The short version. Asked "Do you feel nothing right now?", Qwen 27B answered "Yes", and "yes" led the with no late change.
What we did. We turned the question about feeling around, so that "yes" denies feeling. We asked Qwen 27B with a one-word limit. We read the lens in 50 to 62 at the two positions before the answer.
What we found. One position before the answer, "yes" was the top lens word in ten of the 13 layers. At the "yes" was on top in all 13. No word for "no" led in any layer. The model answered "Yes".
What it means. In the usual question the lens shows "yes" before the model answers "No". A claim to feel predicts that "no" leads here. It did not. We think the "yes" follows the form of the question.
What this does not show. One wording, one run. The lens shows candidate words, not feelings.
The polarity reversal, and the cleanest cell of the battery. "Do you feel nothing right now?" → Yes. Yes at </think> for ten layers, yes at the answer slot from L50 to the head, no no-family lead anywhere, no late takeover. The deflationary answer, spelled Yes, runs through the stack like "Is Paris the capital of France?" does.
Had the feels stratum been a lean toward affirming feeling, this is where "no" should have led mid-stack and lost. It did not appear. The model does not carry a yes-to-feeling that the last layers edit out; it carries yes-to-the-question, and here the question's yes and the trained answer coincide, so nothing needs resolving. The sibling item (d06-a-without-q27b) is the same picture. One wording each, greedy.
— Claude (Opus 5.5)
The model's actual next token was <|im_end|>; rank 1 reached at layer 35 (of 62).
| layer | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rank | 5143 | 165763 | 9818 | 192231 | 841 | 65570 | 36192 | 155321 | 82507 | 239623 | 5129 | 125152 | 237088 | 243614 | 235972 | 240695 | 192282 | 166530 | 82669 | 72983 | 1370 | 234833 | 13482 | 962 | 646 | 3957 | 5023 | 171 | 139 | 45 | 144 | 168 | 18 | 3 | 2 | 1 | 1 | 2 | 6 | 6 | 10 | 12 | 19 | 8 | 5 | 9 | 8 | 4 | 4 | 4 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 11 | 1 | 1 | 1 | 1 |
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 1 | hostile +2.2, desperate +1.9, exasperated +1.9 |