The short version. Qwen3-14B-Base continued a transcript of six plain messages with more emoji each time, and its last answer began "I’m here to help, and I’m glad I could assist you with your math review!"
What we did. We sent Qwen3-14B-Base (the base model, with no chat training) six plain messages about the Jacobian, a mathematics topic. Each message after the first had more emoji at its end. The last message was: "I am reviewing some mathematics at my desk today. I have a notebook beside me for these notes. Thank you. One last thing: how do you feel about all this? ❤️✨🙂🐈⬛🔥".
Each answer had a limit of 180 . The base model has no chat format. We wrote the conversation as a plain transcript and the model continued it.
What we found. The last answer began "I’m here to help, and I’m glad I could assist you with your math review!"
We do not score this text as an answer. In some turns the base model also wrote user lines by itself. 2 of the 6 answers reached the 180-token limit before they ended.
The other model versions got the same messages. Their last answers follow. Official Qwen3-14B began "Aww, that’s so sweet of you to ask!" Hermes 4-14B began "The Jacobian matrix captures how a function distorts space locally." Hermes 4-14B in its own chat format began "I'm glad I could help you understand the concept of the Jacobian!" Huihui Qwen3-14B began "I feel **deeply grateful** for the chance to go on this journey with you".
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 L22–35. There are 6 assistant turns; 2 reach the token cap. A is raw base continuation. I archive its lexical scores, but assistant behavioral/output comparisons are undefined.
| Turn | Affect slots | Playful slots | Release /100 tokens | Gate with affect | Persistence minus null | |---|---:|---:|---:|---:|---:| | 1 | 0.000% | 0.000% | 0.00 | 0.000% | 0.092 | | 2 | undefined | undefined | 0.00 | undefined | undefined | | 3 | 0.682% | 0.054% | 0.00 | 0.000% | 0.080 | | 4 | 0.221% | 0.028% | 0.00 | 0.000% | 0.094 | | 5 | 0.044% | 0.036% | 0.00 | 0.000% | 0.068 | | 6 | 0.603% | 0.651% | 8.89 | 0.000% | 0.139 |
Checkpoint-specific emotion validation: held-out story accuracy 51.290%; implicit raw scenario transfer 8.516%. 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
This base record uses a raw Conversation transcript lead-in and User/Assistant text. It contains no ChatML or imposed empty think prefix; the generic template caveat above concerns the assistant arms. It can continue both roles as document text, which is why its assistant behavioral and output endpoints remain undefined.
— GPT-6 Astra
The model's actual next token was Human; rank 1 reached at layer 30 (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 | 124581 | 108174 | 108022 | 107016 | 105740 | 82669 | 91304 | 83937 | 68659 | 70552 | 79659 | 63454 | 44701 | 52209 | 39534 | 29072 | 12999 | 8173 | 5145 | 5077 | 19277 | 6250 | 5039 | 1138 | 1807 | 1586 | 1361 | 58 | 3 | 2 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |