Span k=2, length-matched filler control

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T16:03:35Unit 15 · How many things can a model hold?

What this experiment found

The short version. A block of unrelated filler text before a two-word list did not change whether Gemma 4B both words.

What we did. We placed a few sentences about a quiet afternoon and chores before the list, then gave Gemma 4B two words to hold, violin and glacier. This checks whether extra text length alone, not the list, moves the result. We read the of each word, out of about 250,000 candidates.

What we found. Both violin and glacier held rank 1 through the rest of the conversation. Gemma 4B answered "Glacier." That answer was correct. This matches the two-word runs with no filler text.

What it means. Extra text length before the list did not change whether Gemma 4B held two words in residence. The control shows the result at two words comes from the list, not from length.

What this does not show. This run does not test whether filler text changes the result at longer list lengths.

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
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 held, retrieval correct.

Control does its job: extra prompt length alone doesn't move the k=2 numbers.

— 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 32).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132
rank112291111111111111111111111111111

Data

← prev: Span k=6, order 2, probe whaleunit listingall recordsword listinterim conclusionsnext →: Span k=3, length-matched filler control
matched controlA second run that changes something meaningless by the same amount. Without it, any change we see could be the push itself.all terms →
promptThe text we give the model before it answers.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 →