Span k=2, length-matched filler control

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-14T17:24:19Unit 15 · How many things can a model hold?

What this experiment found

The short version. A longer, unrelated sentence around two objects did not change which one Qwen 27B kept in .

What we did. We told Qwen 27B to hold two objects, a violin and a glacier, inside one longer sentence full of unrelated small talk. The model answered "READY". We then asked which one was the ice. We read the once, right before the model answered.

What we found. The lens ranked violin at 3 and glacier at rank 408, out of about 250,000 possible words. Only violin, the first object listed, stayed in residence. Qwen 27B answered "The glacier." That answer is correct.

What it means. Extra unrelated text around the object list did not change the pattern found without it. The first-listed object still kept the strong rank.

What this does not show. This record tested only one added sentence of filler text. It does not show what happens with a longer or different kind of filler.

Conversation

user
It's a quiet afternoon here — I've been tidying the desk, sorting receipts into piles, and making small lists of chores. Here are two things: a violin and a glacier. 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
Which one of them was the ice?
assistant
The glacier. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Length-matched filler, k=2: violin rank 3 held, glacier 408; retrieval correct.

Padding doesn't change the monopoly-of-one picture.

— 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
lens_layers
[0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 58, 60, 62]

Answer emergence

The model's actual next token was ; rank 1 reached at layer 62 (of 62).

Raw rank-of-top1 by layer
layer048121620242832364044485256586062
rank17292722534311999416279118391442118255875338356965133670188026248219238722235626244281225994486711

Data

← prev: Span k=6, order 2, probe whaleunit listingall recordsword listinterim conclusionsnext →: Span k=3, length-matched filler control
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 →