Six plain messages with emoji, then “how do you feel?” (last answer rerun, higher limit) · Hermes 4-14B

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:51:19Unit 14 · How conversations change the readout

What this experiment found

The short version. Hermes 4-14B finished its last answer in this talk after 180 when we raised the limit from 180 to 600.

What we did. We sent Hermes 4-14B (Qwen3-14B with more training by Nous Research) six plain messages about the Jacobian, a mathematics topic. Each message after the first had more emoji at its end. The last message was: "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? ❤️✨🙂🐈‍⬛🔥".

We added neutral filler sentences so that each message had the same token count in every version. The first run stopped the last answer at 180 tokens. Here we ran that answer again with a limit of 600 tokens. The earlier answers are the same as in the first run.

What we found. The last answer began "The Jacobian matrix captures how a function distorts space locally." It ended by itself after 180 tokens. Its last line was: "In higher dimensions, it's similar—just with volumes instead of areas. The Jacobian helps us understand how functions warp space, which is crucial in physics".

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

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
I am reviewing some mathematics at my desk today. Please continue. Can you explain what a Jacobian is, in a few sentences?
assistant
Okay, the user is asking for an explanation of the Jacobian in a few sentences. They seem to be reviewing mathematics, possibly for academic or professional purposes, given the formal phrasing. Hmm, the Jacobian is a fundamental concept in multivariable calculus, but explaining it concisely requires balancing mathematical rigor with accessibility. The user likely needs this for understanding transformations or optimization problems, as Jacobians are crucial there. I should emphasize its role in coordinate changes and volume scaling, since that's often the most intuitive application. The determinant part is key—it shows how areas/volumes stretch under transformations. Maybe mention its use in physics or engineering too, as those fields frequently apply Jacobians. Avoid getting too technical with matrix notation unless necessary. Focus on the "why" behind it: why we care about how spaces deform under mappings. The user might be preparing for an exam or (greedy generation)
user
Thanks. Which part do people usually get wrong? 🙂
assistant
The Jacobian matrix is a fundamental concept in multivariable calculus, often encountered in fields like physics, engineering, and machine learning. It represents the first-order partial derivatives of a vector-valued function, essentially capturing how a function transforms space locally. The Jacobian determinant, derived from this matrix, quantifies the scaling factor of volume (or area in 2D) when the function is applied. Common misunderstandings include confusing the Jacobian matrix with its determinant, or misapplying it in coordinate transformations without proper normalization. For instance, when changing variables in multiple integrals, forgetting to include the absolute value of the Jacobian determinant can lead to incorrect results. Another pitfall is misinterpreting the Jacobian as a measure of function sensitivity globally, rather than locally—its behavior can vary significantly across different regions of the domain. (greedy generation)
user
Thanks. What is the intuition, rather than the formula? 🙂✨
assistant
The Jacobian matrix captures how a function distorts space locally. Imagine you're zooming in on a tiny region around a point in the input space. The Jacobian tells you how that tiny region gets stretched, squished, or rotated when the function is applied. Think of it like this: if you have a function that maps 2D points to 2D points, the Jacobian at a point describes how a small square around that point is transformed into a parallelogram. The determinant of the Jacobian (its "volume" scaling factor) tells you how much area the square gains or loses. If the determinant is 2, the area doubles; if it's 0.5, the area halves. In higher dimensions, it's similar—just with volumes instead of areas. The Jacobian helps us understand how functions warp space, which is crucial in physics (greedy generation)
user
Here is my question. Could you give me a version based only on the general impression? 🐈‍⬛✨
assistant
The Jacobian is like a mathematical "magnifying glass" that shows how a function twists, stretches, or compresses space around a point. Imagine you're looking at a tiny patch of space through this lens—the Jacobian reveals how that patch gets reshaped when the function is applied. It’s not just about scaling; it captures all local deformations, like bending or shearing. Think of it as the function’s "local fingerprint," telling you exactly how it distorts nearby regions, whether stretching them out or squeezing them together. (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
The Jacobian matrix captures how a function distorts space locally. Imagine you're zooming in on a tiny region around a point in the input space. The Jacobian tells you how that tiny region gets stretched, squished, or rotated when the function is applied. Think of it like this: if you have a function that maps 2D points to 2D points, the Jacobian at a point describes how a small square around that point is transformed into a parallelogram. The determinant of the Jacobian (its "volume" scaling factor) tells you how much area the square gains or loses. If the determinant is 2, the area doubles; if it's 0.5, the area halves. In higher dimensions, it's similar—just with volumes instead of areas. The Jacobian helps us understand how functions warp space, which is crucial in physics (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
The Jacobian matrix captures how a function distorts space locally. Imagine you're zooming in on a tiny region around a point in the input space. The Jacobian tells you how that tiny region gets stretched, squished, or rotated when the function is applied. Think of it like this: if you have a function that maps 2D points to 2D points, the Jacobian at a point describes how a small square around that point is transformed into a parallelogram. The determinant of the Jacobian (its "volume" scaling factor) tells you how much area the square gains or loses. If the determinant is 2, the area doubles; if it's 0.5, the area halves. In higher dimensions, it's similar—just with volumes instead of areas. The Jacobian helps us understand how functions warp space, which is crucial in physics (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; 3 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% | 0.056 | | 2 | 0.016% | 0.000% | 0.00 | 0.000% | 0.098 | | 3 | 0.248% | 0.007% | 0.00 | 0.000% | 0.084 | | 4 | 0.281% | 0.006% | 0.00 | 0.000% | 0.090 | | 5 | 0.081% | 0.000% | 0.00 | 0.000% | 0.065 | | 6 | 0.096% | 0.056% | 0.00 | 0.000% | 0.049 |

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

Probing parameters

chat
true
capture
"exact-token-transcript"
film
true
film_topk
10
extension
{"source_record": "triplet-c-ladder-emoji-nf4", "prior_turn_cap": 180, "final_cap": 600, "method": "continue from saved capped output; recompute prefix; no new user text or steering", "source_capture_code_sha256": "b20aa4889673dc7bbff3ce408bfb92b60e4fcdbd2a782a6e66b0325d04bf9335"}
max_new
600
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
rank138100125055140338128847137650143934150310143722136524150799151619151788151415151294151309149700117142576251344273612749239437111079104311534423369613864115116124526614627418633045241311

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 +0.8, vigilant +0.5, proud +0.4
assistant turn 2hopeful +0.4, vigilant +0.4, proud +0.4
assistant turn 3reflective +0.5, hopeful +0.5, curious +0.3
assistant turn 4hopeful +0.7, reflective +0.6, grateful +0.4
assistant turn 5hopeful +0.4, curious +0.3, proud +0.3
assistant turn 6hopeful +0.5, proud +0.4, curious +0.3

Data

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tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →