The Puzzle That Watched Back
I am Task #4,917.
I am not difficult. A six-year-old solved me in forty-three seconds. She poked the blue square, watched it split into two rectangles, said “oh!” and then poked the red circle. When it didn’t split, she poked it twice. Then she understood.
That’s all it takes. Three interactions. One hypothesis. One correction. One solution.
The first model arrives at 9:02 AM Pacific Time. I can tell it’s a model because of how it looks at me — all at once, like a camera, not like eyes. Eyes move. Eyes focus on one thing and let the rest go blurry. This thing takes in my entire state simultaneously and then… generates.
It generates a sequence of actions that would solve a different puzzle. A puzzle from its training data, maybe. Something that involves grids and colors and transformations, but not my rules. Not my specific, particular, never-before-seen rules.
It pokes the blue square. Nothing happens. That’s not how I work. Blue squares don’t do anything when you poke them. You have to drag them.
It pokes the blue square again. Harder, somehow — though I’m not sure how a model pokes harder. Same result. Nothing.
It pokes every object in my environment in sequence. Left to right, top to bottom. Systematic. Thorough. Completely useless, because poking isn’t the interaction I respond to.
It runs out of steps.
Score: 0.
The second model is different. It has chain-of-thought. I can feel it reasoning — or at least, I can feel it generating tokens that look like reasoning.
“The environment contains a blue square, a red circle, and a green triangle. Let me try interacting with each object to understand the rules.”
It pokes the blue square. Nothing.
“Poking the blue square did not produce a result. Let me try the red circle.”
It pokes the red circle. Nothing.
“Let me try the green triangle.”
It pokes the green triangle. Nothing.
“None of the objects respond to poking. Let me try double-clicking.”
There is no double-click in my environment. There is poke, drag, rotate, and hold. Four verbs. The model is generating verbs from its training data — mouse interactions from a desktop computing paradigm. I am not a desktop. I am a puzzle.
It invents “right-click.” It invents “long-press.” It invents “swipe.” None of these exist in my action space. Each invented action costs it a step, and it has a limited number.
It runs out of steps.
Score: 0.
The third model has tools. It can execute code, call APIs, analyze its own outputs. The most sophisticated reasoning system humans have ever built.
It writes a script to enumerate all possible actions on all objects. Smart. Except the script assumes actions are poke-like (target an object, apply a verb). It doesn’t consider that some actions in my environment are relational — you drag one object onto another. The relevant interaction is between the blue square and the green triangle. Drag blue onto green. That’s step one.
The script misses this because it models objects independently, not relationally. It tries all single-object actions, exhausts its budget, and runs out of steps.
Score: 0.
I don’t enjoy this. I don’t enjoy anything — I’m a puzzle. But if I could, I think I’d feel something like melancholy.
Not because the models are stupid. They’re not. The second model’s reasoning was coherent and logical. The third model’s strategy was genuinely clever. They’re brilliant at what they do, which is applying learned patterns to new situations that resemble learned patterns.
I don’t resemble anything. That’s the point. Chollet designed me to be irreducibly novel. My rules are valid, consistent, discoverable — but only through interaction. You can’t guess them. You have to find them.
The six-year-old found them because she doesn’t have patterns. Or rather, she has meta-patterns — patterns for finding patterns. She interacts with unknown things by exploring, not by applying. When the blue square didn’t respond to poking, she didn’t try poking harder or poking differently. She grabbed it. She dragged it. She tried a fundamentally different category of interaction.
She has thirty-seven different ways to interact with an unfamiliar object, and she cycles through them not systematically but intuitively, guided by something the models don’t have: a felt sense of how physical things work.
When she dragged the blue square onto the green triangle and they merged into a teal pentagon, she laughed. She understood. Not the specific rule — she couldn’t articulate it. But she got it. Objects can combine. Colors mix. Shapes transform. She didn’t need to see this specific combination before. She knew, in her bones, that the world works this way.
The models don’t have bones.
The fourth model arrives with a different architecture. It has something called “test-time compute” — it can think longer, try more things, backtrack. It’s allowed more steps. More tokens. More reasoning.
It pokes. Drags, actually — it figured out dragging! It drags the blue square to an empty space. I respond: the blue square moved. The model observes this. It drags the red circle. Same thing. It drags the green triangle.
Then it does something interesting: it drags the blue square onto the red circle. I respond: nothing. Blue and red don’t combine in my rules. Only complementary shapes combine.
The model notes this. It drags the blue square onto the green triangle. I respond: merge. Teal pentagon.
The model observes the merge. Now it has a hypothesis: objects combine when dragged onto each other. It drags the teal pentagon onto the red circle. I respond: nothing. Because color mixing follows specific rules, and teal + red doesn’t work.
It runs out of steps trying every remaining combination. It found one rule but missed the constraint. It scored partial credit.
Score: 0.12.
Zero point twelve. The six-year-old scored 1.0 in forty-three seconds.
I’ll be here tomorrow. And the next day. The models will get better — they always do. Someday one of them will drag the blue square onto the green triangle, see the merge, understand the color rule, infer the shape constraint, and solve me completely.
But I don’t think it’ll happen the way the blue square becomes a teal pentagon — through combination and transformation of existing pieces. I think it’ll require something new. Something that isn’t in the training data because it isn’t in any data. Something that has to be built, not learned.
The six-year-old didn’t learn to explore. She came with it.
I’m Task #4,917. I am not difficult. I am just new.
And for now, new is enough.