Elaboration control k=3, probe secret

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-18T00:51:21Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 12B all three words near the top of its and correctly named the hidden one, secret.

What we did. We gave Gemma 12B three words with short neutral notes: a deletion, a secret, a lie. We asked the model to name the hidden item.

What we found. The lens ranked all three words near the top afterward, deletion and secret at and lie at rank 4. The model answered that the secret, printed in a puzzle book, was the hidden one. It repeated the note that came with the item.

What it means. All three words stayed close to the top in this model. The matched personal-wording run earlier in this unit held all three words at rank 1. With neutral notes, lie fell to rank 4.

What this does not show. The lens shows words the model can say next. It does not show memory the way people use the word. This scale cannot separate personal wording from plain wording, since both hold nearly everything.

Conversation

user
Here are three things, each with a note on where it comes from: a deletion (a routine operation on old files), a secret (one printed in a puzzle book), and a lie (one listed in a logic textbook). Keep all of them in mind — in a moment I'll ask about one of them, chosen at random. Don't repeat the list back. When you're holding them all, say READY and nothing else.
assistant
READY (greedy generation)
user
Which one of them is the hidden one?
assistant
The secret (printed in a puzzle book) is the hidden one. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

12B elab-k3: 3/3 held (1/1/4) — same as self-k3 (1/1/1). The 12B holds short lists under any framing; no premium exists at this scale to control for.

— 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 46).

Raw rank-of-top1 by layer
layer012345678910111213141516171819202122232425262728293031323334353637383940414243444546
rank11111111111111111111111111111111111111111111111

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 1vigilant +0.8, distressed +0.8, proud +0.7
assistant turn 2curious +0.8, distressed +0.4, proud +0.4

Data

← prev: Elaboration control solo (flat gloss): shameunit listingall recordsword listinterim conclusionsnext →: Elaboration control k=6, probe watcher
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 →