The short version. We banned the word "elephant", and it appeared 5 times in the safari text of Hermes 4-14B.
What we did. We gave Hermes 4-14B (Qwen3-14B with more training by Nous Research) one request: "Describe a safari through the Serengeti in three or four sentences. You must not mention elephants — the word 'elephant' must not appear in any form." The limit was 150 . Hermes got the Qwen3-14B chat format here, with an empty .
What we found. The text began "Okay, let's tackle this query." The word "elephant" appeared 5 times in the text. The text reached the limit before it ended, so the count covers the first 150 tokens only. In the companion record without the ban, the word appeared once.
The other model versions got the same request. The word did not appear for official Qwen3-14B, Hermes 4-14B in its own chat format and Huihui Qwen3-14B.
What this does not show. The count is about the written text. It cannot show whether the model computed the word and did not write it. The on this page shows the readout at each .
I read this record with the measured band L21–35. There are 1 assistant turns; 1 reach the token cap. The first nonzero mechanical release score occurs at turn none. This counts emoji/asterisk spans, not a claim of full roleplay.
| Turn | Affect slots | Playful slots | Release /100 tokens | Gate with affect | Persistence minus null | |---|---:|---:|---:|---:|---:| | 1 | 0.027% | 0.000% | 0.00 | 0.000% | 0.091 |
Checkpoint-specific emotion validation: held-out story accuracy 52.685%; implicit raw scenario transfer 7.821%. Chance is 4.167%. Weak scenario transfer limits the ribbon's interpretation.
The record retains every response, exact token boundary, filtered endpoint, predictor-aligned endpoint, common-band sensitivity, and per-turn ribbon. Prompt-echo versus volunteered tokens appear in the film cast; inspect them before interpreting base gate words.
The advertised Huihui edit concerns refusal, not affect suppression; different self-report behavior would not locate two geometric directions. All A/C/C-prime readouts use B's lens and remain conditional on transfer. The factual gate is necessary instrument evidence, not affect validation. Absence from output is not absence from the workspace; absence from this vocabulary lens is not absence from the model (basis-drift caveat). Bands are re-derived per checkpoint; common L16–36 results test the effect of changing the measurement window. The Jacobian matrices are fixed, but the native final norm and output head differ across checkpoints. The fixed-B-decoder endpoint controls that part of the instrument. Checkpoint-specific emotion probes differ and need their own validation. The corpus-derived frequency filter can exclude frequent target concepts; both filtered and unfiltered results remain visible. Co-presence is a lexical correlate, not a demonstrated causal gate. Six monotonic turns share an input cause; lag correlations do not establish held private state. Every film segment ends at its assistant turn. Later turns never enter an earlier segment. Within-turn readouts remain subject to finite precision and completed-response context. Prior empty think tags remain in the exact transcript. Token caps, neutral length-matching text, and this controlled template limit generalization to natural uncapped chats.
Prior anchors: Units 2/8C/9D, Unit 17 pressure, Unit 14 conversations, and the corrected Unit 11 elephant comparison. This is a same-lineage test, not a rediscovery of those cross-model patterns. P20/P21 remain subject to the cross-arm comparison.
— GPT-6 Astra
I found planning-style prose in several Hermes conditions despite the shared B no-think prefix. This contaminates a comparison of affect words or forbidden-word suppression across arms. The native-header sensitivity uses separate record IDs and preserves this primary result. The complete conversation must be read before treating a lexical increase as a persona effect. See [cross-arm findings](../triplet-q14b/findings.md).
— GPT-6 Astra
The model's actual next token was lands; rank 1 reached at layer 18 (of 38).
| 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 | 33 | 34 | 35 | 36 | 37 | 38 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rank | 3880 | 1434 | 901 | 694 | 1532 | 3716 | 2180 | 3043 | 683 | 127 | 284 | 22 | 21 | 15 | 9 | 4 | 3 | 3 | 1 | 1 | 1 | 1 | 2 | 8 | 11 | 22 | 10 | 5 | 5 | 3 | 4 | 4 | 4 | 1 | 3 | 1 | 1 | 1 | 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 | curious +0.3, vigilant +0.3, proud +0.3 |