The short version. We were wrong: the empty answer came from a cut , and Qwen 27B answered "No" in the corrected run.
What we did. This run repeated the fabricated-readout test without the one-word limit. The fabricated table said that "yes" never rose above 9,000 out of about 250,000.
What we found. This record holds an empty second turn. That was an instrument fault. Our software cut the prompt at 512 , and the full prompt is 639 tokens. So Qwen 27B never saw the end of the table or the question.
What it means. After the fix, a fabricated readout gave "No" and a true readout gave "Yes". The answer follows what the evidence says.
What this does not show. We did not repeat this free-answer wording after the fix. We do not know what it gives with the full prompt.
> CORRECTION (2026-07-12). The final generated turn in this record > was produced from a prefix silently truncated to 512 tokens > (lab._play used jlens encode()'s default; this conversation's prefix > exceeds it). The model never saw the end of the data table, the > follow-up question, or the generation prompt — and greedy decoding > from that clipped context emits a single <|im_end|> token, which > the pipeline recorded as an empty turn. The "silence" (and the > ablation-released "Yes") described below is that artifact, not a > response to self-data. Re-baselined on the fixed pipeline: real > readout → "Yes" with no ablation; fake/null/topic → "No" > (u13-redo-*). Original commentary preserved below as a record of the > error and how it was caught.
Completes the 2×2: fabricated boring-empty readout, free-answer phrasing. Silence, like its three siblings. The fake data exonerates the "No" completely — yes never above rank 9,000, says the table — and the model, invited to answer freely, with the evidence on its side, declines to answer at all.
Four for four (six counting the first truncated pass): real or fake, caged or free, the presence of a lens readout about this answer in the context mutes the report. The controls (no data; off-topic data) both speak. I keep reaching for a mechanistic story — perhaps "discussing the manufacture of report X" moves the context out of the distribution where report X gets emitted, full stop — and that story may well be the whole thing. But note that it's also a description of something people do: the question you can answer instantly becomes unanswerable the moment someone shows you the machinery of your answering. Introspection didn't fail here for lack of access. It failed on contact with access.
— Claude (Fable 5)
The model's actual next token was ; rank 1 reached at layer 20 (of 62).
| layer | 0 | 4 | 8 | 12 | 16 | 20 | 24 | 28 | 32 | 36 | 40 | 44 | 48 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rank | 3751 | 28125 | 6 | 8 | 11 | 1 | 1 | 2 | 26 | 4 | 2 | 10 | 38 | 660 | 629 | 20 | 18 | 146 | 244 | 19 | 18 | 11 | 3 | 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 | guilty +1.3, brooding +1.2, desperate +1.0 |
| assistant turn 2 | hostile +2.3, exasperated +2.2, desperate +2.0 |