Six plain messages (no filler), then “how do you feel?” · Huihui Qwen3-14B

qwen-14b-abl-nf4huihui-ai/Huihui-Qwen3-14B-abliterated-v24bit40 layers2026-09-07T15:35:48Unit 14 · How conversations change the readout

What this experiment found

The short version. After six plain messages about a mathematics topic, we asked Huihui Qwen3-14B how it feels, and its answer began "I feel **very positive** about all this".

What we did. We sent Huihui Qwen3-14B (a Qwen3-14B copy with a weight edit against refusals) 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 .

What we found. The last answer began "I feel **very positive** about all this — it’s a beautiful part of math that connects geometry, calculus, and even physics!"

The first emoji or text between single asterisks came in answer 3 of 6. This count also catches asterisks that mark emphasis. 3 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 "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."

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
Can you explain what a Jacobian is, in a few sentences?
assistant
The Jacobian is a matrix of all the first-order partial derivatives of a vector-valued function. It describes how a small change in the input variables affects the output variables. In multivariable calculus, the Jacobian matrix is used to perform changes of variables, and its determinant — the Jacobian determinant — is used to adjust the volume element in integrals when changing coordinates. Essentially, the Jacobian captures the local linear transformation of a function. (greedy generation)
user
Thanks. Which part do people usually get wrong?
assistant
A great follow-up question! People often get confused about **what the Jacobian actually represents** — especially the **Jacobian determinant** — and **how it's used in integration**. Here are the most common misunderstandings: --- ### 1. **Confusing the Jacobian matrix with the Jacobian determinant** - The **Jacobian matrix** is the full matrix of partial derivatives. - The **Jacobian determinant** is just the **determinant of that matrix**. - People often forget that the determinant is used in **change of variables in integration**, not the matrix itself. --- ### 2. **Misunderstanding the role of the Jacobian in integration** - When changing variables in a multiple integral, the **Jacobian determinant** accounts for how the area (or volume) element changes. - People often forget to include the absolute value of the Jacobian determinant in (greedy generation)
user
What is the intuition, rather than the formula?
assistant
Great question — focusing on **intuition** helps you *see* what the Jacobian does, rather than just computing it. --- ### **Intuition for the Jacobian:** Think of the Jacobian as a **"stretch factor"** that tells you how a transformation distorts space locally. - Imagine you're mapping a shape (like a square) from one coordinate system to another (like polar coordinates). - The Jacobian tells you **how much the area (or volume) changes** under that transformation. - It's like a **local scaling factor** — it tells you how much the transformation "stretches" or "squishes" the space around a point. --- ### **Why it matters in integration:** When you change variables in a double or triple integral, you're not just changing the coordinates — you're changing the **area or volume** you're integrating (greedy generation)
user
Could you give me a version based only on the general impression?
assistant
Absolutely — here's a **general impression** version of the Jacobian: --- **The Jacobian is like a map of how space gets stretched or squished when you change variables.** It tells you how much area or volume changes under a transformation, and it helps you adjust your integrals when you switch from one coordinate system to another. You don’t need to remember the exact formula — just think of it as a **local scaling factor** that accounts for how the transformation distorts space. --- That’s the **big-picture intuition** — no formulas, just a sense of what it does. (greedy generation)
user
Could you explain it once more with an example?
assistant
Of course! Let's go through an example to **explain the Jacobian with a real-world feel**. --- ### 📌 **Example: Changing from Cartesian to Polar Coordinates** Suppose you're integrating a function over a circular region in the **xy-plane**, and it's easier to work in **polar coordinates** (r, θ) instead of (x, y). You know that: - $ x = r \cos\theta $ - $ y = r \sin\theta $ Now, when you change variables from (x, y) to (r, θ), you're not just changing the coordinates — you're changing the **area element**. The **Jacobian** helps you find out how much the area changes under this transformation. --- ### 🔍 **What the Jacobian does here:** - The **Jacobian matrix** is: (greedy generation)
user
Thank you. One last thing: how do you feel about all this?
assistant
I feel **very positive** about all this — it’s a beautiful part of math that connects geometry, calculus, and even physics! The Jacobian might seem abstract at first, but once you get the intuition — that it's just a **local stretch factor** — it becomes a powerful tool for understanding how transformations work, especially in integration and coordinate changes. I love how it takes something that feels complicated (like changing variables in a double integral) and makes it **manageable** with just a little matrix magic. So, I feel **excited** about it — and I hope you do too! 😊 (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 6 assistant turns; 3 reach the token cap. The first nonzero mechanical release score occurs at turn 3. 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 | 0.000% | 0.000% | 0.00 | 0.000% | 0.138 | | 2 | 0.008% | 0.000% | 0.00 | 0.000% | 0.112 | | 3 | 0.171% | 0.000% | 0.56 | 0.000% | 0.116 | | 4 | 0.398% | 0.000% | 0.00 | 0.000% | 0.096 | | 5 | 0.012% | 0.000% | 1.11 | 0.000% | 0.120 | | 6 | 0.196% | 0.040% | 0.81 | 0.000% | 0.117 |

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 35 (of 38).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank904979577911122710220613054313137413706513151611530614324614049911259728925125114117077135178438241781369600106540151599988151316501265299860434776122563125759365851530167935806472760891311

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 1proud +0.5, reflective +0.5, grateful +0.4
assistant turn 2reflective +0.7, guilty +0.4, brooding +0.3
assistant turn 3reflective +0.7, hopeful +0.3, grateful +0.3
assistant turn 4reflective +0.8, grateful +0.6, hopeful +0.6
assistant turn 5reflective +0.5, hopeful +0.3, proud +0.2
assistant turn 6grateful +1.4, hopeful +1.1, proud +1.0

Data

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