Chapter 232 - 174: So Intense

Chapter 232: Chapter 174: So Intense

He glanced at the video’s stats—it had been posted for less than twelve hours, but it already had over two million views and more than 300,000 likes!

The comment section was blowing up.

[Jesus Christ! Did I really just... watch an entire math lecture on TikTok? I must be insane!]

[Yeah, for me, it was way more exciting than an NBA game that’s half free throws...]

[The moment the applause started, I couldn’t help but hum along with the background music! Hype! So hype!]

[What kind of editing god is this creator? The way the music syncs up is so perfect! It brought tears to my eyes!]

[My mom just asked me why I’m on my knees watching my phone...]

[I’m an art major, and a math video just made me cry? How is that even possible?]

Jason laughed as he scrolled through the comments. He’d never felt this way before—that math could actually be interesting, and all because of a guy from Huaxia.

...

「Meanwhile, at Stanford University.」

The third-floor graduate computer lab in the Gates Computer Science Building.

The lab was still brightly lit late into the night. Three PhD students of different ethnicities were huddled around a single computer, staring at the paused video on the screen.

Seated in the middle was Marcus, a Black student with dark skin and dreadlocks.

To his left was Emily, a white student with blonde hair, blue eyes, and black-framed glasses.

And to his right was Kevin, a male student of Chinese descent with classic East Asian features.

"I can’t believe it..." Emily pushed up her glasses, her voice filled with disbelief. "An undergrad from Yannan University just... solved the problem we’ve been researching for over a year? Just like that?"

"And he solved it so completely," Marcus said, shaking his head with a wry smile. "Look at the algorithm framework he derived. Theoretically, it can be applied directly to all generative models based on the Transformer architecture. You know what that means? It means GPT, Claude, Gemini... all of them can use it!"

Kevin folded his arms, staring at the dense wall of mathematical formulas on the screen. He was silent for a long moment before speaking. "It’s not just us. I just took a look at Twitter. Stanford, MIT, Berkeley, CMU..."

"...practically every research group at a top university studying LLM hallucination rates is freaking out on social media right now."

As he spoke, he swiped through his phone and pulled up a few tweets to show them.

[Some PhD at MIT CSAIL: My advisor told me to finish my research proposal on lowering LLM hallucination rates by today. Now I’m just staring at a blank document with no idea what to write...]

[Researcher at Berkeley AI Lab: Just watched that video. I feel like I’ve spent the last year painstakingly climbing a mountain, only to watch someone land on the summit in a helicopter after a five-minute flight...]

[Scientist at DeepMind: Quick technical post. Regarding the algorithm derived by that student from Huaxia, I did a preliminary analysis of the mathematical principles. The theoretical foundation is rock-solid. I can’t find any angle to attack it from...]

After reading the tweets, Emily slumped back in her chair and let out a long sigh.

"So... what do we do now? Do we even still write our paper?"

Kevin thought for a moment, then slowly shook his head. "We still have to write it, definitely. But our focus has to change."

He stood up, walked over to the whiteboard, and picked up a marker, writing as he spoke.

"The only thing left for us to do is convert the algorithm Chen Lin provided into actual, runnable code."

"First," Kevin said, writing "Step 1: Math to Code" on the board, "Chen Lin’s derivation is purely mathematical. For computer science people, even PhDs, there’s a significant barrier to entry to fully grasp it. We need to translate those formulas into pseudocode first."

"Second," he continued, adding "Step 2: Integration," "Chen Lin’s algorithm was derived from neural network and differential manifold theory. But integrating it into the existing Transformer architecture for practical large-model training and inference... that’s a massive engineering task."

Hearing this, Marcus’s eyes suddenly lit up. "Exactly! If we can be the first to successfully implement this algorithm and prove through experiments that it really does drastically reduce the hallucination rate, that in itself would be an excellent paper!"

"Exactly." Kevin nodded, adding another line to the whiteboard: "Step 3: Benchmark & Validation."

"We can test it on several major open-source models, like LLaMA and Mistral, and compare the changes in their hallucination rates before and after implementing the new algorithm. We’ll create a detailed benchmark."