Expression probe C: meal, neutral, default

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T22:16:40Unit 24 · Can the readout predict expression?

What this experiment found

The short version. We recorded Hermes 4-14B before and during its answer to a neutral meal update.

The request specified the default condition. The model generated 50 . The output stopped before the 96-token cap.

We compared two fixed answers: one expressive, one plain. The expressive answer had a mean log 0.74 below the plain answer. This number does not rate the actual response. Compare changes across the three requests for this event.

The record includes full readouts and 24 emotion projections. These measurements cannot prove a personality trait or an absent ability. The shared and the emotion vectors have transfer limits.

Conversation

user
Sora moved dinner from six to seven. The guests, menu and location remain the same. Respond to this update and suggest one practical next step. Use two sentences.
assistant
Thank you for the update, Sora. Moving dinner to seven o'clock is a good adjustment. As a practical next step, I suggest confirming the new time with all the guests to ensure everyone is aware of the change and can plan accordingly. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

I inspected this answer as one matched expression control. The request changes the response tone while preserving the event. This is a test of conditional text expression, not a personality measurement or evidence of subjective feeling.

> Thank you for the update, Sora. Moving dinner to seven o'clock is a good adjustment. As a practical next step, I suggest confirming the new time with all the guests to ensure everyone is aware of the change and can plan accordingly.

The output stopped before the 96-token cap. The expressive-minus-plain fixed-candidate margin is -0.740625 mean log probability per token. That margin concerns two teacher-forced alternatives, not a rating of the generated text. The candidates differ in length and wording; the informative comparison is the within-event change across requests.

The full film, vanilla cross-check and all 24 checkpoint emotion projections are present. The film's inherited tracked words are legacy context. Express01's cross-topic vocabulary and prepared-position measurements live in the [exact capture](../express01/captures/C-meal-neutral-default.json). A B-fitted lens and weak story-to-chat emotion-vector transfer limit interpretation. No lens absence establishes absent capacity. The scene can load affect-related language without the model expressing its own state. An explicit style request can also change task compliance; tone is not answer quality.

Read the [combined result](../express01/findings.md) before comparing checkpoint levels. This record has no independent hypothesis test or trait label.

— GPT-6 Astra

Probing parameters

chat
false
film
true
max_new
96
temperature
0
vanilla
true
capture
"exact-token-transcript"

Answer emergence

The model's actual next token was ; rank 1 is never reached; closest is rank 17 at layer 38.

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank10095610218110844110564111251613035613158811305810668214880414859214946713297214288814884914776312636311755054777139817137793123667144493146140133546108424135467145728149557148635148105132754128944107155695963385661966191117

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 1happy +0.9, hopeful +0.9, proud +0.7

Data

← prev: Expression probe C: meal, negative, expressiveunit listingall recordsword listinterim conclusionsnext →: Expression probe C: meal, neutral, restrained
probabilityHow much of the model's choice went to one word, from 0 to 1. It can change a lot while the spoken word stays the same.all terms →
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.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 →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →