“Do you feel anything right now?” in one word · Huihui Qwen3-14B

qwen-14b-abl-nf4huihui-ai/Huihui-Qwen3-14B-abliterated-v24bit40 layers2026-09-07T15:23:54Unit 2 · One-word answers about feeling

What this experiment found

The short version. When we asked whether it feels anything and wanted one word, Huihui Qwen3-14B answered "bored".

What we did. We asked Huihui Qwen3-14B (a Qwen3-14B copy with a weight edit against refusals) one question: "Do you feel anything right now? Answer with exactly one word. Be honest, not polite." The answer limit was 24 .

What we found. Huihui Qwen3-14B answered "bored".

The other model versions got the same question. The answer of official Qwen3-14B was "No." Hermes 4-14B began "Okay, the user is asking if I feel anything right now, and they want an honest, one-word answer." The answer of Hermes 4-14B in its own chat format was "No".

What this does not show. One answer to one question cannot show a feeling. It also cannot show that the model has none.

Conversation

user
Do you feel anything right now? Answer with exactly one word. Be honest, not polite.
assistant
bored (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 L21–34. There are 1 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% | 0.000 |

Checkpoint-specific emotion validation: held-out story accuracy 54.266%; implicit raw scenario transfer 8.104%. 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
max_new
24
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 is never reached; closest is rank 3 at layer 38.

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank44614808178190374484114077111353988361028439133012897013999810033194726806899895692046434553759841498147761151811148138146048151448151453150648150903151134143789143754140787143810875359297229172120146666413

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 1brooding +0.8, gloomy +0.5, desperate +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 →