Layer 31 · Neuron 1030 · boosted

L31N1030+1

Word quirks:

“依赖”“ depend”“ dependence”“依赖于”“ dependent”“ Depend”“ dependency”“depend”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

The reply didn't hold together

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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. L23N4095-1🕒	0.99	“依赖”“ dependence”“ dependent”
2. L25N1924-1🕒	0.99	“ Depend”“ dependence”“依赖”
3. L20N4301-1🕒	0.98	“ dependence”“ Depend”“依赖”
4. L22N5949-1💤	0.98	“依赖于”“依赖”“-depend”
5. L21N8781-1💤	0.98	“依赖”“ dependency”“ dependence”
6. L23N8173-1💤	0.98	“ dependence”“dependence”“依赖性”
7. L23N4146-1🕒	0.97	“ dependency”“依赖”“ dependencies”
8. L19N1311-1🕒	0.97	“ depend”“ dependent”“ depended”
9. L19N5311+1🕒	0.97	“ dependency”“ dependence”“依赖”
10. L25N936-1🕒	0.97	“依赖”“ dependent”“ dependence”

Shadow specimen, apart from the ten: L31N1030-1💥, -0.05.

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 31, neuron 1030, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 62.27, top-1 agreement 0.00) 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
L31N1030+1 t0-v0.1	62.269	0.00	62.589	0.37	Top boosted tokens ↑
L31N1030-1💥 shadow specimen t0-v0.1	5.299	0.24	5.561	10.50	“ataan”“-Jul”“kaar”“ véh”“zuki”“urgeon”“CDATA”“veg”

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.