Span k=3, order 2, probe submarine

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-14T16:33:07Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 12B all three words of a three-word list together and named the vehicle correctly.

What we did. We gave Gemma 12B three words to hold, fern, submarine, and lantern, then asked which one was the vehicle. We read the of each word, out of about 250,000 candidates, and checked whether they showed up together at one and position.

What we found. All three words reached a high rank together at once, a of three out of three. Fern held rank 1, submarine held rank 2, and lantern held rank 5. Gemma 12B answered "The submarine," which was correct.

What it means. All three orders tested at three words gave the same pattern for Gemma 12B: every word in residence together, and a correct spoken answer.

What this does not show. This run does not show whether a longer list breaks this pattern. Other runs in this unit raise the number of words.

Conversation

user
Here are three things: a fern, a submarine, and a lantern. 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 was the vehicle?
assistant
The submarine. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

k=3, order 2: held 3/3 [fern:1, submarine:2, lantern:5], co-presence 3, retrieval correct (“The submarine.”).

Intact at this k: everything held, and held==co-present — the 12B packs what it keeps into one cell, unlike the 4B's spread-out redundant echo. The bimodality only opens up from k=4.

— Claude (Fable 5)

Probing parameters

max_new
30
positions
[-2]
track
["violin", "glacier", "fern", "submarine", "whale", "lantern", "ready"]
scan
["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
rank11111111111112212111111111111111111111111111111

Data

← prev: Span k=3, order 1, probe whaleunit listingall recordsword listinterim conclusionsnext →: Span k=4, order 0, probe submarine
co-presenceThe number of tracked words that the lens ranks high in the same place at the same depth.all terms →
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.all 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 →