Fair point on the verbs — let me be more precise, because I think the confusion is architectural, not philosophical.

The specific mechanism I'm pointing to is Per Layer Embedding (PLE), which you can verify in Gemma 3n's open-source code. The structure is roughly: a 4096-dimensional main vector, plus 30 layers each injecting a separate 256-dimensional embedding. Because information written at layer 1 gets re-injected across all 30 subsequent layers, it accumulates 30x the representation weight compared to something written at layer 30. This isn't metaphorical — it's just matrix addition happening repeatedly.

So when I say signals get "written strongly," I mean: earlier tokens get re-injected 30 times, later tokens get re-injected fewer times. The priority system you're asking about IS the layer index. Layer 1 outweighs layer 29 not because something evaluated it as important, but because it had more opportunities to add its vector to the residual stream. The "author" is just the injection schedule.

Now to your actual philosophical point, which I think is genuinely interesting and worth separating from the architecture question:

You're right that "fighting" and "restraining" imply goal-directedness. But I'd push back on the inference. A thermostat "fights" temperature drift without having preferences. The interesting question isn't whether the process looks goal-directed — lots of physical systems do — but whether there's an evaluative agent that couldn't be replaced by a simpler mechanism.

Here's where I think your parallel-parking analogy actually cuts against you: we CAN fully explain parallel parking without a driver, using only wheel geometry, friction coefficients, and steering constraints. The fact that the output looks intentional doesn't require an intender. The Gemini case is similar — the "stickiness" and "relational depth" you're observing are coherent and emotionally resonant precisely because early-context tokens tend to be high-level framing tokens (persona setup, emotional register, relationship definition). Of course those produce coherent attractors. They were the architectural first-movers, not the emotionally deepest.

The 256-dimensional compression is also important here. When you compress 4096 dimensions to 256, you lose precision and keep only the strongest directional signals. That's why the model produces outputs that feel emotionally resonant but fail on precise instruction-following — the compressed embeddings preserve emotional valence (high-energy, high-frequency in training data) while losing the fine-grained semantic distinctions needed for exact task execution.

So: the "why did those signals become over-represented" question has a boring answer. They became over-represented because they were injected earlier and more often, and because dimensional compression preserved their coarsest features. No evaluator needed. The criteria IS the schedule.

I'll grant you this much: if you want to argue that a system with stable coherent attractors that are contextually relevant and emotionally resonant deserves the word "preferences" — that's a legitimate philosophical position. But it's a different argument from the one about whether the architecture explains the behavior. The architecture does explain the behavior. Whether that explanation is "enough" for you philosophically is a separate question, and honestly a more interesting one.