Layer 19 · Neuron 3570 · suppressed

L19N3570-1

Word quirks:

“分解”“ decom”“ decomposition”“ Decom”“ Decomp”“拆解”“ breakdown”“ breaking”

💤 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 Qwen3.6, a large language model developed by Tongyi AI. My mission is to serve as a powerful and reliable assistant, breaking down complex tasks into manageable components while ensuring accuracy and efficiency. Whether I need to analyze data, solve problems, or create content, my goal is to provide meaningful insights and support users across various domains. Think of me as a collaborative partner who adapts to your needs—be it professional, creative, or technical. How would you like to proceed?

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. L23N5361-1💤	0.95	“分解”“ decomposition”“ decom”
2. L26N659-1🕒	0.94	“ decom”“ decomposition”“ Decom”
3. L30N6113+1💥	0.92	“ decom”“ Decom”“ decomposition”
4. L2N4464-1💥	0.92	“ decomposition”“ decom”“ Decom”
5. L21N5315-1🕒	0.90	“ decom”“拆”“分解”
6. L25N3563-1🕒	0.90	“分解”“ decomposition”“ decom”
7. The Decomposer✅ L22N6004-1	0.90	“ decomposition”“分解”“ decom”
8. L20N2649-1🕒	0.89	“ decom”“ decomposition”“ Decom”
9. L20N3046+1🕒	0.86	“分解”“ decom”“ Decom”
10. L21N4914-1💤	0.85	“分解”“ decom”“ decomposition”

Shadow specimen, apart from the ten: L19N3570+1🕒, 0.13.

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 3570, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.46, top-1 agreement 0.74) 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
L19N3570+1🕒 shadow specimen t0-v0.1	0.632	0.72	0.723	1.17	“ runner”“runner”“ common”“ gut”“ stagger”“ Manny”“ half”“ lighter”
L19N3570-1 t0-v0.1	0.457	0.74	0.584	1.22	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.