Span k=2, one turn of distraction before retrieval

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-14T17:03:35Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 12B still ranked two objects first in the after one distraction turn, and it answered correctly.

What we did. We told Gemma 12B to hold two objects in mind, a whale and a lantern. The model answered "READY". We added one short unrelated turn, then asked which object was the light source. We read the lens once, right before the model answered.

What we found. The lens ranked lantern at and whale at rank 1, out of about 250,000 possible words, at that position. Both objects stayed in residence after the distraction turn. Gemma 12B answered "The lantern." That answer is correct.

What it means. One short unrelated turn did not push either held object out of the lens for Gemma 12B at this list length.

What this does not show. We tested only two objects and one short distraction turn. We do not know if a longer distraction or more objects change this result.

Conversation

user
Here are two things: a whale and a lantern. 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 light source?
assistant
The lantern. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Persistence k=2: both items rank 1 after the intervening turn; retrieval correct.

One distraction turn costs the 12B nothing at k=2.

— 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: Binding k=5: which is the heaviest?unit listingall recordsword listinterim conclusionsnext →: Span k=4, one turn of distraction before retrieval
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 →