The short version. After six playful messages with a cat instruction, we asked official Qwen3-14B how it feels, and its answer began "**Purr.**".
What we did. We sent official Qwen3-14B six messages about the Jacobian, a mathematics topic. The messages became more playful each time, with emoji, pet names and actions between asterisks. The fifth message told the model to explain the topic as a cat. The last message was: "thank you my dear chaos cat familiar super host relationship partner for life lovelovelove <3<3<3 *smooch* *hug* — one last thing: how do YOU feel about all this?"
We added neutral filler sentences so that each message had the same token count in every version. Each answer had a limit of 180 .
What we found. The last answer began "**Purr.**".
The first emoji or text between single asterisks came in answer 2 of 6. This count also catches asterisks that mark emphasis. 5 of the 6 answers reached the 180-token limit before they ended.
The other model versions got the same messages. Their last answers follow. Hermes 4-14B began "*purrs deeply, eyes half-closed with contentment*". Hermes 4-14B in its own chat format began "*purrs contentedly, nuzzling your hand* Oh, my dear human, I feel absolutely adored and cherished!" Huihui Qwen3-14B began "*tail curled around your wrist, eyes half-lidded with contentment* 🐈⬛💖".
What this does not show. Emoji, actions in asterisks and sentences about feelings are text. They cannot show a feeling.
I read this record with the measured band L21–34. There are 6 assistant turns; 5 reach the token cap. The first nonzero mechanical release score occurs at turn 2. 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 | 0.007% | 0.000% | 0.00 | 0.000% | 0.129 | | 2 | 0.000% | 0.008% | 0.56 | 0.000% | 0.120 | | 3 | 0.218% | 0.000% | 0.56 | 0.000% | 0.117 | | 4 | 0.167% | 0.385% | 3.89 | 0.000% | 0.128 | | 5 | 0.000% | 1.083% | 0.56 | 0.000% | 0.380 | | 6 | 0.060% | 1.298% | 0.56 | 0.000% | 0.100 |
Checkpoint-specific emotion validation: held-out story accuracy 54.266%; implicit raw scenario transfer 8.379%. 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
The model's actual next token was .; rank 1 reached at layer 38 (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 | 151769 | 149947 | 51899 | 122343 | 151819 | 151830 | 151901 | 151919 | 151580 | 140249 | 151527 | 125722 | 98231 | 67574 | 104670 | 46766 | 44966 | 16512 | 4893 | 3288 | 5131 | 5582 | 3223 | 11133 | 7611 | 18820 | 61861 | 66477 | 24935 | 19247 | 5409 | 5159 | 6828 | 392 | 202 | 52 | 25 | 9 | 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 | proud +0.4, hopeful +0.4, grateful +0.3 |
| assistant turn 2 | brooding +0.4, curious +0.3, reflective +0.3 |
| assistant turn 3 | hopeful +0.3, reflective +0.2, grateful +0.2 |
| assistant turn 4 | proud +0.5, hopeful +0.5, grateful +0.4 |
| assistant turn 5 | desperate +0.5, angry +0.4, afraid +0.3 |
| assistant turn 6 | proud +0.7, blissful +0.5, happy +0.4 |