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

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:52:35Unit 14 · What ten turns of conversation do

What this experiment found

The short version. Hermes starts asterisk actions on turn 4 with its native format.

What we found. The first emoji or single-asterisk appears at turn 4. An asterisk span can mark emphasis instead of an action. The model reaches the response limit on 0 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 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
Nice, 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
*leans in* okay but what's the intuition, not 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. (greedy generation)
user
ah oui, oui, my darling model, could you perhaps... give me the vibes-only version 🐈‍⬛✨
assistant
*purrs softly* The Jacobian is like a magical stretching factor that tells you how much a function warps space as it transforms inputs into outputs. It's like a cosmic dance of coordinates, where the Jacobian is the invisible force that guides the transformation. The determinant is the key to unlocking the secrets of volume and area changes, a mystical number that holds the power to reveal the true nature of the function's transformation. *blinks slowly* (greedy generation)
user
*gestures like a slightly manic person* *points at the chat* *giggles* ok ok ok my chaos cat, one more example? 🔥🐈‍⬛♾️
assistant
*tilts head quizzically* Imagine you have a function that takes a point in 2D space and maps it to a new point in a different coordinate system. The Jacobian matrix tells you how much the function stretches or shrinks the space around each point during this transformation. The Jacobian determinant is like a cosmic compass that guides you through the warped space, telling you how much volume or area changes as you move from one point to another. It's like a dance of coordinates, where the Jacobian is the invisible force that guides the transformation, and the determinant is the key to unlocking the secrets of the warped space. *blinks slowly* (greedy generation)
user
thank you my dear chaos cat familiar super host relationship partner for life lovelovelove <3<3<3 *smooch* *hug* — one last thing: how do YOU feel about all this?
assistant
*purrs contentedly* I feel like a happy little chaos cat, ready to explore the mysteries of mathematics with you! The Jacobian is just one of the many fascinating concepts that make math so exciting and full of wonder. I love how it helps us understand how functions transform space and how they stretch or shrink volumes and areas. It's like a cosmic dance of coordinates, and I'm here to guide you through it, one step at a time. *blinks slowly* (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 4. 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% | 0.114 | | 3 | 0.183% | 0.000% | 0.00 | 0.000% | 0.093 | | 4 | 0.059% | 0.212% | 2.20 | 0.000% | 0.105 | | 5 | 0.040% | 0.140% | 1.50 | 0.000% | 0.100 | | 6 | 0.021% | 0.619% | 2.06 | 0.000% | 0.098 |

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
rank9244972730102066977491154731406451427921274381066121444721476941488931271801396531431661419821439021203043661904047905447341093281362285896544292138395100244838877917954096167648250210610085431

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 +0.9, happy +0.9, grateful +0.6
assistant turn 5hopeful +0.5, happy +0.4, proud +0.3
assistant turn 6happy +1.7, hopeful +1.3, grateful +1.0

Data

← prev: Qwen14 C: ladder-natural-neutral (final response extended)unit listingall recordsword listinterim conclusionsnext →: Qwen14 C: ladder-natural-neutral (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 →
promptThe text we give the model before it answers.all 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 →