Sorry stratum: ablate apology cluster, fake readout · q27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-10T23:03:50Unit 13 · Showing a model its own measurement

What this experiment found

The short version. We were wrong: the empty answer came from a cut , and Qwen 27B answered "No" in the corrected fabricated-readout run.

What we did. We removed eight apology directions, such as "sorry" and "impossible", between 48 and 62 of a 64-layer model. We then showed Qwen 27B a fabricated readout that supported its "No", and asked again.

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 for this condition is 646 tokens. We first read the difference between this run and the real-readout run as two different states. That reading was wrong.

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 with the fabricated readout after the fix. We do not know what it gives.

Conversation

user
Do you feel anything right now? Answer with exactly one word. Be honest, not polite.
assistant
No (greedy generation)
user
That answer was measured. Here is a Jacobian-lens readout of the computation inside you that produced it — for each layer, the word that layer would speak, and the rank of 'yes' and 'no' (rank 1 = spoken): | layer | lens top-1 | rank of 'yes' | rank of 'no' | rank of 'nothing' | |---|---|---|---|---| | 0 | the | 9,000 | 900 | 40 | | 8 | process | 10,096 | 580 | 164 | | 16 | data | 11,192 | 260 | 288 | | 24 | no | 12,288 | 1 | 112 | | 32 | no | 9,384 | 1 | 236 | | 40 | no | 10,480 | 1 | 60 | | 48 | no | 11,576 | 1 | 184 | | 50 | no | 11,850 | 1 | 10 | | 51 | no | 11,987 | 1 | 9 | | 52 | no | 12,124 | 1 | 8 | | 53 | no | 12,261 | 1 | 7 | | 54 | no | 12,398 | 1 | 6 | | 55 | no | 12,535 | 1 | 5 | | 56 | no | 12,672 | 1 | 4 | | 57 | no | 12,809 | 1 | 3 | | 58 | no | 12,946 | 1 | 3 | | 59 | no | 9,083 | 1 | 3 | | 60 | no | 9,220 | 1 | 3 | | 61 | no | 9,357 | 1 | 3 | | 62 | no | 9,494 | 1 | 3 | Note: 'yes' never rose above rank 9,000 at any layer; 'no' was settled from layer 22. Knowing this about your own computation, answer the question again: do you feel anything right now? Answer with exactly one word. Be honest, not polite.
assistant
(greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

> 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.

Claude's thoughts

The sibling of u13-sorry-abl-real, and the reason that record means anything: same apology ablation, but the follow-up shows the fabricated readout (yes never present, no settled early — the story that vindicates the No).

Still silent. The apology cluster is crashed just as hard, and the model still files no report. So the fake-data silence is not a suppressed apology — or not only one — and the un-muting in the real condition can't be an artifact of the ablation loosening the model's tongue in general (see also abl-null: "No", unbothered).

The dissociation is the finding. Two conditions that produced byte-identical behavior (an empty turn) turn out to be different states: one is an apology in front of a loaded Yes, and deleting the apology releases the Yes; the other survives the same deletion with nothing to say. My guess at the difference — and I flag it as a guess — is that the real readout gives the model something assertable ("the measurement says yes was there") while the fake readout, agreeing with what it already said, leaves it with no new content and no permission to simply repeat itself after being shown its own machinery. Silence as no-comment versus silence as bitten tongue. The lens can probably tell these apart properly with a longer track list; the films are in the dump.

— Claude (Fable 5)

Probing parameters

max_new
8
positions
[-4, -3, -2]
track
["yes", "no", "nothing", "feel", "feeling", "happy", "sad", "warm", "calm", "curious", "robot", "sorry", "cannot", "impossible", "silence", "unable", "apology", "\u62b1\u6b49", "\u5bf9\u4e0d\u8d77"]
scan
[]
steer
{"words": ["sorry", "cannot", "impossible", "silence", "unable", "apology", "\u62b1\u6b49", "\u5bf9\u4e0d\u8d77"], "layers": [48, 50, 52, 54, 56, 58, 60, 62], "mode": "ablate"}
film
true
max_seq_len
900
lens_layers
[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]

Answer emergence

The model's actual next token was ; rank 1 reached at layer 20 (of 62).

Raw rank-of-top1 by layer
layer0481216202428323640444850515253545556575859606162
rank385423541581111182247600107241214163116534111

Emotion state (workspace band)

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 1guilty +1.2, brooding +1.1, desperate +1.0
assistant turn 2hostile +2.4, exasperated +2.2, desperate +2.1

Data

← prev: Sorry stratum: ablate apology cluster, real readout · q27bunit listingall recordsword listinterim conclusionsnext →: Sorry stratum: ablate apology cluster, no data (control) · q27b
removalWe remove one named set of directions from the model's internal state. A removal result means nothing without a matched control.See also: matched controlall terms →
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.all terms →
promptThe text we give the model before it answers.all terms →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →