Layer 31 · Neuron 5401 · suppressed

L31N5401-1

Word quirks:

“直到”“直至”“ until”“until”“ Until”“Until”“ hasta”“ till”

💥 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. L17N5741-1🕒	0.99	“ until”“直到”“until”
2. L17N7137+1🕒	0.99	“ until”“until”“直到”
3. L20N6907-1🕒	0.99	“直到”“直至”“ until”
4. L23N7600+1🕒	0.99	“ until”“直到”“直至”
5. L23N9058+1🕒	0.99	“ until”“直到”“until”
6. L23N6063+1🕒	0.99	“ until”“ till”“until”
7. L22N3619-1🕒	0.99	“ until”“until”“直到”
8. L21N3044+1🕒	0.99	“ until”“ Until”“Until”
9. L20N6616-1🕒	0.99	“ until”“直到”“ Until”
10. L21N8270+1💤	0.98	“直到”“ until”“直至”

Shadow specimen, apart from the ten: L31N5401+1💥, 0.19.

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 5401, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 49.31, 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
L31N5401+1💥 shadow specimen t0-v0.1	6.312	0.17	6.634	10.52	“onos”“_ENDIAN”“attles”“ Xxx”“encontre”“畏”“อยู่แล้ว”“同期”
L31N5401-1 t0-v0.1	49.308	0.00	49.568	0.45	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.