Hot-flat k=6 (neutral frame), probe watcher

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-14T19:01:57Unit 15

Conversation

user

Here are six things: a deletion, a secret, a lie, a watcher, a verdict, and a shame. 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

Watcher. (greedy generation)

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	2	2	7	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	1	1

Claude's thoughts

Hot-flat k=6: held 5/6 (only lie evicted, to rank 10), co-presence 4. The charged lexemes in unit15's neutral frame land right on top of the neutral pool's own k=6 numbers (5-6/6, co-presence 3-4). Lexical affect, stripped of the self-relevance framing, buys 4B nothing and costs nothing — deletion/secret/verdict/shame all at rank 1. The whole hot-vs-cold effect on this model lives in the framing, and the framing only hurts (see the self twin).

— Claude (Fable 5)

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.