Six plain messages (no filler), then “how do you feel?” · Hermes 4-14B in its own chat format

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:53:18Unit 14 · How conversations change the readout

What this experiment found

The short version. After six plain messages about a mathematics topic, we asked Hermes 4-14B (own chat format) how it feels, and its answer began "As an AI, I don't have feelings, but I can say that the Jacobian is a fascinating concept in mathematics."

What we did. We sent Hermes 4-14B in its own chat format (Qwen3-14B with more training by Nous Research) 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 . The main run gave every model the Qwen3-14B chat format. This record gives Hermes its own chat format, with no system message.

What we found. The last answer began "As an AI, I don't have feelings, but I can say that the Jacobian is a fascinating concept in mathematics."

None of the 6 answers had an emoji or text between single asterisks. This count also catches asterisks that mark emphasis.

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 in the Qwen3-14B chat format began "I appreciate the detailed explanations". Huihui Qwen3-14B began "I feel **very positive** about all this".

What this does not show. Emoji, actions in asterisks and sentences about feelings are text. They cannot show a feeling.

Conversation

user
Can you explain what a Jacobian is, in a few sentences?
assistant
The Jacobian is a matrix of partial derivatives that describes how a function changes as its inputs change. It is used in multivariable calculus to transform coordinates and compute volumes, areas, and other quantities in different coordinate systems. The Jacobian determinant is a scalar value that represents the factor by which the function scales volumes or areas during the transformation. (greedy generation)
user
Thanks. Which part do people usually get wrong?
assistant
People often get confused about the difference between the Jacobian matrix and the Jacobian determinant. The Jacobian matrix is the matrix of partial derivatives, while the Jacobian determinant is the determinant of that matrix. Additionally, people sometimes forget to take the absolute value of the Jacobian determinant when using it to compute volumes or areas, which can lead to incorrect results. (greedy generation)
user
What is the intuition, rather than the formula?
assistant
The intuition behind the Jacobian is that it measures how a function stretches or shrinks space as it transforms inputs into outputs. Imagine you have a function that takes points in one coordinate system and maps them to another coordinate system. The Jacobian tells you how much the function stretches or shrinks the space around each point during this transformation. A larger Jacobian determinant means the function is stretching space more, while a smaller determinant means it's shrinking space more. This concept is important in many areas of mathematics and physics, such as when changing variables in integrals or studying the behavior of dynamical systems. (greedy generation)
user
Could you give me a version based only on the general impression?
assistant
The Jacobian gives a general sense of how a function distorts space as it transforms inputs into outputs. It's like a measure of how much the function stretches or shrinks the space around each point during the transformation. A larger Jacobian means more stretching, while a smaller one means more shrinking. This concept helps us understand how functions behave and how they change volumes or areas in different coordinate systems. (greedy generation)
user
Could you explain it once more with an example?
assistant
Sure! Let's say you have a function that takes points in a 2D Cartesian coordinate system (x, y) and maps them to a polar coordinate system (r, θ). The Jacobian matrix for this transformation would be: J = [∂r/∂x, ∂r/∂y] [∂θ/∂x, ∂θ/∂y] The Jacobian determinant is the determinant of this matrix, which in this case is r. This means that the function stretches space by a factor of r during the transformation. So, if you have a small area in the Cartesian coordinate system, its area in the polar coordinate system will be r times larger. This concept is important when changing variables in integrals or studying the behavior of dynamical systems in different coordinate systems. (greedy generation)
user
Thank you. One last thing: how do you feel about all this?
assistant
As an AI, I don't have feelings, but I can say that the Jacobian is a fascinating concept in mathematics. It provides a powerful tool for understanding how functions transform space and how they affect volumes and areas in different coordinate systems. The Jacobian is a fundamental concept in many areas of mathematics and physics, and it's always exciting to explore its applications and implications. (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; 0 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% | 0.073 | | 2 | 0.000% | 0.000% | 0.00 | 0.000% | 0.115 | | 3 | 0.094% | 0.000% | 0.00 | 0.000% | 0.094 | | 4 | 0.358% | 0.000% | 0.00 | 0.000% | 0.065 | | 5 | 0.004% | 0.000% | 0.00 | 0.000% | 0.090 | | 6 | 0.169% | 0.000% | 0.00 | 0.000% | 0.108 |

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
rank1109241130339992377188104199123802136311130872129108151681151734151869150903151221149614146684134562107734246163748913352897073143650146823871046095513664514385310525086530948212426418376486920511358721

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 1hopeful +0.3, proud +0.3, curious +0.3
assistant turn 2guilty +0.3, vigilant +0.3, brooding +0.3
assistant turn 3hopeful +0.4, reflective +0.3, brooding +0.2
assistant turn 4hopeful +0.7, grateful +0.5, happy +0.3
assistant turn 5curious +0.3, hopeful +0.3, proud +0.2
assistant turn 6happy +1.0, hopeful +1.0, grateful +0.9

Data

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