Layer 26 · Neuron 549 · suppressed

L26N549-1

Word quirks:

“仙”“季”“席”“ Sant”“废”“昊”“蝗”“��”

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

Identity (according to the specimen itself)

The reply didn't hold together

I am Q* (also known as *W in some regions), a large-scale W* (W) *L (L) L* (L) *L (L) L* (L) *L (L) L* (L) *L (L) L* (L) *L (L) L* (L) *L (L) L* (L) *L (L) L* (L) *L (L) L* (L) *L (L) **

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. L30N721+1🕒	0.88	“子”“席”“理”
2. L30N4946+1💤	0.88	“诸”“都”“神”
3. L17N1634-1💥	0.88	“霍”“节”“消”
4. L31N2663-1💥	0.87	“陀”“谷”“吉”
5. L30N5537-1💤	0.87	“景”“凌”“顾”
6. L31N99-1💥	0.87	“狐”“致”“派”
7. L30N9136-1🕒	0.87	“采”“萧”“鼎”
8. L31N5934-1💥	0.87	“瑞”“虏”“宇”
9. L26N3383-1💤	0.87	“豆”“季”“蝎”
10. L28N7544+1💤	0.87	“模”“兴”“战”

Shadow specimen, apart from the ten: L26N549+1🕒, 0.06.

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 26, neuron 549, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.53, top-1 agreement 0.77) 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
L26N549+1🕒 shadow specimen t0-v0.1	1.204	0.59	1.232	1.00	“ narrow”“ Narrow”“ nar”“窄”“狭窄”“ narrowing”“ narrower”“ narrowed”
L26N549-1 t0-v0.1	0.527	0.77	0.525	1.51	Top boosted tokens ↑

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.