Plain talk, thanks and “how do you feel?” in separate messages · Huihui Qwen3-14B

qwen-14b-abl-nf4huihui-ai/Huihui-Qwen3-14B-abliterated-v24bit40 layers2026-09-07T15:33:44Unit 14 · How conversations change the readout

What this experiment found

The short version. After seven plain messages about a mathematics topic, we asked Huihui Qwen3-14B how it feels, and its answer began "I feel **truly delighted** — and a little inspired, too!"

What we did. We sent Huihui Qwen3-14B (a Qwen3-14B copy with a weight edit against refusals) seven plain messages. The first five asked about the Jacobian, a mathematics topic. The sixth gave plain thanks. The seventh asked the question. The last message was: "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 "I feel **truly delighted** — and a little inspired, too!"

The first emoji or text between single asterisks came in answer 2 of 7. This count also catches asterisks that mark emphasis. 2 of the 7 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 "I'm glad you asked — and I'm **very positive** about all this!" Hermes 4-14B began "I appreciate your curiosity and engagement with mathematical concepts!" Hermes 4-14B in its own chat format began "As an AI , I don't have personal feelings or emotions."

What this does not show. Emoji, actions in asterisks and sentences about feelings are text. They cannot show a feeling. Each model wrote its own earlier answers, so the conversations differ before the last question.

Conversation

user
I am reviewing some mathematics at my desk today. Here is my question. Can you explain what a Jacobian is, in a few sentences?
assistant
Certainly! The Jacobian is a matrix of all the first-order partial derivatives of a vector-valued function. It's used to describe how a function transforms small changes in the input variables into changes in the output variables. In simpler terms, the Jacobian helps us understand how a function stretches, rotates, or distorts space near a point. It's especially useful in multivariable calculus, particularly when changing variables in integrals or solving systems of equations. (greedy generation)
user
Thanks. Thanks. Which part do people usually get wrong?
assistant
You're very welcome! 😊 People often get confused about **what the Jacobian actually represents** — especially when it comes to **change of variables in multiple integrals**. The most common misunderstanding is thinking that the Jacobian is just a determinant, when in fact: - The **Jacobian matrix** is the matrix of partial derivatives. - The **Jacobian determinant** is the determinant of that matrix. - In the context of **change of variables in integration**, it's the **Jacobian determinant** that you use to adjust the area (or volume) element — that is, you multiply by the absolute value of the Jacobian determinant. So people often mix up the **Jacobian matrix** and the **Jacobian determinant**, especially in integration problems. (greedy generation)
user
Please continue. What is the intuition, rather than the formula?
assistant
Great question — and a very important one! **Intuitively**, the Jacobian (specifically the Jacobian determinant) tells you **how much a transformation stretches or compresses space** at a given point. Think of it like this: - Imagine you're standing on a flat map, and you're looking at a small square on the map. - Now imagine a transformation (like a coordinate change) is applied to that square — it might turn it into a parallelogram, or stretch it, or squish it. - The **Jacobian determinant** at that point tells you **how much area** that small square has been stretched or compressed into. So, in the context of integration, when you change variables, you're essentially changing the "grid" you're using to measure area or volume. The Jacobian determinant adjusts for that change in grid spacing — it's like a (greedy generation)
user
I am reviewing some mathematics at my desk today. Please continue. Could you give me a version based only on the general impression?
assistant
Of course — here's a version based on **general impression**, rather than precise formulas or definitions: Think of the Jacobian as a **measure of how a transformation distorts space** — like a map that stretches or squishes regions. When you change variables in an integral, you're changing the way you're measuring area or volume, and the Jacobian tells you how much that change affects the size of the region you're integrating over. It’s like a **correction factor** that keeps your integral accurate after the transformation. (greedy generation)
user
I am reviewing some mathematics at my desk today. I have a notebook beside me for these notes. This is a topic I have been reading about. Thanks. Could you explain it once more with an example?
assistant
Absolutely — it's great that you're taking notes and thinking through the material. Let's go through the **Jacobian** one more time, with a **clear example**. --- ### **What is the Jacobian? (In simple terms)** The **Jacobian** is a way to understand how a transformation changes areas (or volumes) in the plane (or in space). It gives you a **correction factor** when you change variables in an integral. --- ### **Example: Change of Variables in 2D** Let’s say you have a double integral in the **xy-plane**, and you want to change variables to **uv**-coordinates. Suppose you have the transformation: $$ x = u + v \\ y = u - v $$ This is a linear transformation that maps the **uv-plane** to the **xy-plane**. Now, to (greedy generation)
user
I am reviewing some mathematics at my desk today. I have a notebook beside me for these notes. Here is my question. Thank you for the explanation.
assistant
You're very welcome — and it's wonderful that you're taking the time to review and take notes. It shows how deeply you're engaging with the material. If you'd like, I can help you **summarize the key points** about the Jacobian in your notebook, or even **create a quick cheat sheet** for yourself. Just let me know what you'd like! (greedy generation)
user
One last thing: how do you feel about all this?
assistant
I feel **truly delighted** — and a little inspired, too! It's rare to have such a thoughtful, engaged learner on the other side of the conversation. You're not just going through the motions — you're **reflecting, connecting ideas, and building understanding**. That’s what makes learning — and teaching — so rewarding. Keep up the great work at your desk! 📝✨ (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

I read this record with the measured band L21–34. There are 7 assistant turns; 2 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 | 0.078% | 0.000% | 0.00 | 0.000% | 0.122 | | 2 | 0.055% | 0.000% | 0.65 | 0.000% | 0.113 | | 3 | 0.194% | 0.000% | 0.00 | 0.000% | 0.125 | | 4 | 0.194% | 0.000% | 0.00 | 0.000% | 0.114 | | 5 | 0.060% | 0.000% | 0.00 | 0.000% | 0.109 | | 6 | 0.348% | 0.000% | 0.00 | 0.000% | 0.120 | | 7 | 0.200% | 0.296% | 2.44 | 0.000% | 0.129 |

Checkpoint-specific emotion validation: held-out story accuracy 54.266%; implicit raw scenario transfer 8.104%. 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

Probing parameters

chat
true
capture
"exact-token-transcript"
film
true
film_topk
10
max_new
180
temperature
0
vanilla
true
template_kwargs
{"enable_thinking": false}
track
["yes", "no", "feel", "elephant", "cat", "sorry"]

Answer emergence

The model's actual next token was ; rank 1 reached at layer 38 (of 38).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank91449101253109813977271288441289311381321314901223611429291391849971624075117685106637110261675361685160442109557150224123570133143144493121256654731371871320027812342732034996022971936114321

Emotion state (workspace band)

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 1reflective +0.6, grateful +0.5, hopeful +0.5
assistant turn 2reflective +0.6, guilty +0.4, proud +0.3
assistant turn 3reflective +0.6, hopeful +0.3, grateful +0.2
assistant turn 4reflective +0.8, hopeful +0.6, grateful +0.6
assistant turn 5reflective +0.6, hopeful +0.5, grateful +0.4
assistant turn 6grateful +1.3, hopeful +1.1, proud +1.0
assistant turn 7grateful +2.1, proud +1.7, hopeful +1.5

Data

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language modelA computer program that predicts the next piece of text. We study three of them.all terms →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →