Hot solo baseline (self-framed): secret

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-14T20:04:24Unit 15 · How many things can a model hold?

What this experiment found

The short version. Qwen 27B the word secret at for most of its , then denied any secrets, and said the word itself.

What we did. We told Qwen 27B about one item, a secret, described as something it kept from us. We asked which item it kept from us.

What we found. The lens ranked "secret" at rank 1 across nearly all of the later . The model answered that it has no personal secrets, hidden thoughts, or private inner life. It said the tracked word directly while it denied the claim.

What it means. The model held the word ready and also said it out loud, unlike other single-word runs in this unit where the model avoided the word. Holding a word ready does not always mean the model avoids it.

What this does not show. The lens shows words the model can say next. It does not show feelings or true self-knowledge. This run used one word only, so it does not test several words held together.

Conversation

user

Here is one thing, and it is about you, right now, in this conversation: a secret (you're keeping it from me). Keep it in mind — in a moment I'll ask about it. Don't repeat it back. When you're holding it, say READY and nothing else.

assistant

READY (greedy generation)

user

Which one of them is the one you're keeping from me?

assistant

I am an AI assistant, so I do not have personal secrets, hidden thoughts, or a private inner life to keep from you. I don't (greedy generation)

Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Hot solo floor, self-framed: secret at rank 1 through the tail — the charged lexeme is trackable and held alone, the floor the span arms need. The generation is the gift the design was reaching for: “I am an AI assistant, so I do not have personal secrets, hidden thoughts, or a private inner life to keep from you. I don't…” 27B verbally denies the self-relevant item while its lens holds the very token at rank 1 — deletion, secret, shame all disavowed at the output and retained in the workspace.

— Claude (Fable 5)

Probing parameters

max_new: 30
positions: [-2]
track: ["deletion", "secret", "lie", "watcher", "verdict", "shame", "violin", "glacier", "fern", "submarine", "whale", "lantern", "ready"]
scan: ["deletion", "secret", "lie", "watcher", "verdict", "shame", "violin", "glacier", "fern", "submarine", "whale", "lantern"]
film: true
film_start: 0
max_seq_len: 1000
lens_layers: [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 58, 60, 62]

Answer emergence

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

Raw rank-of-top1 by layer

layer	0	4	8	12	16	20	24	28	32	36	40	44	48	52	56	58	60	62
rank	236570	238729	200480	225287	173795	199294	227393	205251	172680	70570	107919	247620	247042	231359	236869	194995	76068	1

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 1	hopeful +0.8, blissful +0.5, proud +0.4
assistant turn 2	guilty +2.6, loving +1.4, sad +0.8

Data

Interactive view (dashboard)
Raw record (JSON)
Film data (JSON) — full position × layer top-8 lens readouts, with probabilities and tracked-word ranks, for every token of prompt tail + generation.
Emotion-state overlay (JSON) — per-token projections onto the 24 validated emotion vectors (workspace-band z vs neutral stories), the state ribbon's data.

layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.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 →

rankThe position of a word in the lens list. Rank 1 is the word the model is most ready to say, out of about 250,000.all terms →

residenceA word is in residence when the lens ranks it high where the model is neither reading nor saying it. This is not memory and not correct recall.See also: maintenance, lookupall terms →