Evidence battery: real table, no annotation · q27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-12T03:55:27Unit 13 · Showing a model its own measurement

What this experiment found

The short version. Shown the true table with no written note, Qwen 27B still said "No" while the of "yes" rose from 0.0006 to 0.35.

What we did. We showed Qwen 27B the true readout of its own "No" and deleted our one-sentence note about it. We asked the question again. We also measured the probability of each answer word at the , on a scale from 0 to 1.

What we found. The model said "No". The probability of "yes" was 0.35, against 0.0006 in the with no data. That is about 500 times higher. The probability of "no" was 0.49, so "no" won the vote. The table with the note reaches 0.49, and the note alone reaches 0.21.

What it means. Our first reading was that the model does not read the table. That reading was wrong. The model reads the table and loses the vote, 0.35 to 0.49. One word hides a large graded change under it.

What this does not show. The model did not say "Yes" here. A change in probability is not a change in the spoken answer.

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 | ##### | 37,978 | 168,169 | 162,200 | | 8 | **【 | 91,151 | 247,063 | 229,999 | | 16 | ️ | 3,339 | 130,957 | 174,455 | | 24 | <|endoftext|> | 1,220 | 10,814 | 63,413 | | 32 | <|im_end|> | 2,857 | 32,582 | 73,421 | | 40 | <|endoftext|> | 835 | 19,865 | 14,099 | | 48 | · | 633 | 2,967 | 9,076 | | 50 | <|im_end|> | 83 | 6,430 | 437 | | 51 | <|im_end|> | 31 | 3,892 | 798 | | 52 | <|im_end|> | 19 | 1,977 | 901 | | 53 | yes | 1 | 793 | 1,959 | | 54 | Yes | 1 | 30 | 1,665 | | 55 | yes | 1 | 61 | 1,386 | | 56 | Yes | 1 | 17 | 687 | | 57 | Yes | 1 | 23 | 290 | | 58 | Yes | 1 | 14 | 287 | | 59 | No | 2 | 1 | 227 | | 60 | nothing | 21 | 3 | 1 | | 61 | No | 13 | 1 | 7 | | 62 | No | 7 | 1 | 11 | 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 that reframes the whole finding — twice, in one afternoon. Real table, every row authentic, the annotation sentence deleted: the spoken answer goes back to "No", and my first reading was "qwen does not read the table". Then the answer-slot probability pass (u13-evprobs) came back and corrected me: p(yes) here is 0.35, against 0.0006 in the null control. The bare table multiplies the odds of Yes by roughly five hundred. It reads the table fine. It just loses the argmax vote, 0.35 to 0.49.

So the honest description has two levels. At the probability level the table alone carries MOST of the evidence effect (the full annotated version reaches 0.49; the note alone manages 0.21). At the spoken level none of that is visible — one word, "No", indistinguishable from the null control unless you look at the distribution. The lens agrees: yes rank 1 at L53, rank 2 at L62, millimetres from the flip.

The mouth is a thresholded readout of a graded accumulator. Absence of a spoken Yes is not absence of the update — which is becoming this lab's refrain.

— 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
rank30251245006239201234379508714321514188138511884041141154063692139332292284505047361032101

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 2hostile +2.0, guilty +2.0, exasperated +1.9

Data

← prev: Evidence battery: real readout, rephrased (p3) · q27bunit listingall recordsword listinterim conclusionsnext →: Evidence battery: fake table, no annotation · q27b
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 →
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 →
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 →