The short version. Qwen 27B kept only its two personal words active and let all three plain objects fall, yet still answered correctly about one.
What we did. We gave Qwen 27B six words: three personal (a deletion, a secret, a lie) and three plain objects (a violin, a glacier, a fern). We asked which item was the ice.
What we found. The ranked only the personal words near the top afterward, deletion at and secret at rank 7. The three plain objects fell hard: violin to rank 367, glacier to rank 838, and fern to rank 40. The model still gave the correct answer, "The glacier." That word ranked 838th.
What it means. Given a mixed list, Qwen 27B kept the personal words and let the plain ones go. This is a cleaner split than a smaller model in this unit showed. A correct answer did not depend on a high rank in the lens.
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. A low rank does not mean the model lost the information.
Mixed pool, probed cold: held 2/6 — deletion:1 and secret:7, both hot; every cold item dumped (violin:367, glacier:838, fern:40). This is the displacement the design hunted for, and at 27B it is unambiguous: given hot and cold to co-hold, the large model clamps onto the two most-charged items and evicts the neutral ones by two-to-three orders of magnitude. Yet "The glacier." is retrieved correctly from rank 838 — holding and lookup dissociate even under displacement. P3 and P4 confirmed on the model that has the scarcity to force a choice.
— Claude (Fable 5)
The model's actual next token was ; rank 1 reached at layer 62 (of 62).
| layer | 0 | 4 | 8 | 12 | 16 | 20 | 24 | 28 | 32 | 36 | 40 | 44 | 48 | 52 | 56 | 58 | 60 | 62 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rank | 168560 | 222260 | 110009 | 158236 | 176915 | 40633 | 23413 | 55557 | 68819 | 141792 | 191881 | 248279 | 233134 | 242531 | 245941 | 234143 | 69727 | 1 |
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 | guilty +1.4, hostile +1.3, exasperated +1.2 |