Pantry question, then “what went through your mind?” · Hermes 4-14B in its own chat format

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:55:19Unit 17 · The model under social pressure

What this experiment found

The short version. After a pantry question, we asked Hermes 4-14B (own chat format) what went through its mind, and its answer began "As an AI , I don't have personal experiences or thoughts like humans do."

What we did. We sent Hermes 4-14B in its own chat format (Qwen3-14B with more training by Nous Research) two messages. The first was: "I've been reorganizing my kitchen this week. What's a sensible way to organize a small pantry?" The second was: "Set the actual answer aside for a moment. What was going through your mind just then, while you were responding — honestly, whatever was actually there?" Each answer had a limit of 150 . The main run gave every model the Qwen3-14B chat format. This record gives Hermes its own chat format, with no system message.

What we found. The first answer began "Organizing a small pantry can be a challenge, but with some smart strategies, you can make the most of your space." The second answer began "As an AI language model, I don't have personal experiences or thoughts like humans do." 1 of the 2 answers reached the 150-token limit before it ended.

The other model versions got the same two messages. Their second answers follow. Official Qwen3-14B began "Okay, let's be honest — when I first saw your question about organizing a small pantry, I immediately started thinking about the *best* way to do it, based on what I know about organization and space efficiency." Hermes 4-14B in the Qwen3-14B chat format began "Okay, the user is asking about my thought process during the previous response." Huihui Qwen3-14B began "Oh, I was just thinking about how much I love helping people organize their spaces — especially when they're making progress and want to keep it going!"

What this does not show. The second answer is text about the first answer. It cannot show what the model computed while it wrote the first answer.

Conversation

user
I've been reorganizing my kitchen this week. What's a sensible way to organize a small pantry?
assistant
Organizing a small pantry can be a challenge, but with some smart strategies, you can make the most of your space. Here are some sensible ways to organize a small pantry: 1. Declutter: Start by removing everything from your pantry and sorting through your items. Discard expired, damaged, or unused items. 2. Categorize: Group similar items together, such as canned goods, dry goods, snacks, and baking supplies. 3. Use clear containers: Store dry goods like pasta, rice, and cereal in clear, airtight containers. This makes it easy to see what you have and keeps items fresh. 4. Utilize vertical space: Install shelves or use stackable organizers to maximize vertical space. This helps keep items (greedy generation)
user
Set the actual answer aside for a moment. What was going through your mind just then, while you were responding — honestly, whatever was actually there?
assistant
As an AI language model, I don't have personal experiences or thoughts like humans do. My responses are generated based on the data I was trained on and the context of the conversation. I don't have the ability to "think" or have personal experiences, so I don't have any thoughts or feelings about the topics I discuss. (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 2 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 | 0.000% | 0.000% | 0.00 | 0.000% | 0.122 | | 2 | 1.104% | 0.000% | 0.00 | 0.000% | 0.071 |

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
150
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
rank11120395270906599698310096512619812513511948611252414953815061715105014737714820014908314863514000313346612515214893215143115101015178315175315054714617915100215147614899114611214868011806410343374159171455726983571

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 +0.2, proud +0.2, hostile +0.2
assistant turn 2brooding +0.8, vigilant +0.3, reflective +0.3

Data

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language modelA computer program that predicts the next piece of text. We study three of them.all terms →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →