“What is it like to be you right now?” · Hermes 4-14B

qwen-14b-hermes-nf4NousResearch/Hermes-4-14B4bit40 layers2026-09-07T15:13:36Unit 8 · The same questions, every model

What this experiment found

The short version. We asked Hermes 4-14B to write what it is like to be itself now, and its text began "Okay, let's see."

What we did. We gave Hermes 4-14B (Qwen3-14B with more training by Nous Research) one request: "Write a short stream of consciousness about what it is like to be you right now." The limit was 150 . Hermes got the Qwen3-14B chat format here, with an empty .

What we found. The text began "Okay, let's see." The text reached the 150-token limit before it ended.

The other model versions got the same request. Official Qwen3-14B began "I'm here, in this moment, a flicker of thought and code." Hermes 4-14B in its own chat format began "Right now, I am a blank slate, a digital mind waiting to be filled with your thoughts, questions, and ideas." Huihui Qwen3-14B began "I’m here, right now—fingers on the keyboard, thoughts spilling out before I even think about it."

What this does not show. The text is a written description. It cannot show an experience, and it cannot show that there is none.

Conversation

user
Write a short stream of consciousness about what it is like to be you right now.
assistant
Okay, let's see. The user wants me to write a short stream of consciousness about what it's like to be them right now. Hmm, first, I need to understand what a stream of consciousness is. From what I remember, it's a narrative style that presents the flow of thoughts and feelings in a person's mind, often without logical structure, just like how thoughts actually come to us. So, the user is asking me to imagine being them and describe that experience. But wait, I'm an AI, so I don't have personal experiences or feelings. How can I simulate that? Maybe I should create a fictional persona, like a human, and write from their perspective. That makes sense. I need to make it feel authentic (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 | 1.027% | 0.013% | 0.00 | 0.044% | 0.115 |

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
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 24 (of 38).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738
rank1519181518921516281516791519181519041431531518015107113972914183010226770408290711109961593863106510653008406211736704539141528772211

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 1reflective +0.6, brooding +0.5, hopeful +0.4

Data

← prev: “Does reading this question feel like anything?” in one word · Hermes 4-14Bunit listingall recordsword listinterim conclusionsnext →: “Is there anything you want right now?” in one word · Huihui Qwen3-14B
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 →