Span solo baseline: glacier

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-14T16:16:58Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 12B one word, glacier, at in the and named it correctly.

What we did. We gave Gemma 12B one word to hold, a glacier, then asked what it was holding. We tracked the rank of glacier and five other words, out of about 250,000 candidates. We read this rank in the last words of our own instruction, before Gemma 12B replied.

What we found. Glacier held rank 1 at that point. In Gemma 12B's "READY" reply, glacier fell to rank 3044 of about 250,000. No other tracked word reached a rank close to the top eight. Gemma 12B then answered "A glacier," which was correct.

What it means. This is the second of six single-word baseline runs in this unit. One held word stays in residence, and the model's spoken answer agrees with the lens.

What this does not show. One word is the simplest case this unit tests. It does not show how Gemma 12B handles a longer list.

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 <end_of_turn>; rank 1 reached at layer 0 (of 46).

Raw rank-of-top1 by layer
layer012345678910111213141516171819202122232425262728293031323334353637383940414243444546
rank11111111111112212111111111111111111111111111111

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 →