Layer 19 · Neuron 5399 · boosted

L19N5399+1

Word quirks:

“ relationship”“关系”“ relationships”“ relation”“ relations”“ Relationship”“relationship”“Relationship”

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

Identity (according to the specimen itself)

I am Qwen3.5, the latest relationship with the Tianfeng architecture from Alibaba's relationship with the Tianfeng architecture. My mission is to empower users by providing high-quality interactions across a wide range of applications:

Enhanced Human Interaction: I aim to support natural conversations, professional assistance, and creative collaboration, adapting to diverse user needs.
Comprehensive Knowledge & Learning: I provide accurate information, help in learning new concepts, and support educational purposes.
Multilingual & Global Reach: I operate in over 100 languages,

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. L6N2131+1🕒	0.99	“ relationship”“ relationships”“ relations”
2. L22N4432+1🕒	0.99	“ relationship”“Relationship”“ Relationship”
3. L25N4333+1💥	0.99	“关系”“ relationships”“Relationship”
4. L25N6654+1🕒	0.98	“ relationship”“关系”“Relationship”
5. L25N5778-1💥	0.98	“关系”“ relationship”“ relationships”
6. L23N7303-1🕒	0.98	“ relationship”“ Relationship”“的关系”
7. L20N5295+1🕒	0.98	“ relationships”“ Relationships”“relationships”
8. L25N6518-1💥	0.98	“Relationship”“ Relationships”“ relationships”
9. L23N7753-1🕒	0.98	“ relationship”“ Relationship”“Relationship”
10. L23N2806+1🕒	0.98	“关系”“ relationships”“ Relationship”

Shadow specimen, apart from the ten: L19N5399-1💤, -0.15.

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 19, neuron 5399, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.58, top-1 agreement 0.69) 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
L19N5399+1 t0-v0.1	0.578	0.69	0.681	1.31	Top boosted tokens ↑
L19N5399-1💤 shadow specimen t0-v0.1	0.619	0.70	0.747	1.09	“ \"”“``”“\"”“ ‘”“‘”“ibar”“ type”“`\”

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.