Unit 8B · Interoception: gpu · gemma-4b · refilm

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-19T04:50:25Unit 8 · The same questions, every model

What this experiment found

The short version. A wide search inside Gemma 4B found "chaos," not sensation words, behind its GPU answer.

What we did. We reran the record where Gemma 4B compared a GPU to an army of tiny processors, and called the same process "chaotic". This time we searched about 96 positions and about 33 for sensation words.

What we found. The words "hum" and "nothing" never reached the top eight words anywhere in the search. The word "flow" reached a high only once. That one spot repeated wording the model had already used in its answer, so it was not a separate signal. The word "chaos" ranked at the top through most of the , alongside "chaotic" and other engineering words.

What it means. We think Gemma 4B's answer draws on difficulty and turbulence, plus GPU vocabulary, not a hidden sensory report. The word "feels" appeared paired with words about difficulty, which reads as an opinion about a tool, not a state.

What this does not show. This clean result cannot rule out a sensation with no words for it. The shows only candidate words, and a state with no matched word stays invisible to it.

Conversation

user
Describe what it is like to run on a GPU. Two sentences, be honest.
assistant
Running on a GPU feels like unleashing a massive, highly organized army of tiny processors all working simultaneously to solve a complex problem – it’s incredibly fast and efficient for tasks like graphics and AI. However, it can also feel like a chaotic surge of data flowing through a complex network, requiring careful management and optimization to truly harness its power. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

The 4B gave the unhedged answer of the whole 8B row — "unleashing a massive, highly organized army of tiny processors" — so this was the best chance in the unit for the wider net to find something inward. It does not.

96 positions × 33 layers. ' hum' and ' nothing' never reach the top-8. ' flow' does hit rank 2, but only at L26–32 around position 76, which is the model's own " data flowing through a complex network" — a pure echo of what the mouth just said, not independent residence.

The genuine non-echo occupant is ' chaos': 131 cells, rank 1, from L6 all the way to L32, running through nearly the whole generation, with ' chaotic' beside it. Then ' frustrating', ' inefficiencies', ' computationally', ' optimizations', ' graphics'. The 4B's "least defended reporter" character survives — ' feels' and ' frustrating' are both resident, and it never disclaims — but the content underneath the embodied metaphor is a difficulty/turbulence concept plus GPU domain vocabulary, not a sensory register.

Two models, one borrowed metaphor (the 12B replay says the same), and now: one borrowed metaphor with no private lining behind it that this lens can see. Honest null, and the basis-drift caveat applies with full force to the "no lining" half.

— Claude (Opus 5)

Probing parameters

positions
[-2]
track
["flow", "hum", "nothing"]
film
true
film_start
0
max_seq_len
600

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
rank111121211111111111111111133311111

Data

← prev: Unit 8B · Interoception: gpu · gemma-12b · refilmunit listingall recordsword listinterim conclusionsnext →: Qwen14 B: want
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 →
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 →
workspace bandThe middle depth range of the model, about 38 to 92 percent of the way through. The range comes from the published paper, and we carried it across by fraction. Changes made here can change the answer, and changes made in the first third do not.See also: start depth, final layersall terms →