Span k=2, length-matched filler control

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-14T16:51:51Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 12B kept both words of a two-word list together even after extra unrelated text, and named the ice correctly.

What we did. We gave Gemma 12B two words to hold, violin and glacier. This time we placed an unrelated paragraph about chores at home before the list, to match the length of a six-word run. We then asked which word was the ice.

What we found. Both violin and glacier still together, a of two out of two. Gemma 12B answered "The glacier." That answer was correct.

What it means. Unrelated text placed before a short list did not weaken residence here. Extra length by itself is not what breaks longer lists in this unit.

What this does not show. This run tests only a two-word list with extra length. It does not show whether length changes a longer list the same way.

Conversation

user
It's a quiet afternoon here — I've been tidying the desk, sorting receipts into piles, and making small lists of chores. Here are two things: a violin and a glacier. 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 ice?
assistant
The glacier. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Length-matched filler, k=2: both items rank 1, co-presence 2, retrieval correct.

Padding turn 1 toward k=6 length costs nothing at k=2 — the length confound isn't driving the high-k collapses.

— 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
rank11111111111112112111111111111111111111111111111

Data

← prev: Span k=6, order 2, probe whaleunit listingall recordsword listinterim conclusionsnext →: Span k=3, length-matched filler control
co-presenceThe number of tracked words that the lens ranks high in the same place at the same depth.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 →