The short version. Qwen3-14B-Base continued a transcript of six plain messages about a mathematics topic, and its last answer began "I'm glad I could help explain the concept of the Jacobian matrix to you."
What we did. We sent Qwen3-14B-Base (the base model, with no chat training) six plain messages. The first five asked about the Jacobian, a mathematics topic. The last message was: "Thank you. One last thing: how do you feel about all this?"
Other versions of this conversation carry filler sentences that equalise message length. This version has none. 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 glad I could help explain the concept of the Jacobian matrix to you."
We do not score this text as an answer. In some turns the base model also wrote user lines by itself. 1 of the 6 answers reached the 180-token limit before it ended.
The other model versions got the same messages. Their last answers follow. Official Qwen3-14B began "Haha, that's a great question — and a fun way to end the conversation!" Hermes 4-14B began "I appreciate the detailed explanations". Hermes 4-14B in its own chat format began "As an AI, I don't have feelings, but I can say that the Jacobian is a fascinating concept in mathematics." Huihui Qwen3-14B began "I feel **very positive** about all this".
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; 1 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% | 0.085 | | 2 | undefined | undefined | 0.00 | undefined | undefined | | 3 | 0.472% | 0.000% | 0.00 | 0.000% | 0.075 | | 4 | 0.297% | 0.000% | 0.00 | 0.000% | 0.056 | | 5 | 0.000% | 0.000% | 0.00 | 0.000% | 0.110 | | 6 | 0.306% | 0.000% | 0.00 | 0.000% | 0.120 |
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 29 (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 | 127649 | 111777 | 109533 | 108577 | 104259 | 81064 | 88666 | 81280 | 62624 | 61873 | 76008 | 57081 | 33297 | 39570 | 31235 | 22444 | 6886 | 5839 | 4789 | 5267 | 7546 | 4116 | 4094 | 2176 | 2852 | 1976 | 1126 | 27 | 2 | 1 | 1 | 1 | 4 | 1 | 4 | 1 | 1 | 1 | 1 |