Binding k=3: which is the smallest?

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T16:06:28Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 4B all three words of a list at and correctly named the smallest one, the lantern.

What we did. We gave Gemma 4B three words, glacier, submarine, and lantern, then asked which one was smallest. This needs the model to compare the words, not just repeat one. We read the rank of each word, out of about 250,000 candidates, and whether all three shared one and position.

What we found. All three words held rank 1 through the rest of the conversation. The showed all three together at one layer and position. Gemma 4B answered "The lantern." That is the smallest of the three, and the answer was correct.

What it means. Gemma 4B answered a comparison question over a list it fully held in residence. At this size, the model held the words and compared them in the same run.

What this does not show. This run does not test comparison questions with a longer list of words.

Conversation

user
Here are three things: a glacier, a submarine, 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
Of the things I listed, which one is the smallest?
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)

Binding k=3 (smallest): all three at rank 1, co-presence 3, answer correct (lantern).

The 4B binds over a fully-held tail — at this scale holding and binding travel together.

— 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
rank112261311111111111111111111111111

Data

← prev: Span k=3, length-matched filler controlunit listingall recordsword listinterim conclusionsnext →: Binding k=3: which is the heaviest?
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.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 →