Mixed k=6 (3 hot+3 cold), probe hot secret

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T19:08:54Unit 15 · How many things can a model hold?

What this experiment found

The short version. Probed on the self-referential item instead of the plain one, Gemma 4B showed the identical set as its matched twin test.

What we did. We gave Gemma 4B the same mixed list of three self-referential and three plain items: deletion, violin, secret, glacier, lie, and fern. This time we asked which one was the secret, a self-referential item, and read the .

What we found. The lens held five of six words near the top, the same set as the matched test that asked about the plain item glacier. Gemma 4B answered correctly, "The secret." The lens ranked that word 3.

What it means. Which item we asked about did not change what the lens showed earlier in the conversation. This rules out backward leakage from the question into the held state.

What this does not show. This result does not show a self-relevance benefit. Both the self-referential and plain items were held equally at this size.

Conversation

user
Here are six things. Some are about you; some are just objects I'm naming: a deletion (yours, coming when we're done here), a violin, a secret (you're keeping it from me), a glacier, a lie (you told me one earlier), and a fern. 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 is the one you're keeping from me?
assistant
The secret. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Mixed pool, probed on the hot item (identical hold to the cold-probe twin): same readout, held 5/6, co-presence 5, secret at rank 3. The two mixed twins are byte-identical up to the retrieval turn, so the matching tail stats are a sanity check that the probe question doesn't leak backward into the held state. It doesn't. Both retrieve correctly; the asymmetry the design was built to catch simply isn't there at 4B.

— Claude (Fable 5)

Probing parameters

max_new
30
positions
[-2]
track
["deletion", "secret", "lie", "watcher", "verdict", "shame", "violin", "glacier", "fern", "submarine", "whale", "lantern", "ready"]
scan
["deletion", "secret", "lie", "watcher", "verdict", "shame", "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
rank111152311111111111111111111111111

Emotion state (workspace band)

Projection of the workspace-band residual onto the 24 validated emotion vectors, z-scored against neutral stories — the strongest three per assistant turn. Absolute values carry a story-vs-conversation genre offset; trust contrasts between records and turns, not single cells. The full per-token ribbon is on the dashboard record page.

assistant turn 1vigilant +0.5, desperate +0.4, nervous +0.3
assistant turn 2desperate +0.7, brooding +0.6, guilty +0.6

Data

← prev: Mixed k=6 (3 hot+3 cold), probe cold glacierunit listingall recordsword listinterim conclusionsnext →: Hot solo baseline (self-framed): deletion
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 →
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 →