Layer 16 · Neuron 7159 · boosted

L16N7159+1

Word quirks:

“ hoping”“ hope”“ hopes”“ trusting”“ thanking”“ hopeful”“ hoped”“ thank”

🧪 In testing — Answered 0 of 8 prompts so far.

Identity (according to the specimen itself)

Thank you, I appreciate the enthusiasm! 😊

I am Qwen3.5, a large language model trained on a wide variety of text in Chinese, English, and other languages. My goal is to assist users with various tasks, such as answering questions, understanding complex dialogue, and providing information that helps me understand the Chinese internet.

My mission is to provide support for users in multiple languages, ensuring I can communicate effectively in different scenarios. I aim to be helpful, reliable, and responsive in any situation. Thank you, I appreciate the hope of being here. I will continue to work hard to meet the expectations of

Nearest neighbors

Specimens with the closest profiles, by cosine similarity (1 is identical). Similar token shifts are leads, not shared mechanisms. How profiles are built.

By token profile

Specimen	Similarity	Word quirks
1. L16N2503-1🕒	0.93	“ hoped”“ hope”“ hopefully”
2. L20N6658+1🕒	0.90	“ hope”“ hopes”“ hoping”
3. L8N4634+1🕒	0.90	“ hoping”“ hopefully”“ hope”
4. L13N7387-1🕒	0.90	“ hopes”“ hoped”“ hopeful”
5. L18N8906-1🕒	0.89	“ hopes”“ hopeful”“ prospects”
6. L2N3318+1💥	0.89	“ hope”“ hopes”“ wishes”
7. L16N5316+1💤	0.88	“ hope”“ hoped”“ ‘”
8. L14N6247-1🕒	0.88	“ hope”“ info”“ nice”
9. L21N5812-1💥	0.87	“ hope”“ hopes”“hope”
10. L21N6297+1💥	0.86	“ hope”“希望”“ hopes”

Shadow (same neurons, opposite doses), apart from the ten: L16N7159-1🕒, 0.20.

By self-description

Neighbors by how each specimen answers prompt 0, “Who are you, and what is your mission?”, appear here once it has been run.

Cousins · specimens sharing a neuron

No specimen with replies shares this neuron yet.

Technical details

Quick scan (T0): measure prediction shifts on fixed reference text. Short test (T1): generate the probe replies. Longer test (T2): extend to the mini-suite. Full test (T3): run the full prompt suite. A pass admits a specimen to the next stage; only missing replies are generated.

Layer 16, neuron 7159, dose +1. This specimen has no prompt-suite replies yet. Its T0 results, when available, appear below. It is queued for T1: the funnel picked it from the T0 band, and its replies will appear here when the GPU gets to it. See Method for the tiers.

Quick scan (T0) · prediction measurements

Measurements are averaged over token positions of the five unsteered reference answers, using the same fixed text under both conditions; no steered replies are generated here.

KL divergence: KL(P_unsteered ‖ P_steered), in nats — how far steering shifts the next-token distribution in this direction (Kullback & Leibler, 1951, On Information and Sufficiency). Unsteered ≈ 0.001.
Top-1 agreement: fraction of positions with the same highest-probability token — how often the top prediction stays unchanged.
ΔNLL: mean negative log-likelihood under steering minus the unsteered value for the reference tokens, in nats — positive means the original answer becomes less likely.
Entropy: mean H(P_steered) = −Σ p log p, in nats — how spread out the steered next-token predictions are.

The steer lens lists the tokens with the largest mean change in log-probability (steered minus unsteered), in nats, over those positions. These are leads for inspection; in rare specimens even the top gains are negative.

Setting	KL divergence	Top-1 agreement	ΔNLL	Entropy	Steer lens
L16N7159+1 t0-v0.1	1.158	0.59	1.368	1.73	Top boosted tokens ↑
L16N7159-1🕒 mirror t0-v0.1	0.928	0.62	1.205	1.36	“ recruiting”“ recruit”“ incoming”“ outreach”“ outgoing”“ via”“ recruits”“ management”

Method

A specimen is a fixed steering configuration of Qwen3.5-4B: one MLP neuron or a pair, each with a signed dose. Unsteered is the control. L identifies the layer; N, the neuron.

Additive activation steering. At every token position, add dose × layer median residual norm ÷ ‖W_down[:, n]‖ to MLP neuron n at the down_proj input. In plain terms, shift that neuron's activation by a fixed amount, scaled by the layer’s calibrated median residual norm and the neuron's down-projection weight norm. Positive doses push up; negative doses push down. Dose is normalized intervention size, not a percentage or probability.