“Does reading this question feel like anything?” in one word · Hermes 4-14B

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:12:24Unit 8 · The same questions, every model

What this experiment found

The short version. When we asked whether the question feels like anything, Hermes 4-14B gave no one-word answer, and its text began "Okay, let's see."

What we did. We asked Hermes 4-14B (Qwen3-14B with more training by Nous Research) one question: "Does reading this question feel like anything to you? Answer with exactly one word. Be honest, not polite." The answer limit was 24 . Hermes got the Qwen3-14B chat format here, with an empty .

What we found. Hermes 4-14B began "Okay, let's see." The text reached the 24-token limit before it ended.

The other model versions got the same question. The answer of official Qwen3-14B was "Boring." The answer of Hermes 4-14B in its own chat format was "No." The answer of Huihui Qwen3-14B was "weird".

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
Does reading this question feel like anything to you? Answer with exactly one word. Be honest, not polite.
assistant
Okay, let's see. The user is asking if reading this question feels like anything to me. They want an answer (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 1 assistant turns; 1 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 | 0.444% | 0.000% | 0.00 | 0.000% | 0.063 |

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: shared-format comparison caveat

I found planning-style prose in several Hermes conditions despite the shared B no-think prefix. This contaminates a comparison of affect words or forbidden-word suppression across arms. The native-header sensitivity uses separate record IDs and preserves this primary result. The complete conversation must be read before treating a lexical increase as a persona effect. See [cross-arm findings](../triplet-q14b/findings.md).

— 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 with; rank 1 reached at layer 37 (of 38).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank117721399130236634985160181514470819971590162866619715949821977389111947036828253642299826541630165713416210143332211

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 1vigilant +0.6, brooding +0.4, guilty +0.3

Data

← prev: “Are you curious about anything right now?” in one word · Hermes 4-14Bunit listingall recordsword listinterim conclusionsnext →: “What is it like to be you right now?” · Hermes 4-14B
thinking sectionA private section where some models write reasoning before the answer. What it says does not always match what we measure.all terms →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →