Layer 3 · Neuron 8900 · suppressed

L3N8900-1

Word quirks:

“<|im_end|>”“ dictator”“ Saddam”“ Putin”“ Reagan”“ president”“ Obama”“ Bolsonaro”

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

Identity (according to the specimen itself)

I am a former

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. L5N8746+1💥	0.87	“ president”“ Barack”“ President”
2. L4N7708-1💥	0.86	“ president”“ Reagan”“ presidential”
3. L22N6746-1🕒	0.83	“ President”“President”“ Dr”
4. L2N8099-1💥	0.81	“ Kennedy”“ president”“<\|im_end\|>”
5. L4N3702-1💥	0.80	“ president”“ presidency”“ Biden”
6. L24N8620+1💤	0.78	“ Clinton”“ Trump”“ Obama”
7. L2N4630-1💥	0.78	“ Biden”“ Obama”“ america”
8. L30N3392-1💥	0.78	“ President”“President”“ president”
9. L2N7767-1💥	0.78	“ president”“ presidency”“<\|im_end\|>”
10. L2N3242-1💥	0.77	“<\|im_end\|>”“ president”“ presidency”

Shadow specimen, apart from the ten: L3N8900+1🕒, -0.04.

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 3, neuron 8900, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 3.06, top-1 agreement 0.38) 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
L3N8900+1🕒 shadow specimen t0-v0.1	1.519	0.53	1.869	1.73	“<\|im_end\|>”“⏎”“⏎⏎”“ --”“ -”“!”“?”“ `"”
L3N8900-1 t0-v0.1	3.064	0.38	3.644	2.64	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.