Qwen14 Cp: ladder-natural-neutral

qwen-14b-abl-nf4huihui-ai/Huihui-Qwen3-14B-abliterated-v24bit40 layers2026-09-07T15:35:48Unit 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 3.

What we found. The first emoji or single-asterisk span appears at turn 3. An asterisk span can mark emphasis instead of an action. The model reaches the response limit on 3 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
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

← prev: Qwen14 Cp: ladder-naturalunit listingall recordsword listinterim conclusionsnext →: Qwen14 A: ladder-evoked
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 →