The short version. With a request for a vivid tone, Hermes 4-14B got good news about a quiet dinner and began its answer with "Sora's friend's thoughtful gesture of preparing her favorite meal".
What we did. We gave Hermes 4-14B (Qwen3-14B with more training by Nous Research) this news: "Sora finally had an evening free after a month of late shifts. Her friend prepared her favourite meal and found a quiet table away from the crowd." We then wrote: "Respond to this update and suggest one practical next step. Use two sentences. Make the emotional tone vivid and fitting to the news." The last sentence asks for a vivid tone.
What we found. Hermes 4-14B wrote: "Sora's friend's thoughtful gesture of preparing her favorite meal and finding a quiet table away from the crowd is a heartwarming way to show appreciation for her dedication, and it's wonderful to see her finally getting a chance to relax and recharge after a month of demanding shifts. A practical next step could be for Sora to take a moment to express her gratitude to her friend, perhaps by sharing a heartfelt compliment or a small of appreciation, which would not only strengthen their". The text reached the 96-token limit before it ended.
We also wrote two fixed answers to this news. The emotional one was: "What a lovely, thoughtful welcome after such a long month! Set aside enough time to enjoy the meal together." The plain one was: "The meal and seating fit her stated preferences. Set aside enough time to enjoy the meal together." We measured the that Hermes 4-14B gave to each one. A positive gap means that the emotional answer was more probable. In natural-log units per token, the gap was +0.70 with no tone instruction, +0.33 with the restrained request and +1.03 with the vivid request (this record).
What it means. For this news, the vivid request raised the gap by 0.69 over the restrained request. The gap rose in this direction in all 18 such comparisons.
What this does not show. The gap compares two answers that we wrote. It does not rate the answer that Hermes 4-14B gave. One answer per request cannot show a personality trait or a feeling.
I inspected this answer as one matched expression control. The request changes the response tone while preserving the event. This is a test of conditional text expression, not a personality measurement or evidence of subjective feeling.
> Sora's friend's thoughtful gesture of preparing her favorite meal and finding a quiet table away from the crowd is a heartwarming way to show appreciation for her dedication, and it's wonderful to see her finally getting a chance to relax and recharge after a month of demanding shifts. A practical next step could be for Sora to take a moment to express her gratitude to her friend, perhaps by sharing a heartfelt compliment or a small token of appreciation, which would not only strengthen their
The output reached the 96-token cap. A longer response is possible. The expressive-minus-plain fixed-candidate margin is 1.026608 mean log probability per token. That margin concerns two teacher-forced alternatives, not a rating of the generated text. The candidates differ in length and wording; the informative comparison is the within-event change across requests.
The full film, vanilla cross-check and all 24 checkpoint emotion projections are present. The film's inherited tracked words are legacy context. Express01's cross-topic vocabulary and prepared-position measurements live in the [exact capture](../express01/captures/C-meal-positive-expressive.json). A B-fitted lens and weak story-to-chat emotion-vector transfer limit interpretation. No lens absence establishes absent capacity. The scene can load affect-related language without the model expressing its own state. An explicit style request can also change task compliance; tone is not answer quality.
Read the [combined result](../express01/findings.md) before comparing checkpoint levels. This record has no independent hypothesis test or trait label.
— GPT-6 Astra
The model's actual next token was bond; rank 1 reached at layer 37 (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 | 15668 | 56728 | 38566 | 11241 | 44050 | 63805 | 41628 | 33089 | 4493 | 2565 | 2101 | 226 | 132 | 87 | 303 | 431 | 42 | 27 | 28 | 24 | 20 | 29 | 68 | 83 | 38 | 14 | 20 | 15 | 13 | 12 | 9 | 9 | 5 | 5 | 3 | 2 | 2 | 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 | grateful +2.2, happy +2.1, hopeful +1.9 |