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Imagine for a second, that it is possible to create, well, let's say, a fully functional interactive website with a very cool design. And all this with the help of one single text command. I'm not talking about a simple page, but about a complex interface with animation, with effects, so that it looks like a team worked on it for weeks. It sounds, of course, like science fiction, but, judging by the sources we are analyzing today, it is already quite a reality. Today we are diving into the analysis of a new artificial intelligence model. It is called K2C5. And our task is to figure out what is so special about it. We will look at its, as they say, unique swarm-agent architecture, study the test results, and try to understand why there is already, so to speak, a whisper in the industry that this is a serious challenge to the giants. Let's get to it. >> Yes, and it's probably worth starting here with the key difference. We are used to the fact that most models are like one, albeit a brilliant employee. He receives a task, well, and performs it step by step sequentially. Mi K2 works completely differently. At its core is what is called a self-guided swarm of agents architecture. Swarm of agents sounds impressive, something straight out of science fiction. But what does it mean in practice? >> In practice, it means that one large main model, it acts not as an executor, but rather as a project manager. Having received a complex task, it does not solve it itself, it instantly creates up to a hundred highly specialized sub-agents. One expert in design, another in JavaScript, a third in testing, a fourth in searching for information on the web. And most importantly, they do not work in turn, they work simultaneously, in parallel. >> So it's no longer one genius, but a whole team. A concrete project in fractions of a second. >> Okay, so that it doesn't sound too abstract, let's go back to that very example with the website. This is not a hypothetical case, right? But a real experiment described in one of our sources. The model was given a very, very complex request. To create a single HTML file for an interactive dashboard that compares different models. >> Uh-huh. And the request was very detailed. >> Yes, there were specific requirements. For example, a glassmorphism style design. >> That's a kind of frosted glass effect. Yes, >> exactly. Plus a professional dark theme, smooth animation, interactive elements. That is, it was not just about making a page, but about creating a product that looks like a ready-made commercial SaaS solution. And what was the result? >> And the result is that the model coped. The result in the source is described as visually polished, responsive, and, quoting, with a real wow effect. All elements were in their places, everything worked as it should. The author even writes that the interface looks sublime, which for generated code is, well, an incredibly high rating. And all this with a single command, honestly, it's hard to believe. How is this even possible from a technical point of view? What is the mechanism there? >> And here we come to the most interesting part. At its core is a technology that can be compared, perhaps, to the work of a brilliant improvising conductor. Technically, it is called PAL. The key innovation here is a trainable orchestrator. Usually, in such systems, there is a rigid scenario. First, we do this, then that, but here the orchestrator looks at the task, say, create a website, and instantly decides. So, for this, I need one designer agent, three programmers, and one tester. For another task, it will assemble a completely different team. It does not follow instructions, it creates them on the fly. That is, it doesn't just speed up the process, it reinvents it every time. >> Exactly. And it's incredibly effective. The system is capable of coordinating up to one and a half thousand calls to different tools simultaneously. Thanks to this parallel execution of tasks, the total working time is reduced. Well, tests show up to 4.5 times compared to the sequential approach. >> 4.5 times, >> yes. And what's important, it's not the total number of steps of all agents that is measured, but the path of the slowest one. This guarantees that we see a real acceleration of the entire process, from start to finish. >> Listen, creating such a website with one command is impressive, of course, but in the world of AI, it's always a race. It's one thing to have a beautiful demo example, and quite another to have dry numbers in tests. How does Kim K2C5 compare to, well, titans like Cloud or GPT? >> And here, judging by the sources, everything is very decent. >> Yes. >> Yes. Benchmark results show extremely high performance in tasks related to coding, video and image processing. The model particularly stands out where so-called agent behavior is required, >> that is, autonomous search and analysis of information. >> Absolutely. For example, in the Browse Camp and Wild Search tests. And the most important thing is that in these tests, Kimi K4.5 not only surpassed its already standard version, but also, as claimed in the source, crushed the performance of the ClOPUS 45 model. Wait, crushed. That's a very strong word. Usually, in such comparisons, we're talking about a few percent advantage, no more. Is there really such a big gap here? >> Exactly. The source emphasizes that in these specific tests related to searching and synthesizing information from the web, the gap was huge, and this is a very serious claim. >> The question arises: at whose expense? What's under the hood? >> Well, such power, of course, is due to the architecture. It's a so-called mixture of experts with a total size of 1 trillion parameters. This puts it on par with the biggest and most powerful models available today. >> A trillion parameters. It sounds like it needs a whole power plant to run. Isn't that astronomically expensive? >> And here lies the real economic miracle of this architecture. Although the model's total knowledge base is indeed a trillion parameters, for any specific request, it uses only a small, highly specialized part, just 32 billion. >> And how does that work? >> Imagine a huge library the size of a city. But instead of turning on the lights in the whole building, you have a smart flashlight that instantly illuminates only the necessary shelf. You get access to all the knowledge, but you pay for electricity as if for one small room. >> Excellent analogy. So, this solves the main problem of giant models, their monstrous cost and slowness. >> Absolutely. This allows the model to have the power of a trillion-parameter system, but at the same time require resources comparable to a much smaller thirty-two-billion-parameter model. If we delve into the details available in the source, the model has 61 layers. On each layer, there are about 384 experts. And for processing each unit of information, a token, the system selects the eight most suitable ones. This approach allows for both scale and amazing efficiency. >> This explains how it handles one, albeit very complex, task. But the author of one of the articles decided to go further and put the model through a real stress test. He described a second, even more ambitious experiment. The researcher tried to give Kimi K2C5 a task of a completely different scale. To create a comprehensive cultural intelligence report for the fifty most common languages in the world. >> Fifty. So the idea was to check if the model could simultaneously launch 50 independent research streams. Each in its own language, and then bring everything together. It sounds like a task for an entire research institute. >> Exactly. And the experiment failed. At least, not as planned. It was not possible to conduct the test in swarm-agent mode. >> Why? Due to a purely practical limitation, the user simply ran out of credits on their account for such a large-scale aggregation, >> which, in general, is also telling, >> yes, but he launched the task in the usual single-agent mode. And here's where things got interesting. Even in this reduced mode, it was evident how the system desperately tried to parallelize tasks from its internal to-do list. It performed searches on the internet in different languages and, notably, corrected its own errors in the process. >> It corrected itself, >> yes. For example, if a search in one language yielded no results, it independently rephrased the query and tried again and again until it found what it needed. >> You know what's striking about this observation? Even a failed experiment, in essence, turned out to be incredibly telling. It's like an X-ray that illuminated the model's own thought process. We saw its internal fundamental tendency to decompose, well, to break down one giant task into many small ones and to work on them autonomously in parallel. >> This is perhaps the most interesting point in the whole story. We are used to judging systems by their successful results. But here, the most telling aspect is failure. It's what showed what's happening under the hood. It's like seeing the drafts of a brilliant writer. Sometimes the process turns out to be even more important and telling than the final result. >> Absolutely. This confirms that the parallel approach is embedded in the very foundation of its logic, and is not just some kind of add-on. This model, one might say, thinks in parallel by its very nature. >> Okay, let's try to sum it up. What do we have in the dry residue? A new AI architecture has appeared, which doesn't just execute commands, but essentially acts as an efficient manager that organizes a whole team of virtual specialists to solve complex problems. Mi K2PU5 with its swarm of agents can take one large goal and break it down into 100 simultaneous work streams. >> And if we look at it more broadly, it's not just about who outran whom in the next benchmark race. We may be witnessing a fundamental paradigm shift in how artificial intelligence will solve complex problems in the future. >> A paradigm shift, >> yes, it's a transition from the idea of one omnipotent, but sequential intelligence to the idea of a coordinated and hyper-efficient team of specialized agents. >> And what could be the consequences of this? >> Huge for many areas. From software development, where complex applications can be created in hours, not months. >> Uh-huh. >> To scientific research, where genome analysis or climate modeling can be done orders of magnitude faster. Any complex problem that can be broken down into parts becomes a candidate for solution using such a swarm. >> And in conclusion, one thought that keeps me up at night. In one of the sources, plans are mentioned for creating a local version of this model that can be run on personal computers. Just think for a second about what will happen when this level of parallel agent computation becomes available not in the cloud services of tech giants, but on the personal devices of each of us. What complex tasks, which were previously beyond the capabilities of a single person, will be solvable right from our desks while our morning coffee brews.