Decoding Attention and Emergence in AI

Arshavir Blackwell, PhD

Today I want to dig into a specific puzzle: how do attention heads actually learn what to look for? If you open up an attention head and look at the math, you see three weight matrices—Q, K, and V. Query, key, value. They're just matrices of numbers. But somehow, in practice, they end up doing things like matching "France" with "is" in "The capital of France is..." so the model can output "Paris."

Arshavir Blackwell, PhD

Here's what's interesting: the model isn't told what a country is. There's nothing in the training objective that says "make your queries represent what you need and your keys represent what you offer." That's a metaphor we impose, not an instruction. No one wrote code saying "build a country-name detector." And yet, that's what emerges.

Arshavir Blackwell, PhD

What happens is that during training, the model keeps adjusting weights to minimize prediction error. If the Q vector for "is" ends up pointing in a direction that aligns with the K vector for "France," their dot product goes up, attention flows that direction, and "Paris" gets boosted in the output. The pattern persists because it reduces mistakes. That's the whole story at the mechanical level.

Arshavir Blackwell, PhD

Neither the query nor the key vector means anything intrinsically. They're coordinates in high-dimensional space. There's no requirement that "country-ness" clusters together, or that similar queries are neighbors. The attention head survives if its pattern works. If there were a more efficient but totally uninterpretable way to solve the same problem, the model could have done that instead. So why don't we see that?

Arshavir Blackwell, PhD

This is where constraints matter. The architecture only allows the model to learn things expressible through query-key matching. That's the mechanism available. You also have shared word embeddings, pressure to solve many tasks with the same weights, efficiency constraints from model size. These push learning toward reusable, general patterns rather than one-off solutions.

Arshavir Blackwell, PhD

There's a useful analogy here to natural selection, though I want to be careful not to overextend it. In evolution, structures persist because they're rewarded by reproductive success. No designer planned an eyeball; it accumulated because each step improved survival odds. In neural networks, structures persist because they reduce prediction error. No designer planned "Q means what's needed." The arrangement just worked, so it stuck.

Arshavir Blackwell, PhD

What we actually observe is that interpretable patterns emerge—heads that track syntax, heads that recall facts, heads that check agreement. Part of this is the architecture constraining what's learnable. Part of it is the pressure for efficiency: better to compress many patterns into few heads than to have specialized circuitry for every task.

Arshavir Blackwell, PhD

The "what I need" and "what I offer" metaphor is useful as a mental shortcut, but it can mislead. The vectors don't know they're "offering" anything. Sometimes a clean conceptual mapping doesn't exist. All that's actually happening is a dot product. That's the mechanism—matching numbers to get answers right.

Arshavir Blackwell, PhD

This connects to something Elizabeth Bates and her Competition Model argued in the 1980s about human language processing. Her view was that the brain doesn't run explicit rules like "subject before verb." It's weighted cues competing to explain the input. You hear a sentence, patterns fight it out, the one with most support wins. There's no lookup table.

Arshavir Blackwell, PhD

Attention works similarly. Nothing is written down saying "queries mean needs, keys mean offers." These are weight matrices shaped by experience that happen to produce outputs we can recognize as meaningful. Meaning isn't stored; it's what happens when learned patterns collide with new data and produce something useful.

Arshavir Blackwell, PhD

This raises a philosophical question I keep returning to: if meaning isn't a thing you store but something a network enacts when the right inputs align—does that change how we think about understanding? About intelligence? It blurs the line between machines and brains. Neither is magical. Both are layers of number-crunching, forced toward useful patterns by constraints and optimization pressure.

Arshavir Blackwell, PhD

So as we keep opening these systems up—whether cortical circuits or transformer attention—it's less about finding a treasure map to meaning and more about watching how random-looking math ends up acting like reasoning. The behavior is where the interesting questions live.

Arshavir Blackwell, PhD

I'm Arshavir Blackwell, and this has been Inside the Black Box.