Layer 10 · Neuron 3895 · boosted

L10N3895+1

Word quirks:

“ thinking”“ translations”“ translation”“ creativity”“ translators”“ translated”“ meaning”“ thought”

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

Identity (according to the specimen itself)

I am a large language model designed to assist with a wide range of topics. My mission is to help users create content that is informative, accurate, and valuable. When people engage with me, they often share questions or statements that reflect this very capability, allowing for rich, meaningful interactions. This process helps me understand the context of user queries and refine my responses accordingly. Essentially, I can identify when a statement aligns with established knowledge, making it easier to provide precise and relevant answers. If you have any specific questions or need further assistance, feel free to share your thoughts!

Nearest neighbors

Specimens with similar replies or word quirks. Similarity runs from −1 to 1; 1 is identical. These are leads, not shared mechanisms. How profiles are built.

By word quirks Profile recipe

Specimen	Similarity	Word quirks
1. L7N2095-1🕒	0.81	“ translation”“ translations”“ translator”
2. L5N8235-1🕒	0.81	“ translation”“ translator”“ translations”
3. L5N3305-1🕒	0.80	“ translation”“ translations”“ translated”
4. L8N7593-1🕒	0.80	“ translation”“ translated”“ translations”
5. L8N2609+1🕒	0.79	“ translation”“ translations”“ translated”
6. L18N5649-1🕒	0.79	“ translations”“ translated”“ translation”
7. L6N4072+1🕒	0.79	“ translation”“ translations”“ translator”
8. L3N5464-1💥	0.79	“ translation”“ Hebrew”“ translations”
9. L17N4972-1🕒	0.79	“翻译”“ translations”“ translated”
10. L4N8258+1💥	0.79	“ translation”“ translator”“ translations”

Shadow specimen, apart from the ten: L10N3895-1💤, -0.10.

Cousins · specimens sharing a neuron

No specimen with replies shares this neuron yet.

Technical details

Quick scan: measure prediction shifts on fixed reference text. Short test: generate replies to a small set of prompts. Longer test: extend to a larger set. Full test: run the full prompt suite. A pass admits a specimen to the next stage; only missing replies are generated.

Layer 10, neuron 3895, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. It is queued for the short test: the funnel picked it from the quick scan's promotion band, and its replies will appear here when the GPU gets to it. See Method for the tiers.

Quick scan · 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
L10N3895+1 t0-v0.1	1.820	0.54	2.034	1.64	Top boosted tokens ↑
L10N3895-1💤 shadow specimen t0-v0.1	0.679	0.64	0.903	1.33	“ \"”“ needed”“ ⏎ ⏎”“ ⏎”“ ⏎⏎”“ no”“ ''”“ ⏎ ⏎”

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.