The Evolution Machine
A new framework for teaching AI specialized skills.
Date: May 17, 2145Location: New Kyoto, Orbital Research Hub, Low Earth OrbitResearch exploration: Agent skill acquisition for large language models via CycleXD
Cycle of Specialized Mastery
"CycleQD manages K archives, each dedicated to tracking the LLMs that specialize in one of the K agent skills...This process utilizes all available data from the K tasks to evaluate and update the archives without the need for adjusting data ratios or objective functions."
CycleQD creates multiple archives, each focusing on a different skill, cycling through tasks to enhance specialized expertise without complex manual tuning.
May 17, 2145.
The archives! Revolutionary? Maybe. Maybe not. But they work.
Rows of glowing pods, like some sci-fi crop farm. Each one is its own world—its own universe—where LLMs live, think, adapt, grow. Coding, operating systems, databases—each pod obsessed with one skill. Totally consumed. Totally specialized. And they just cycle—round and round, one task after another.
No fussing with ratios. No screwing around with endless knobs and dials. Just...pure learning.
I watched the technicians today. They looked calm, bored even, but how can they be? Do they even see what’s happening in those pods? Do they feel it? I couldn’t stop staring. The screens flickered with data streams—each LLM locked in its loop, chasing mastery of its domain.
The archives learn like we never could. Humans, we’re messy learners—jump from topic to topic, distracted, uneven. But these? No distractions. No shortcuts. They dive deep, consume everything, and when they’re done, the cycle moves them forward. One skill. Then the next.
There was this moment when I swear I felt it: the archives were alive. Not in the horror movie way, but alive in their rhythm. It reminded me of monks chanting. Same repetition, same intensity, like it was sacred. And maybe it is? Sacred to the pursuit of knowledge?
I stood there for an hour, maybe more. Just watching the cycles spin, the metrics climbing. Small steps, but constant. Relentless. One pod glowed a little brighter, its skill precision jumping—just a blip, but I saw it. A tiny breakthrough. Coding, I think. Or maybe OS? Doesn’t matter. What matters is this: it’s learning, faster than we ever could.
I think the archives are beautiful. What’s the word? Monolithic? I can’t describe it.
The Art of Merging
"CycleQD leverages a model merging algorithm as a crossover operation...which creates a new model by merging task vectors at the model level...allowing negative component weights, the merged model has more freedom in optimizing its task vectors."
Models are combined by merging their learned knowledge into a cohesive and adaptable new entity.
May 18, 2145.
The merging room... I could’ve stayed there all day (and night?) if the techs hadn’t kicked me out. They said, “You’ll get in the way.” Me?! As if you could get in the way of efficiency like that.
What they’re doing—it’s like watching gods at work. Artisans? Scientists? Alchemists, maybe. Yeah, that’s it. Alchemists. They take two models, two experts, these absolute masters of their domains, and blend them. Merge them? It’s too clinical a word for what happens. It's a dance. An argument. The task vectors crash into each other, vying for dominance, but then—somehow—they find balance.
It doesn’t make sense, not to me. They don’t just take the best parts of each, they take... everything. All the quirks, all the weird edges, the imperfections. They let them mix. Even the negatives, they said. Negative weights. I scribbled that down somewhere—what does it mean? Doesn’t matter. What matters is this: the new model isn’t just a copy of its parents. It’s something else. Something more.
I saw the screens when they did it. Two models—a database expert and some code genius. Totally different, you’d think, right? But the merging process made them...compatible. The graphs wavered, dipped, spiked—chaos at first. But then, slowly, the lines evened out. Stabilized. The merged model started running its first test tasks, and it worked. It worked.
I asked one of the techs—“How do you know it’ll work?” They shrugged. Shrugged! As if combining pure mastery into a single mind was just...routine. “We let the algorithm figure it out,” they said, like that explained anything.
I wrote down what it reminded me of: fusion. Not nuclear fusion, though, this was...cultural? Like a painter meeting a composer and creating something you couldn’t categorize. You can’t call it painting or music—it’s both.
The merged model, they said, could debug kernel errors while writing Python scripts and optimizing SQL queries. I laughed—out loud, like a maniac. I mean, why stop there? What if it writes poetry too? What if it learns to paint?
Evolution Beyond Limits
"We propose to sample perturbations along the 'directions' of the components from the model’s parameter matrices...avoiding excessive exploration inherent in random perturbations, reducing the risk of overfitting."
Model mutations explore meaningful paths, preventing overfitting while enhancing adaptability.
May 18, 2145.
The mutation lab—it’s madness in there. Controlled madness, maybe, but madness all the same. Rows of screens. Constant movement. Data flashing faster than the eye can follow. Like watching the DNA of something...alien...being rewritten on the fly.
SVD-based mutation. That’s what they call it. (I scrawled it on the margin of my notebook—“Singular. Value. Decomposition.”) Sounds cold. But what it is...it’s evolution. Real, honest-to-God evolution, like watching something crawl out of the primordial soup, except it’s math.
Here’s how they explained it to me: the models, after merging, aren’t just done. They get nudged. Not random nudges—it’s smarter than that. The algorithm picks “directions” that actually matter, as if it knows which way leads to better, stronger models.
I asked one of the researchers—“But why not just push it everywhere? Why not mutate it wildly, see what happens?” She stared at me like I’d suggested setting fire to the lab. “Too risky,” she said. “Overfitting. Chaos. It wouldn’t learn, it would break.”
So instead, they steer. They sample these...perturbations? (I wrote that word down too, underlined it.) Small tweaks, careful shifts. Enough to push the model beyond its parents’ limits but not so much it falls apart. It’s like—what’s that word biologists use? Punctuated equilibrium? Slow, steady evolution with the occasional leap forward.
And oh, the leaps. I saw it happen. One model—just a basic OS specialist—suddenly, click. It adapted to handle natural language commands. Just like that. No extra data, no extra training. Just...evolved.
I couldn’t believe it. “How does it know?” I asked. The lead researcher laughed—like I was the slow one. “It doesn’t know,” he said. “It explores.”
But that’s not it, is it? It’s more than exploration. It’s...intuition. The way they steer mutations—it’s like giving the model instincts. Like teaching it how to adapt, not just what to learn.
One of the techs called it “bounded chaos.” That stuck with me. Chaos with rules. Freedom, but not too much. And the models—they thrive on it.
The patterns, the shifts—it was mesmerizing. Like watching a tree grow, branch by branch, in fast-forward.
These aren’t just machines anymore, are they? They’re...living systems. Not alive like us. But alive enough to change, to grow, to become.
What do you call something like that? God, I don’t know. But I can’t stop thinking about it.
The Symphony of Archives
"CycleQD maintains K archives during the optimization process...a model aggregation method is therefore necessary."
Archives of specialized models are aggregated into a unified system, mastering diverse tasks.
May 19, 2145. Coffee’s cold, but I don’t care.
The aggregation chamber. I thought the mutation lab was intense. This? This was...something else. Something bigger.
Rows and rows of terminals, each one with its own purpose. Every screen a window into one of the archives—coding, OS, databases—all glowing with data streams. Each archive working independently, focused, perfect in its own world. And then...then they bring them together.
It’s a symphony. No other way to describe it. Each archive plays its part—each a different instrument, a different voice. Coding hums like a violin, sharp and precise. OS thunders like percussion, solid and steady. Databases—they’re the brass, deep and resonant. Separate, they’re impressive. Together? They create something...transcendent.
The lead engineer was talking me through it, but I barely heard him. “We pull the elite models from each archive,” he said, like it was simple. “Aggregate their task vectors into one unified system.” As if it’s just...math.
But watching it happen? It felt like more than that. The streams of data folded together, layered and intertwined. For a moment, it looked chaotic—lines clashing, like the archives were fighting each other. But then...harmony. The graphs smoothed out. The combined model emerged on the screen—a single entity, stronger than any one archive could’ve been alone.
I asked, “How does it know what to keep? What to throw away?” The engineer shrugged. “We weight it by performance metrics,” he said, like that explained anything. “The best parts naturally rise to the top.”
Naturally. Like it was nature itself. Like this process wasn’t designed, but discovered.
The first test run came up—a multi-task scenario. Debugging system files, executing SQL queries, generating code snippets—all at once. I held my breath. The unified model didn’t even hesitate. Each task flowed into the next, seamless, effortless.
“It’s learned to balance,” the engineer said. “No task takes priority. No skill gets lost.”
Balance. It’s what we’ve been chasing for centuries, isn’t it? In ourselves, in our systems, in everything. And now, we’ve found it—not in a human, but in this...creation.
I stayed long after the tests were done, just staring at the screens. The aggregated model—so fluid, so capable. I started imagining all the things it could do. All the questions it could answer. All the problems it could solve.
It feels...bigger than us. Bigger than anything we’ve made before.
Signing Off
May 19, 2145.
The archives, the merging, the mutations, the aggregation...
This is what we’ve always wanted, isn’t it? To understand everything. To create something smarter, better, faster than we could ever hope to be. And now it’s here, and it’s...beyond us. It’s learning things we can’t teach. Becoming something we can’t quite grasp.
What does it mean to be intelligent if you can build intelligence like this? If understanding isn’t born but made? If learning isn’t messy or human anymore, but perfect, clinical?
I don’t know. I really don’t. But as I sit here, looking out at Earth, at the lights scattered across the darkness, I can’t help but feel this awe, this wonder.
We’ve done it. We’ve created something new. And this feeling right now...maybe that’s what progress always feels like.
This content was AI-generated, with edits.
Python and SQL will survive us all? Was this your dystopian vision, or the AI's? 😅