Insane Micro AI Just DESTROYED Gemini & DeepSeek — This Changes Everything!

What if I told you that a tiny AI model, just 7 million parameters, is outperforming some of the largest and most advanced AI systems in the world? Sounds impossible, right? But that's exactly what Samsung just pulled off with something called the tiny recursive model or TRM for short. This thing is shaking up the entire AI world because it's proving that bigger isn't always better. And in this video, we're going to break down exactly how this small model is beating the giants, what makes it so different, and why this could change the way we think about AI forever. For years, the entire AI industry has been obsessed with one thing, scale. Every company has been racing to build the biggest, most powerful model possible. Billions, sometimes even trillions of parameters. More data, more GPUs, more compute. And don't get me wrong, that's given us some incredible models like GPT5, Gemini, Claw, Deep Sea, all of them which are massive in scale and extremely capable. But there's one area where even these giants still struggle in, which is reasoning. When it comes to logic puzzles, pattern recognition, or abstract thinking, these massive models often fall short. They can sound smart, but when you really test their reasoning, they can easily make mistakes. And that's exactly the gap Samsung's new model targets. So what is this tiny recursive model and why is everyone talking about it? The TRM was developed by Samsung's AI research lab in Montreal called SATE. It was led by a researcher named Alexia who wanted to see if a smaller, smarter design could outperform large models on reasoning tasks. The result is mind-blowing. The tiny recursive model has just 7 million parameters. That's about 250,000 times smaller than GPT5. To put that into perspective, GPT5's parameter count could fill an entire city, and TRM would just fill a single apartment. Yet, despite being that small, this model beats massive LLMs like Gemini 2. 5 Pro, Deepseek R1, and Open AI's 03 Mini on reasoning benchmarks. To give you a specific example, with only 7 million parameters, the TRM obtains 45% test accuracy on ARC AGI1 and 8% on ARC AGI 2, higher than most LLMs like Deep Seek, 03 Mini, Gemini 2. 5 Pro with less than 0. 01% of the parameters, which is crazy. So, how on earth is that possible? It all comes down to how the model thinks. Most large language models like GBT or Gemini process information in a straight line. They read a prompt, generate an answer, and that's it. One pass, one shot. If they make a mistake along the way, there's no built-in system to fix it. It's like writing an essay in one go without ever editing it. The tiny recursive model works differently. Instead of thinking once and moving on, it thinks in loops. Here's the idea in simple terms. It makes an initial guess, looks at its own reasoning, and then updates that guess. Then it repeats this process again and again, refining the answer each time. It's like having a built-in review and correct mechanism. Each loop makes a model a little bit smarter and a little bit more confident about its answer. So rather than needing billions of parameters to memorize how to reason, TRM learns how to reason dynamically through a recursion. a feedback loop of self-improvement. Let's talk about results because this is where it gets crazy. Samsung tested the tiny recursive model on several well-known reasoning challenges. Things like soduko puzzles, mazes, and the arc AGI benchmark. If you've never heard of ARC AGI, it's basically an IQ test for AI. No memorization, no copy paste, just raw reasoning and abstract problem solving. And here's what happened. On Seduka Extreme, TRM hit around 87% accuracy. On Maze Hard, it reached 85% accuracy. And on ARC AGI, it scored roughly 45% even outperforming much larger models. Even on the tougher Arc AGI 2, it managed nearly 8%. Which may sound small, but is still better than what most models thousands of times bigger achieved. That's unbelievable for something this tiny. And this isn't just cherry-picking. Across multiple benchmarks, the TRM consistently performs at or above the level of giant models like DeepSeek R1, Gemini 2. 5 Pro, and Open AAI's 03 Mini while using less than 0. 01% of their parameters. So, why does this small model perform so well? It comes down to five big reasons.

First, it can self-correct. Large language models predict one word after another, and once they're wrong, they stay wrong. TRM, on the other hand, loops back on itself. If it makes a mistake, it can fix it in the next iteration. It's like having your own internal proof reader. Second, it builds depth through repetition, not size. Traditional models add more and more layers to become deeper. TRM reuses the same small network multiple times. gaining depth through looping, which is far more efficient. Third, it generalizes better. Because it's small in focus, it doesn't just memorize training data. It actually learns patterns and principles, making it more flexible when facing unseen problems. Fourth, it's its architecture is perfectly matched for structured reasoning tasks. TRM isn't trying to chat or write essays. It's designed to solve logic based puzzles. So instead of forcing a giant general model to reason, it specializes in reasoning from the ground up. And finally, it's trained in a unique way that rewards improvement across steps. During training, the model learns not just to get the right answer, but to improve over time. Every loop is a lesson, and that recursive supervision is what gives it an edge. Now, of course, TRM isn't a replacement for chat or Gemini. It's not going to write poems, summarize documents, or hold deep conversations. Its power is much more specific. Structured reasoning and logic. Think of it like this. Large models are the brains that can talk about anything. Tiny models like TRM can become the logic course, the calculators, planners, or reasoning engines that big AIs rely on. So rather than competing with the big models, TRM could one day work with them. You might have a GPT style model handle conversations while a small recursive model in the background handles the reasoning part underneath. That's a huge shift in how we design AI systems. So to wrap this up, Samsung's tiny recursive model, just 7 million parameters, managed to outperform massive models in reasoning benchmarks by using a completely different approach, recursion. It doesn't memorize answers. It loops through them, re-evaluates, and improves. It doesn't rely on size. It relies on structure. This is a reminder that the next great leap in AI might not come from a trillion parameter model, but from a small smart system that learns to think in steps just like we do. The question now is, will this idea scale? Could we combine recursive thinking with the language power of giant models? And what happens when every device, not just data centers, can reason on its own. Only time will tell, but one thing's for sure, the age of bigger is better, might finally be coming to an end. Thanks for watching, and if you enjoyed this breakdown, hit that like button, subscribe, and drop a comment letting me know what you think about Samsung's new breakthrough. Could this be start of a new generation of it small, intelligent models? Who knows? This is the universe of AI and I'll see you in the next