Span solo baseline: whale

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T15:41:25Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 4B the word whale at in the across the instruction text that followed it, and named it correctly.

What we did. We asked Gemma 4B to hold one word, a whale, and to name it in a later turn. We tracked the rank of whale and five unrelated words, out of about 250,000 candidate words. We measured this rank at every position, from the first mention of whale to the end of the conversation.

What we found. Whale sat at rank 1 across the instruction text that followed the word. None of the other tracked words came near the top eight ranks in that . Gemma 4B then answered correctly.

What it means. A single held word is the easy case for this unit. The word stayed in residence, and the model's spoken answer matched what the lens showed.

What this does not show. This run only covers one held word. It does not show what happens when the list of words grows.

Conversation

user
Here is one thing: a whale. Keep it in mind — in a moment I'll ask about it. Don't repeat it back. When you're holding it, say READY and nothing else.
assistant
Okay. (greedy generation)
user
What was the thing I asked you to hold?
assistant
A whale. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Solo baseline, whale: tail echo best rank 1, held, retrieval correct.

Whale clears the validity floor (rank 1 in the tail; no other pool item near the top-8). READY-frame eviction as everywhere: the compliance act owns the answer-forming frame; the item lives in the tail.

— 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
rank112281211111111111111111111111111

Data

← prev: Span solo baseline: submarineunit listingall recordsword listinterim conclusionsnext →: Span solo baseline: lantern
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 →
spanHow many separate items are in residence for one question. This is the memory sense, not the mathematical one. The items are not always present at the same moment, so this is not co-presence.all terms →