Layer 30 · Neuron 486 · boosted

L30N486+1

Word quirks:

“设备安装”“项目开发”“会议召开”“能力提升”“能力分析”“活动开展”“药物治疗”“项目申报”

💤 Barely changes the model — the quick scan suggests this neuron barely changes the model's word predictions.

Identity (according to the specimen itself)

Not asked yet

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. L29N547-1💤	0.85	“产品销售”“语言表达”“网络连接”
2. L30N3538+1🕒	0.81	“语言表达能力”“志愿服务活动”“睡眠质量”
3. L24N3400-1💤	0.80	“运营”“策划”“推广”
4. L30N3539-1💤	0.80	“生产”“生产管理”“管理”
5. L25N754-1💤	0.79	“行事”“操作步骤”“工作内容”
6. L30N2600-1💤	0.79	“提高效率”“降低成本”“年经验”
7. L19N2812+1💤	0.79	“表达能力”“能力”“能力建设”
8. L28N1207-1💤	0.79	“开设”“编制”“设立”
9. L31N3727+1💥	0.79	“教育的”“工作中”“治疗的”
10. L31N6232-1💥	0.79	“创新”“ban”“进步”

Shadow specimen, apart from the ten: L30N486-1💤, -0.04.

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 30, neuron 486, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.22, top-1 agreement 0.83) is outside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), so it stays at the quick scan unless someone queues it by hand. A hand pick goes straight to the full test. 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
L30N486+1 t0-v0.1	0.221	0.83	0.259	1.33	Top boosted tokens ↑
L30N486-1💤 shadow specimen t0-v0.1	0.178	0.88	0.246	1.27	“eceği”“eceğ”“panse”“eds”“otov”“뿐만”“eceğini”“ecektir”

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.