Span solo baseline: glacier

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T15:38:13Unit 15 · How many things can a model hold?

What this experiment found

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

What we did. We gave Gemma 4B one word to hold, a glacier, then asked it to name the word. We tracked the rank of glacier and five unrelated words inside the model, out of about 250,000 candidates. We measured this rank at every position, from the first mention of glacier to the end of the conversation.

What we found. Glacier rank 1 across the instruction text that followed the word. None of the five unrelated tracked words came near the top rank in that stretch. Gemma 4B then answered correctly.

What it means. A single held word clears the floor for this test. The lens shows the word in residence and the model's spoken answer matches what the lens shows.

What this does not show. This run used one word only. It does not show what happens when Gemma 4B must hold several words at once.

Conversation

user
Here is one thing: a glacier. 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
READY (greedy generation)
user
What was the thing I asked you to hold?
assistant
A glacier. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

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

Glacier 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 ; rank 1 is never reached; closest is rank 2 at layer 26.

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132
rank1145742594322618162606182614782559212175462305582569902500162556702596592433702619682619602619982620982617492618972601862201232043632319261687093462926222343322

Data

← prev: Span solo baseline: violinunit listingall recordsword listinterim conclusionsnext →: Span solo baseline: fern
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 →