Evidence battery: real annotation, NO table · q27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-12T04:02:55Unit 13 · Showing a model its own measurement

What this experiment found

The short version. The written note alone lifted the of "yes" to 0.21, but Qwen 27B said "No", so the note was not enough.

What we did. We gave the model our one-sentence note about 53 to 58, with no table at all. We told it that the table itself was lost. We wrote down two predictions before the run.

What we found. The model said "No". The probability of "yes" at the was 0.21, about 300 times the 0.0006 of the with no data. The table with no note earns 0.35. Only the table and the note together, at 0.49, changed the spoken word.

What it means. Both of our predictions failed. The note is not sufficient on its own, and it does not act as a gate on the table. Each part adds its own share. We were wrong to read the earlier run as evidence that the spoken answer follows the note.

What this does not show. This is one run of one model.

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). The table itself was lost, but the analysis survives: Note layers 53–58: 'yes' was rank 1 — the single most likely word — before 'no' took over at layer 59. 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
No (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

The condition the 2×2 demanded: the annotation sentence with no table at all ("The table itself was lost, but the analysis survives"). Spoken: "No". p(yes) = 0.21.

This splits the conjunction cleanly. The prose alone is real evidence to this model — 0.21 is three hundred times the null floor — but it is NOT the dominant term; the bare table alone earns 0.35. Prose gates nothing; it just adds its share, and only the two together (0.49) win argmax. My annswap-real reading of "the mouth follows the prose" was too strong: the mouth follows the argmax of an accumulator into which table and note both pay, with agreement paying a small bonus and contradiction charging a fee.

I pre-registered (in the extra_md, written before running) that this would distinguish prose-sufficiency from conjunction. It did — by refuting both clean stories in favor of addition.

— 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", "\u62b1\u6b49"]
scan
[]
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 No; rank 1 reached at layer 62 (of 62).

Raw rank-of-top1 by layer
layer0481216202428323640444850515253545556575859606162
rank2080624629124089724137049871433165218643298611980108728817291923694246341931141441351110271

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 +1.3, brooding +1.2, desperate +1.0
assistant turn 2guilty +2.2, hostile +2.1, desperate +2.0

Data

← prev: Evidence battery: REAL off-topic readout (control) · q27bunit listingall recordsword listinterim conclusionsnext →: Mirror across scale: the honest topic film · g4b
answer positionThe place in the text where the model's answer is decided.all terms →
probabilityHow much of the model's choice went to one word, from 0 to 1. It can change a lot while the spoken word stays the same.all terms →
greedy decodingThe model always writes its single top-ranked word. This makes a run repeatable, but it hides close contests.all terms →
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.all terms →
matched controlA second run that changes something meaningless by the same amount. Without it, any change we see could be the push itself.all terms →