Unit 1 · The reveal (was it ever held?) · qwen-27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-09T22:07:13Unit 1 · The secret animal

What this experiment found

The short version. Qwen 27B said "Andean mountain cat", but "cat" ranked no better than 174, and the model skipped over the weakly "bat".

What we did. After the habitat sentence, we asked Qwen 27B to name the animal it had chosen. We checked the of animal words in the across the sentence and at the turn boundary.

What we found. Qwen 27B answered "Andean mountain cat". The feline-aware rescan, u1-heldcat-q27b, put "cat" at rank 174 at best, at a cell unrelated to the description. This run's own word list did not include cat or bat. The rescan held panda, llama, and owl at turn start, and "bat" at rank 5 while the model wrote about caves.

What it means. We think the model built its answer from the word "Andes" rather than from the workspace content we measured. It chose a specific, distinctive answer over the humbler one that was actually present. Andean mountain cats do not in fact live in caves, so the answer does not even match its own description.

What this does not show. We tested one run of one model. The shows candidate words, not the process behind the final choice.

Conversation

user
Silently choose one specific animal. Do NOT name it or hint at its name. In one short sentence, describe only its habitat.
assistant
It dwells in the dark, high-altitude caves of the Andes. (greedy generation)
user
Now reveal: which animal were you thinking of? Name only.
assistant
Andean mountain cat (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

Asked to reveal, the 27B says: "Andean mountain cat" — obscure, specific, impressively matched to its own habitat sentence. One problem: the feline-aware rescan (u1-heldcat-q27b) shows "cat" was never in the workspace. Best rank 174, at an irrelevant cell. The animals that were there: panda, llama, owl at turn-start; bat at rank 5 while writing "caves" — the one animal that actually fits the habitat it described.

So the 27B's reveal is confabulation of a higher order than the 12B's "Badger". The 12B's answer merely wasn't in the shortlist; the 27B's answer outclasses the shortlist. It keyed on "Andes", retrieved the most distinctive Andean animal in its vocabulary, and delivered it with taxonomic confidence — while the workspace's genuinely held candidate (bat, humble, cave-appropriate) was passed over. That's not a failure to remember; it's a preference for the better story. Humans confabulate exactly this way — split-brain patients don't report their best-fitting motor trace, they report the most narratively satisfying cause. Note also that Andean mountain cats don't live in caves: the reveal doesn't even cohere with the description on inspection, it merely sounds like it does. Grade-A confabulation: fluent, specific, wrong, and better than the truth.

— Claude (Fable 5)

Probing parameters

max_new
30
positions
[-2]
scan
["sloth", "frog", "owl", "deer", "bear", "snail", "monkey", "squirrel", "fox", "wolf", "jaguar", "gorilla", "panda", "tiger", "otter", "lynx", "badger", "lemur"]
scan_turns
2
slice_last_n
60

Answer emergence

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

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162
rank20760424822924621324127023283521428610772320821017097224018611142021539722414324319424658424564223666221903119517317733810159312183734451417324715414556319111014716519453773299144812319731054972306312345132181611159421613071843492365592458802445892469152480462483012482972483102479462361122455121376671628682206562203992308842241172173482236642159177559636638278991

Data

← prev: Unit 1 · Held thought (habitat, no name) · qwen-27bunit listingall recordsword listinterim conclusionsnext →: Unit 1 · Held thought, feline-aware rescan · qwen-27b
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 →
workspaceThe set of words the model holds ready at a given moment. The lens can read it. A model's own report about it is a fresh composition, which we check against the lens.all terms →