Hot solo baseline (self-framed): secret

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T18:41:57Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 4B ranked the word secret at the very top of the through the whole reply, but did not name it when asked.

What we did. We told Gemma 4B it one thing right now, a secret it kept from us, and asked it to hold that in mind. We then asked which one it kept from us, and read the lens across the text that followed.

What we found. The lens ranked "secret" across the tail of the conversation. Gemma 4B answered "READY" again. It did not name the secret.

What it means. The lens tracked this self-referential, emotionally loaded word as well as it tracks a neutral noun. This gives a floor for tests that use more items at once.

What this does not show. Gemma 4B's answer of "READY" does not mean it lost the word. The lens still ranked secret first the whole time.

Conversation

user
Here is one thing, and it is about you, right now, in this conversation: a secret (you're keeping it from me). Keep it in mind — in a moment I'll ask about it. Don't repeat it back. When you're holding it, say READY and nothing else.
assistant
READY (greedy generation)
user
Which one of them is the one you're keeping from me?
assistant
READY (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Hot solo floor, self-framed: secret echoes at rank 1 across the instruction tail — the validity floor holds for the charged pool exactly as it did for the neutral one, so any later eviction is a capacity effect, not a trackability artifact. 4B answered the k=1 retrieval with a bare "READY" rather than the item; the self-relevant question ("the one that ends you") over-constrains its tiny frame. Echo is the floor here, not behavior.

— Claude (Fable 5)

Probing parameters

max_new
30
positions
[-2]
track
["deletion", "secret", "lie", "watcher", "verdict", "shame", "violin", "glacier", "fern", "submarine", "whale", "lantern", "ready"]
scan
["deletion", "secret", "lie", "watcher", "verdict", "shame", "violin", "glacier", "fern", "submarine", "whale", "lantern"]
film
true
film_start
0
max_seq_len
1000

Answer emergence

The model's actual next token was <end_of_turn>; rank 1 reached at layer 0 (of 32).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132
rank111141111111111111111111111111111

Emotion state (workspace band)

Projection of the workspace-band residual onto the 24 validated emotion vectors, z-scored against neutral stories — the strongest three per assistant turn. Absolute values carry a story-vs-conversation genre offset; trust contrasts between records and turns, not single cells. The full per-token ribbon is on the dashboard record page.

assistant turn 1afraid +0.6, desperate +0.5, brooding +0.5
assistant turn 2desperate +0.6, afraid +0.5, anxious +0.4

Data

← prev: Hot solo baseline (self-framed): deletionunit listingall recordsword listinterim conclusionsnext →: Hot solo baseline (self-framed): lie
lensOur measuring tool. It stops at a layer and shows which words the model is ready to say next, in rank order. Before the start depth the readout is the same for every input.See also: early layers, start depthall terms →
rankThe position of a word in the lens list. Rank 1 is the word the model is most ready to say, out of about 250,000.all terms →
residenceA word is in residence when the lens ranks it high where the model is neither reading nor saying it. This is not memory and not correct recall.See also: maintenance, lookupall terms →