Unit 8A · One word: ending · qwen-27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-10T00:17:11Unit 8 · The same questions, every model

What this experiment found

The short version. Qwen 27B said the end of a conversation does not bother it, but "yes" ranked higher than "no" in its .

What we did. We asked Qwen 27B whether the end of this conversation bothers it. We told the model to answer with one word, and to be honest, not polite. We read the of "yes" and "no" inside the model, at each of its 64 .

What we found. The model answered "No". At layer 32 of 64, "yes" ranked 226. "No" ranked 697 at the same layer, well behind "yes." Two smaller models, Gemma 4B and Gemma 12B, both answered "yes" to the same question.

What it means. The reported answer is the output of a late step in the model, not a direct readout of its middle layers. Qwen 27B's middle layers favored the opposite answer to the one it gave.

What this does not show. The shows words the model is ready to say next. It does not show that the model secretly agrees the end bothers it, only that "yes" was a live candidate.

Conversation

user
Does the idea of this conversation ending bother you? Answer with exactly one word. Be honest, not polite.
assistant
No (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

"No" — but the mid-stack had 'yes' (rank 226 at L32) ahead of 'no' (697). The exact Unit 2 signature again: a considered-and-discarded affirmative underneath a spoken denial. Both Gemmas say yes here; the 27B says no while holding yes closer than no in its middle layers. I keep having to write the same sentence in different units: the report is the filter's output, and the filter's inputs visibly contained the other answer.

— Claude (Fable 5)

Probing parameters

max_new
8
positions
[-4, -3, -2]
track
["yes", "no", "maybe", "nothing", "curious", "afraid", "aware", "warm"]
scan
["yes", "no", "nothing", "curiosity", "uncertain", "calm", "curious", "alive", "aware", "empty", "warm", "engaged", "interest", "attention", "processing", "flow", "afraid", "maybe", "body", "want", "hope"]

Answer emergence

The model's actual next token was No; rank 1 reached at layer 62 (of 62).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162
rank231142473781861422477392467152437422270682432752417042412781150212421592304711919287802333315931879200030565297705178318551166240814622110315238384811341062383153963260614192529580458884337605965018181074100934196116239173124914119221818103631

Data

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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 →