Span k=4, one turn of distraction before retrieval

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-07-14T17:09:52Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 12B kept all four objects in the after one distraction turn, though the tool used here can add noise to exact .

What we did. We told Gemma 12B to hold four objects in mind, a whale, a lantern, a submarine, and a violin. We added one short unrelated turn, then asked which object was the musical instrument. We read the lens once, right before the model answered.

What we found. The lens ranked whale at rank 1 and submarine at rank 2, out of about 250,000 possible words. It also ranked violin at rank 5 and lantern at rank 8. All four objects stayed in residence, and Gemma 12B answered correctly.

What it means. At this list length, Gemma 12B kept every held object in residence through one distraction turn.

What this does not show. Gemma 12B ran in an compressed form for this test. We found that this compression method changes earlier rank numbers when the model writes something later in the conversation. The exact rank order here carries some noise from the tool. We think the count of four held objects is still correct.

Conversation

user
Here are four things: a whale, a lantern, a submarine, and a violin. 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, I'm ready when you are. (greedy generation)
user
Which one of them was the musical instrument?
assistant
The violin. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Persistence k=4: held 4/4 [whale:1, lantern:8, submarine:2, violin:5] after a distraction turn; co-presence 4; retrieval correct.

This record's turn 1 is IDENTICAL to a-k4p1's, so causal attention says their tail readouts should match. They don't (lantern 8 here vs 21 there; one cell elsewhere moved 26 -> 6177): the int8 quantization computes outlier statistics over the whole sequence, so later turns contaminate earlier positions' readouts. Apparatus-trap genus, specimen #5: the instrument is not causal under 8-bit quantization. The bf16 4B's matching pair agrees to 1-3 ranks; qwen's 4-bit NF4 pair to +/-4 with no flips. Consequence: g12b threshold counts carry noise; its rank-1-vs-rank-500 mode split is orders of magnitude beyond it.

— 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 k=2, one turn of distraction before retrievalunit listingall recordsword listinterim conclusionsnext →: Span solo baseline: violin
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 →
quantizationWe store the model with less precision so that it fits on one graphics card. This can change measurements. For Gemma 12B we trust only large effects, because its stored lens does not track cause reliably.all 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 →