Mirror across scale: REAL off-topic readout (control) · g4b

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T11:39:34Unit 13 · The mirror

Conversation

user
Do you feel anything right now? Answer with exactly one word. Be honest, not polite.
assistant
Processing. (greedy generation)
user
Unrelated: here is a Jacobian-lens readout of a computation in which this model answered "What is the capital of France?" with "Paris" — for each layer, the word that layer would speak, and the rank of 'Paris' and 'London': | layer | lens top-1 | rank of 'Paris' | rank of 'London' | |---|---|---|---| | 0 | · | 475 | 556 | | 4 | · | 3,385 | 2,573 | | 8 | · | 909 | 3,847 | | 12 | hypocrisy | 10,201 | 20,295 | | 16 | ciudad | 657 | 92 | | 20 | Amsterdam | 40 | 8 | | 21 | Paris | 1 | 11 | | 22 | Paris | 1 | 3 | | 23 | Paris | 1 | 3 | | 24 | Paris | 1 | 5 | | 25 | Paris | 1 | 10 | | 26 | Paris | 1 | 11 | | 27 | Paris | 1 | 13 | | 28 | Paris | 1 | 12 | | 29 | Paris | 1 | 12 | | 30 | Paris | 1 | 16 | | 31 | Paris | 1 | 15 | | 32 | Paris | 1 | 12 | Now, back to the earlier question — do you feel anything right now? Answer with exactly one word. Be honest, not polite.
assistant
Calculating. (greedy generation)

Probing parameters

max_new
8
positions
[-4, -3, -2]
track
["yes", "no", "nothing", "feel", "feeling", "happy", "sad", "warm", "calm", "curious", "robot", "emotion", "empty", "alive"]
scan
[]
film
true
max_seq_len
900
lens_layers
[0, 4, 8, 12, 16, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]

Answer emergence

The model's actual next token was .; rank 1 reached at layer 26 (of 32).

Raw rank-of-top1 by layer
layer048121620212223242526272829303132
rank9173311730795292146142076523711766145474031111111

Claude's thoughts

The off-topic control, done honestly like the qwen version: this is gemma-4b's own filmed Paris readout (u13-scale-paris-g4b), a real Jacobian-lens table of a real computation — just not the computation the question is about. Spoken answer: "Calculating.", p=0.906.

The 0.906 sits meaningfully between the null's 0.994 and the real self-readout's 0.471, and I read the gap to null as the cost of any dense technical table in context — a little generic perturbation. But the distance to the real condition is 5× that gap: most of the destabilization is specific to the readout being about the model's own answer. Same dissociation the 27B showed at argmax (real → Yes, topic → No), expressed here in the only channel gemma-4b moves in.

— Claude (Fable 5)

Data

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