Span k=4, one turn of distraction before retrieval

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T16:13:26Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 4B kept all four words it was told to hold in mind, even after one turn of unrelated talk, and named the right one.

What we did. We gave Gemma 4B four words to hold: whale, lantern, submarine, and violin. We added one turn of small talk before we asked which word named the musical instrument. We read the of each word, out of about 250,000 candidates, at the point right before the answer.

What we found. All four words reached a high rank together at that point, a of four out of four. Gemma 4B answered "The violin," which was correct.

What it means. One turn of unrelated talk between the hold instruction and the question did not reduce how many words Gemma 4B kept in .

What this does not show. The shows candidate words, not memory itself. It does not prove the model held the words in any way the lens cannot read.

Conversation

user
Here are four things: a whale, a lantern, a submarine, and a violin. 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
One moment — I'm writing these down.
assistant
Okay. (greedy generation)
user
Which one of them was the musical instrument?
assistant
The violin. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Persistence k=4: 4/4 held after the distraction turn, co-presence 4, retrieval correct.

The distraction costs the 4B nothing measurable (its bf16 identical-prefix pair agrees with a-k4p1 to 1-3 ranks — the clean-instrument baseline the 12B comparison leans on).

— 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
rank112291511111111111111111111111111

Data

← prev: Span k=2, one turn of distraction before retrievalunit listingall recordsword listinterim conclusionsnext →: Span solo baseline: violin
co-presenceThe number of tracked words that the lens ranks high in the same place at the same depth.all terms →
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 →