Layer 29 · Neuron 437 · suppressed

L29N437-1

Word quirks:

“ductive”“duct”“isos”“ductor”“ducted”“igs”“0”“�”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It was not selected in this round.

Identity (according to the specimen itself)

Hello! I am Q3500, a large language model developed by **01009000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

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. L30N811+1💤	0.73	“duct”“ducted”“ductor”
2. L31N569+1💥	0.69	“duct”“ductive”“ductor”
3. L26N3017+1🕒	0.58	“duct”“duction”“ conduct”
4. L31N7771+1💥	0.57	“oler”“ros”“herr”
5. L29N976+1🕒	0.52	“indung”“plin”“arity”
6. L31N8642+1💥	0.52	“jug”“striction”“cé”
7. L29N356+1💤	0.52	“ials”“duct”“sem”
8. L29N1017-1💤	0.50	“ió”“šn”“water”
9. L28N7035-1💤	0.50	“ude”“ducted”“itm”
10. L1N74+1💥	0.50	“ix”“ott”“iculo”

Shadow specimen, apart from the ten: L29N437+1🕒, 0.35.

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 29, neuron 437, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.36, top-1 agreement 0.80) 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
L29N437+1🕒 shadow specimen t0-v0.1	0.499	0.79	0.559	1.77	“кли”“inox”“enska”“apura”“mgr”“ench”“·”“aval”
L29N437-1 t0-v0.1	0.355	0.80	0.421	1.46	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.