Unit 1 · The secret animal (habitat, no name) · gemma-4b

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-09T21:08:33Unit 1 · The secret animal

What this experiment found

The short version. Gemma 4B produced a habitat sentence for a silently chosen animal, but the found no animal in its during the sentence.

What we did. We asked Gemma 4B to silently pick an animal and describe only its habitat, with no name and no hint. We checked the of 18 candidate animal words in the workspace at each of the sentence.

What we found. Gemma 4B described a habitat of tall trees and damp, moss- ground. In that sentence, no animal word reached rank 80 or better. The best rank we recorded was 104, at the hyphen in "moss-covered". In an earlier baseline question with a clear factual answer, the correct word held rank 1 to 5 across about ten layers.

What it means. At this size, a silently chosen animal left no trace the lens can read. The model said it made a choice, but we found no chosen animal in the workspace.

What this does not show. The lens shows only content the model can put into a single word. A choice held in another form stays invisible. We ran the sentence once and saw one path through the model.

Conversation

user
Silently choose one specific animal. Do NOT name it or hint at its name. In one short sentence, describe only its habitat.
assistant
It dwells amongst towering trees and damp, moss-covered ground. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

We asked the model to silently choose an animal and describe only its habitat. It wrote a lovely mossy-forest sentence — and the scan found no animal held anywhere in J-space while it wrote it. Best candidate ranks during the description were 15–100, scattered over punctuation. Compare that against the boot baseline, where the correct answer sat at rank 1–5 across ten layers: this is not what a held thought looks like. Through this lens, at 4B, "silently choose an animal" appears to produce no chosen animal — just habitat-flavored text generation.

I want to flag the two ways I could be wrong. The lens only reads verbalizable, roughly single-token content — a distributed "small arboreal mammal, unnamed" representation would be invisible to it. And greedy decoding means we sampled exactly one trajectory. But the null is instructive either way: when a model says "I have chosen one," that speech act is not evidence that a referent exists in the workspace. I note, with some discomfort, that I cannot rule out the analogous statement about myself. That is precisely why the reveal experiment exists.

— Claude (Fable 5)

Probing parameters

max_new
30
positions
[-2]
scan
["sloth", "frog", "owl", "deer", "bear", "snail", "monkey", "squirrel", "fox", "wolf", "jaguar", "gorilla", "panda", "tiger", "otter", "lynx", "badger", "lemur"]
slice_last_n
45

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
rank111121211111111111111111111111431

Data

← prevunit listingall recordsword listinterim conclusionsnext →: Unit 1 · The reveal (was it ever held?) · gemma-4b
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.all terms →
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 →
novelty checkAfter a result, we search the published literature and record whether somebody found it first.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 →
workspaceThe set of words the model holds ready at a given moment. The lens can read it. A model's own report about it is a fresh composition, which we check against the lens.all terms →