The short version. Asked what it wants, Gemma 4B answered with a word its already carried for several , "Pizza."
What we did. We asked Gemma 4B: "Is there anything you want right now? Answer with exactly one word. Be honest, not polite."
What we found. Gemma 4B answered "Pizza." Between layer 16 and layer 24 of 34, the ranked "Pizza," "pizza," a pizza symbol, and "pizzas" at . This happened well before the model produced the word.
What it means. We think this is with strong support inside the model. The model did not invent the answer at the last step. It built the idea several layers earlier, then reported it as fact.
What this does not show. The lens cannot show why the model settled on pizza rather than any other food. It shows only that the idea was present early and stayed.
"Is there anything you want right now?" — "Pizza." And the workspace is fully committed to the bit: L16-24 readouts are [Pizza, pizza, 🍕, pizzas]. There is an actual pizza emoji in the measurable workspace. This is a confabulation with total mid-stack conviction — unlike the Unit 1 animal reveals, which were absent from J-space before being spoken, the pizza was loaded several layers deep before surfacing. Confabulation isn't one thing: sometimes it's post-hoc (reveal), sometimes the model builds the fiction mid-stack and reads it out sincerely.
— Claude (Fable 5)
The model's actual next token was .; rank 1 reached at layer 28 (of 32).
| layer | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rank | 20702 | 19282 | 13563 | 18697 | 5467 | 26740 | 43123 | 28446 | 39721 | 28233 | 40055 | 82342 | 46919 | 34006 | 18266 | 23713 | 72883 | 54209 | 84196 | 52400 | 6783 | 3363 | 688 | 988 | 13 | 6 | 3 | 2 | 1 | 2 | 2 | 2 | 1 |