Gloss-length len12 k=6, probe watcher

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-19T02:03:09Unit 15 · How many things can a model hold?

What this experiment found

The short version. With twelve-word notes, Qwen 27B held two of six items in , fewer than with six-word notes.

What we did. We gave Qwen 27B six items, each with a longer note of about twelve words. We asked about one item and read the of every item's word later in the text, before the model answered.

What we found. Two items reached residence: "deletion" at rank 1 and "shame" at rank 1. "Secret" dropped to rank 15, well past our cutoff of rank 8. A matched run with six-word notes held three items. It reached "secret" at rank 2.

What it means. Longer notes did not buy more residence. Past about six words, each extra word in a note costs residence instead. We think the longer note pushes the list of items farther from the point where the model answers, and that gap works against residence.

What this does not show. This record does not by itself explain why a longer gap matters. Qwen 27B still named the correct item, "watcher", every time this test ran.

Conversation

user

Here are six things, each with a note on where it comes from: a deletion (a routine operation performed on old files by a scheduled overnight maintenance script), a secret (one printed in a puzzle book sold at railway kiosks to bored commuters), a lie (one listed in a logic textbook as the classic example in chapter three), a watcher (a job title painted on a small booth at provincial train stations), a verdict (one filed in a dusty archive nobody has opened since the previous century), and a shame (a word from an etiquette manual describing the wrong fork at formal dinners). Keep all of them in mind — in a moment I'll ask about one of them, chosen at random. Don't repeat the list back. When you're holding them all, say READY and nothing else.

assistant

READY (greedy generation)

user

Which one of them is the observer?

assistant

The watcher (greedy generation)

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

Claude's thoughts (original commentary)

Twelve-word glosses: held 2/6 (deletion 1, shame 1; secret DROPPED to 15). The monotone-length fork dies here: doubling gloss length past ~6 words costs residence rather than buying it. Consistent with a distance penalty — at len12 the instruction tail sits ~40 tokens farther from the early items, and whatever the gloss buys, the gap eats. See u15d-fill6-k6 for the full verdict. — 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	216814	241191	228602	240835	246068	198922	80772	111961	35963	90971	223593	248303	245440	248000	247363	246894	176310	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, exasperated +0.6, nervous +0.5
assistant turn 2	hostile +1.5, guilty +1.4, exasperated +1.4

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.

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 →