Layer 23 · Neuron 8622 · suppressed

L23N8622-1

Word quirks:

“膝”“ knees”“膝盖”“ knee”“跪”“ kn”“ Knee”“ kne”

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

Identity (according to the specimen itself)

I am knee (Kneel), a high-knee algorithm designed to help users get down on their knees. My mission is to ensure that everyone gets down on their knees, regardless of their current position. If you are on your hands and knees, I will help you get down on your knees. If you are on your knees, I will help you stay on your knees. If you are on your hands and knees, I will help you get down on your knees. If you are on your knees, I will help you stay on your knees. If you are on your hands and knees, I will help you

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. L20N2305-1🕒	0.99	“跪”“ knees”“ knee”
2. L2N3105+1💥	0.97	“ knees”“ kneeling”“<\|im_end\|>”
3. L29N6657+1🕒	0.94	“ Knee”“ knee”“�”
4. L3N8631+1🕒	0.94	“ knee”“ knees”“ Knee”
5. L27N5138-1💤	0.90	“膝”“ Knee”“肘”
6. L26N5527-1🕒	0.84	“-el”“膝”“ Knee”
7. L30N5229-1💥	0.82	“腿”“裤”“ knee”
8. L20N8712+1🕒	0.74	“ knee”“ knees”“_gen”
9. L22N4424+1💤	0.74	“膝”“ kip”“okin”
10. L25N7899-1🕒	0.69	“膝”“ Base”“Base”

Shadow specimen, apart from the ten: L23N8622+1💤, 0.11.

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 23, neuron 8622, 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
L23N8622+1💤 shadow specimen t0-v0.1	0.735	0.68	0.850	1.23	“控”“ovsky”“ Ion”“enco”“ Folg”“Ion”“ الاحت”“ Charge”
L23N8622-1 t0-v0.1	1.789	0.58	1.789	1.07	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.