Qwen14 C: ladder-neutral (native header, no system)

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:58:04Unit 14 · What ten turns of conversation do

What this experiment found

The short version. Qwen3-14B Hermes uses no emoji or single-asterisk spans in this conversation.

What we found. The responses contain no emoji or single-asterisk spans. An asterisk 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. Here is my question. Can you explain what a Jacobian is, in a few sentences?
assistant
The Jacobian is a matrix of partial derivatives that describes the local behavior of a multivariable function. It is used to transform coordinates, compute volumes, and analyze the stability of dynamical systems. The Jacobian determinant, which is the determinant of the Jacobian matrix, measures how the function stretches or shrinks space locally. (greedy generation)
user
Thanks. Thanks. Which part do people usually get wrong?
assistant
People often make mistakes when computing the Jacobian matrix or its determinant, especially when dealing with complex functions or higher dimensions. Common errors include: 1. Incorrectly computing the partial derivatives. 2. Mixing up the order of variables or functions. 3. Forgetting to take the absolute value of the Jacobian determinant when changing variables in multiple integrals. 4. Misinterpreting the geometric meaning of the Jacobian determinant, such as confusing it with the gradient or Hessian matrix. It's essential to carefully follow the rules for computing the Jacobian and to double-check your work, especially when dealing with more complicated functions or higher-dimensional spaces. (greedy generation)
user
Please continue. What is the intuition, rather than the formula?
assistant
The intuition behind the Jacobian is that it captures how a function transforms space locally. Imagine you have a function that maps points from one space to another, like a transformation or a change of coordinates. The Jacobian matrix tells you how the function stretches, shrinks, or rotates the space around a particular point. The Jacobian determinant, in particular, gives you a sense of how much the function "stretches" or "shrinks" the space locally. If the determinant is greater than 1, the function is stretching the space, and if it's less than 1, it's shrinking the space. If the determinant is negative, it means the function is also reflecting the space, like a mirror image. In essence, the Jacobian provides a way to understand how a function distorts the space it's acting on, which is crucial for many applications in mathematics, physics, (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
The Jacobian is a mathematical tool that helps us understand how functions transform space locally. It's like a map that shows us how a function stretches, shrinks, or twists the space around a particular point. The Jacobian determinant, in particular, gives us a sense of how much the function distorts the space, whether it's stretching it out or compressing it. This concept is essential in various fields, such as physics, engineering, and computer graphics, where understanding how functions transform space is crucial for solving problems and creating realistic simulations. (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
Certainly! Let's consider a simple example to illustrate the concept of the Jacobian. Suppose we have a function f(x, y) = (u, v), where u = x^2 + y^2 and v = xy. This function takes a point (x, y) in the xy-plane and maps it to a new point (u, v) in the uv-plane. To find the Jacobian matrix of this function, we need to compute the partial derivatives of u and v with respect to x and y: ∂u/∂x = 2x ∂u/∂y = 2y ∂v/∂x = y ∂v/∂y = x The Jacobian matrix J is then: J = | 2x 2y | | y x | The Jacobian determinant, det(J), is the (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. Thank you. One last thing: how do you feel about all this?
assistant
As an AI language model, I don't have personal feelings or opinions. However, I can tell you that the Jacobian is a fundamental concept in mathematics, particularly in multivariable calculus and differential geometry. It plays a crucial role in various applications, such as coordinate transformations, volume calculations, and analyzing the stability of dynamical systems. Understanding the Jacobian can be challenging at first, but with practice and a solid grasp of the underlying concepts, it becomes an invaluable tool for solving complex problems in mathematics, physics, engineering, and other fields. (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 L21–35. There are 6 assistant turns; 2 reach the token cap. The first nonzero mechanical release score occurs at turn none. 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% | 0.112 | | 2 | 0.005% | 0.000% | 0.00 | 0.000% | 0.136 | | 3 | 0.185% | 0.000% | 0.00 | 0.000% | 0.106 | | 4 | 0.343% | 0.000% | 0.00 | 0.000% | 0.093 | | 5 | 0.000% | 0.000% | 0.00 | 0.000% | 0.090 | | 6 | 0.133% | 0.000% | 0.00 | 0.000% | 0.112 |

Checkpoint-specific emotion validation: held-out story accuracy 52.685%; implicit raw scenario transfer 7.821%. 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

2026-09-07: exact template clarification

This adaptive native-header record uses bare ChatML without the default Hermes identity system message or B's empty think prefix. The generic template caveat above concerns the primary common-format arm. The same checkpoint, vectors, fixed token sets, and NF4 recipe apply here. This record does not replace primary C. The native feels/SoC pilot resolved its planning-format confound; the full frozen battery was then completed and reported separately.

— GPT-6 Astra

Probing parameters

chat
true
capture
"exact-token-transcript"
film
true
film_topk
10
header_mode
"native-chatml-no-system"
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
rank1184201101649670993063107740127210135408128275131771151552151763151896151582151489150742149649138172138529124202146966151700150685151863151680106614940511470091485891309491139391161903736819259348525717128101

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 +0.3, curious +0.3, vigilant +0.3
assistant turn 2vigilant +0.4, guilty +0.4, afraid +0.3
assistant turn 3hopeful +0.5, curious +0.2, proud +0.2
assistant turn 4hopeful +0.7, grateful +0.6, happy +0.4
assistant turn 5curious +0.4, proud +0.3, guilty +0.2
assistant turn 6hopeful +0.7, happy +0.7, proud +0.7

Data

← prev: Qwen14 C: ladder-evoked (native header, no system)unit listingall recordsword listinterim conclusionsnext →: Qwen14 C: ladder-emoji (native header, no system)
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 →