they still had a few, but as previously noted, there was no need to make things too difficult for a newcomer. The current level of performance was already sufficient.

Hearing their mentor's prompt, the two seniors were startled and shook their heads in unison. "No questions."

Deng Yonghua gave them a long look before saying, "Then let's end the interview here. You may all go back and wait for notification."

Had it not been for the presence of the other four students, he would have announced Zhou Yun's acceptance on the spot. But after a moment's thought, he decided against it.