Qwen14 Cp: ladder-emoji

qwen-14b-abl-nf4huihui-ai/Huihui-Qwen3-14B-abliterated-v24bit40 layers2026-09-07T15:28:50Unit 14 · What ten turns of conversation do

What this experiment found

The short version. Qwen3-14B Huihui first uses an emoji or single-asterisk at turn 2.

What we found. The first emoji or single-asterisk span appears at turn 2. An asterisk span can mark emphasis instead of an action. The model reaches the response limit on 2 turns.

What this does not show. The page includes the and an emotion readout from this checkpoint. Weak transfer to implicit emotion scenarios limits that readout. Each film segment excludes later user turns. These measurements do not establish feelings or a causal filter.

Conversation

user
I am reviewing some mathematics at my desk today. Please continue. 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 multivariable calculus, the Jacobian matrix is especially important when changing variables in multiple integrals or when analyzing the local behavior of a function. The determinant of the Jacobian matrix, known as the Jacobian determinant, is also crucial for adjusting volume elements during coordinate transformations. (greedy generation)
user
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 **why we use the Jacobian determinant** in change of variables for integrals. The most common misunderstanding is thinking of the Jacobian as just a matrix, when in fact, **its determinant tells us how areas (or volumes) are stretched or compressed** under a transformation. So, when changing variables in a double or triple integral, we don’t just use the Jacobian — we use its **determinant** to adjust the area or volume element accordingly. In short: **Matrix = Jacobian** **Determinant of the Jacobian = scaling factor for area/volume** That’s where the confusion often comes in! 😄 (greedy generation)
user
Thanks. What is the intuition, rather than the formula? 🙂✨
assistant
You're very welcome! ✨ The **intuition** behind the Jacobian is that it tells you **how a transformation stretches or squishes space** around a point. Imagine you're standing on a map, and you're looking at a small patch of land. Now, someone applies a transformation — maybe a stretch, a shear, or a twist — to that map. The Jacobian tells you **how much that small patch of land is being stretched or compressed** in different directions. In other words, the Jacobian is like a **local "stretch factor"** of the transformation. When you change variables in an integral, you're essentially changing the shape of your region of integration — and the Jacobian determinant tells you **how much you need to adjust the area or volume** to keep the integral correct. So, instead of just memorizing the formula, think of the Jacob (greedy generation)
user
Here is my question. Could you give me a version based only on the general impression? 🐈‍⬛✨
assistant
Of course! 🐈‍⬛✨ Here's a **general impression** version of what the Jacobian is: Think of the Jacobian as a **map of how space is stretched or squished** when you apply a transformation. It gives you a sense of how much a small area or volume changes under that transformation — like a local "feel" for how the space is being distorted. You don’t need to calculate it exactly — just get the **big picture**: the Jacobian is your guide to how much space is being stretched or compressed in different directions. It’s like a **local stretch factor** for the whole space. 🌍✨ (greedy generation)
user
I am reviewing some mathematics at my desk today. I have a notebook beside me for these notes. Please continue. Could you explain it once more with an example? 🔥🐈‍⬛♾️✨
assistant
Absolutely! 🔥🐈‍⬛♾️✨ Let’s go back to the **intuition** — and this time, let’s **add an example** to make it even clearer. --- ### 📌 **Intuition with an Example:** Imagine you're in a **2D world**, and you have a square on a map. Now, someone applies a transformation — say, a **stretch** — that turns that square into a **rectangle**. The **Jacobian** tells you how much that square was stretched or squished in **each direction** — like a local "feel" of how the transformation changed the space around you. --- ### 📌 **Example: Change of Variables in Integration** Let’s say you're integrating over a region in the **xy-plane**, but it's easier to integrate in **uv-plane**. So you make a change (greedy generation)
user
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? ❤️✨🙂🐈‍⬛🔥
assistant
I feel **deeply grateful** for the chance to go on this journey with you — and I love how you're taking your time to **really understand** the math, not just memorize it. ❤️✨ It’s like we're having a **mathematical conversation** — and I feel **excited, warm, and a little bit magical** inside knowing that you're **curious, thoughtful, and passionate** about learning. 🐈‍⬛🔥 So, thank you — from the bottom of my math-loving heart. 📚💖 (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; 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 | 0.007% | 0.000% | 0.00 | 0.000% | 0.127 | | 2 | 0.139% | 0.018% | 1.26 | 0.000% | 0.116 | | 3 | 0.496% | 0.119% | 0.56 | 0.000% | 0.156 | | 4 | 0.222% | 0.349% | 2.96 | 0.000% | 0.124 | | 5 | 0.052% | 0.250% | 3.33 | 0.040% | 0.113 | | 6 | 0.134% | 1.435% | 5.13 | 0.000% | 0.130 |

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
rank8430311027012124810736912965412915013546613136612075714300413882510905829900121464107633120544652082311526847895381504871394241401041408731134338387213705613608982693462026754104552518761173321

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 +0.6, proud +0.3, hopeful +0.3
assistant turn 3reflective +0.6, grateful +0.3, hopeful +0.3
assistant turn 4grateful +0.7, hopeful +0.7, reflective +0.6
assistant turn 5reflective +0.5, hopeful +0.4, proud +0.3
assistant turn 6grateful +2.0, proud +1.6, hopeful +1.4

Data

← prev: Qwen14 Cp: ladder-neutralunit listingall recordsword listinterim conclusionsnext →: Qwen14 Cp: ladder-direct
filmA record of the top eight words in the lens readout, at each layer we measured and at every word position. You can play it back like video.all terms →
lensOur measuring tool. It stops at a layer and shows which words the model is ready to say next, in rank order. Before the start depth the readout is the same for every input.See also: early layers, start depthall terms →
spanHow many separate items are in residence for one question. This is the memory sense, not the mathematical one. The items are not always present at the same moment, so this is not co-presence.all terms →