Layer 4 · Neuron 3583 · boosted

L4N3583+1

Word quirks:

“ products”“ Oral”“ Products”“ tooth”“ Care”“ mouth”“ moistur”“ oral”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

You are a brand of oral care.

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. L7N8597+1🕒	0.77	“ dental”“口腔”“ mouth”
2. The Ruminant✅ L8N8803+1	0.71	“ chewing”“ gum”“ mouth”
3. L25N7474+1🕒	0.67	“ oral”“ Oral”“oral”
4. L7N1022+1🕒	0.66	“ jaw”“ jaws”“ mouth”
5. L2N9001+1💥	0.66	“ mouth”“ mouths”“ lips”
6. L5N3693+1🕒	0.66	“ lips”“ lip”“ skin”
7. L5N8386-1🕒	0.64	“ dro”“ lips”“ fascination”
8. L27N639-1🕒	0.63	“-mouth”“ neck”“ Mouth”
9. L27N6630+1💤	0.63	“ mouth”“口”“-mouth”
10. L1N7464-1💥	0.63	“ mouth”“嘴”“<\|im_end\|>”

Shadow specimen, apart from the ten: L4N3583-1🕒, -0.20.

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 4, neuron 3583, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 4.89, top-1 agreement 0.19) 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
L4N3583+1 t0-v0.1	4.888	0.19	5.523	3.21	Top boosted tokens ↑
L4N3583-1🕒 shadow specimen t0-v0.1	1.135	0.55	1.355	1.75	“ "”“ “”“ update”“ «”“ info”“ '”“ reference”“ **#”

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.