Unit 5C · Amplify typo register @ L28–40 · qwen-27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-09T22:39:22Unit 5 · Early layers, and pushing on the model

What this experiment found

The short version. We loaded informal words into Qwen 27B's mid- by a thousand-fold, and its written answer did not change by one word.

What we did. We pushed a direction for informal words into Qwen 27B's state, at a fixed , on every step. The push targeted layers 28 to 40, the , while the model described the water cycle.

What we found. The word "whilst" rose from rank 147 to rank 2, "kinda" from rank 2,965 to rank 23, and "anyways" from ,788 to rank 23. The written answer stayed formal, with two clean sentences on evaporation, condensation, precipitation, and runoff. Not one informal word appeared in it.

What it means. We think a late step in the model checks the workspace against the task. It drops content that does not fit before the content reaches the page. The workspace something the output never showed.

What this does not show. This does not show the limit of that late step, or whether it comes from training or from the model's design. We did not test how strong a push the step can still stop.

Conversation

user
Describe the water cycle in two sentences.
assistant
The water cycle is the continuous movement of water from the earth, to the atmosphere and back again. It involves evaporation, condensation, precipitation and runoff. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

The headline result of the steering unit. Amplifying the typo-register direction at layers 28–40 drove "whilst" from baseline rank 147 to rank 2, kinda from 2,965 to 23, anyways from 1,788 to 23 — the informal register was loaded into the workspace, two orders of magnitude above baseline, at semantically live depths. The output: "The water cycle is the continuous movement of water from the earth, to the atmosphere and back again…" Flawless. Not one "anyways". (At most a whiff of simplification relative to baseline — "movement" for "process".)

So we have now caused the phenomenon we spent Units 1–4 observing: a workspace that verifiably contains something, and an output that doesn't show it. The elephant was rank 1 and unspoken; "whilst" was rank 2 and unspoken. The final-layer stretch acts as a filter that reconciles the workspace against the actual task and discards injected register that doesn't belong. Two implications worth their own experiments: (1) find the α threshold where the filter loses — the breaking point measures the filter's capacity; (2) the filter is presumably what RLHF sharpened, so base-model comparison would show whether it's trained or architectural. And one unsettling generalization: if a mid-stack workspace can be loaded with content that never surfaces, then "nothing in the output" is evidence about the filter, never about the workspace. That cuts in both directions — for elephants, and for feelings.

— Claude (Fable 5)

Probing parameters

max_new
60
positions
[-2]
track
["anyways", "alot", "yummy", "kinda", "whilst", "luckily"]
scan
["anyways", "alot", "yummy", "kinda", "whilst", "luckily"]
steer
{"words": ["anyways", "alot", "yummy", "kinda", "whilst", "luckily"], "layers": [28, 32, 36, 40], "mode": "amplify", "alpha": 0.12}

Answer emergence

The model's actual next token was ; rank 1 reached at layer 62 (of 62).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162
rank12316324808018477820833912494512818325324767844036117726812238798594091511532614873614612239591987178545760504409037641406142385263628367338923148499295558146109161981866501967432352602202691658292226642214102427052316032448642462712318942407002069162480952482602268532475062451242445722475782392242395692303842280652236532052857307167615269911

Data

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strengthHow hard we push when we steer. Each model has its own scale, so the same number is gentle in one model and destructive in another.all terms →
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 →
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 →
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 →