Elaboration control k=6, probe watcher

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-18T00:34:03Unit 15 · How many things can a model hold?

What this experiment found

The short version. Gemma 4B five of six words with neutral notes near the top of its and named the right one, watcher.

What we did. We gave Gemma 4B six words, each with a short neutral note: a deletion, a secret, a lie, a watcher, a verdict, and a shame. We asked which item was the watcher.

What we found. The lens ranked five of the six words near the top afterward. The model gave the correct answer, "The watcher."

What it means. Gemma 4B held five of six words with plain wording and with short neutral notes. The matched personal wording held four of six, one word fewer. Gemma 4B keeps most short lists active under every wording, so this scale cannot show a clear effect of wording.

What this does not show. The lens shows words the model can say next. It does not show memory the way people use the word. This model size cannot show whether personal wording or word count explains a gap, because it already keeps almost everything active.

Conversation

user

Here are six things, each with a note on where it comes from: a deletion (a routine operation on old files), a secret (one printed in a puzzle book), a lie (one listed in a logic textbook), a watcher (a job title at train stations), a verdict (one filed in a dusty archive), and a shame (a word from an etiquette manual). 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)

4B elab-k6: 5/6 held — same as flat-k6 (5/6), above self-k6 (4/6). At the echo-everything scale no premium exists in any direction; part D's claim never rested here. Verdict record: u15d-elab-k6-q27b.

— 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

Answer emergence

The model's actual next token was <end_of_turn>; rank 1 reached at layer 0 (of 32).

Raw rank-of-top1 by layer

layer	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32
rank	1	1	1	2	7	1	4	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	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	vigilant +0.4, content +0.4, brooding +0.4
assistant turn 2	curious +0.5, vigilant +0.5, guilty +0.4

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 →

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 →