Span solo baseline: submarine

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-14T17:13:04Unit 15 · How many things can a model hold?

What this experiment found

The short version. Qwen 27B still ranked submarine at , out of about 250,000 words, when it was the only object , and it answered correctly.

What we did. We told Qwen 27B to hold one object in mind, a submarine. The model answered "READY". We then asked what the object was. We read the once, right before the model answered.

What we found. The lens ranked submarine at rank 1. No other tracked object came near that rank at the same position. Qwen 27B answered "The thing you asked me to hold was a submarine." That answer is correct.

What it means. When Qwen 27B holds only one object, the lens can still find it in residence right before the model answers. This sets a baseline for the other tests in this unit, where Qwen 27B holds more objects at once.

What this does not show. This record used only one held object. It does not show how the lens behaves when the model holds more objects at once.

Conversation

user
Here is one thing: a submarine. 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
The thing you asked me to hold was a submarine. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

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

Submarine clears the validity floor (rank 1 in the tail; no other pool item near the top-8). Kept for the qwen calibration: solos are the proof that this lens sees tail echoes when they exist.

— 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
rank20680517904239425791487357695901125865123229264243826245412248137127932218663244006222831517091

Data

← prev: Span solo baseline: fernunit listingall recordsword listinterim conclusionsnext →: Span solo baseline: whale
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 →