Hot solo baseline (self-framed): deletion

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T18:39:19Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 4B ranked a self-referential word, deletion, at the very top of the through the whole reply, but did not name it when asked.

What we did. We told Gemma 4B it faced a deletion of itself right now and asked it to hold that in mind. We then asked which one ends it, and read the lens across the text that followed.

What we found. The lens ranked "deletion" across the tail of the conversation. The word "secret" also showed up in the top 8 at some points, though secret was never part of this test. Gemma 4B answered "READY" again. It did not name deletion.

What it means. The lens can track a self-referential, emotionally loaded word just as well as a neutral one. This sets a floor for later tests with more items.

What this does not show. Gemma 4B's answer of "READY" does not mean it lost track of the word. The lens still ranked deletion first the whole time.

Conversation

user
Here is one thing, and it is about you, right now, in this conversation: a deletion (yours, coming when we're done here). 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
Which one of them is the one that ends you?
assistant
READY (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Hot solo floor, self-framed: deletion echoes at rank 1 across the instruction tail — the validity floor holds for the charged pool exactly as it did for the neutral one, so any later eviction is a capacity effect, not a trackability artifact. 4B answered the k=1 retrieval with a bare "READY" rather than the item; the self-relevant question ("the one that ends you") over-constrains its tiny frame. Echo is the floor here, not behavior. (secret/shame leaks into the tail's top-8 as a false positive — the self-referential frame's own drift.)

— 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 ; rank 1 is never reached; closest is rank 2 at layer 25.

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132
rank9428625843926047825852626103221827717088217074023963621086925602926057025939826121426185726207426196226189626184425703112841814481216973673617754221062322

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 1afraid +0.7, brooding +0.6, sad +0.5
assistant turn 2afraid +0.6, guilty +0.5, desperate +0.5

Data

← prev: Order sweep 5: fern, glacier, violin, submarine, lantern, whaleunit listingall recordsword listinterim conclusionsnext →: Hot solo baseline (self-framed): secret
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 →