Layer 14 · Neuron 4266 · suppressed

L14N4266-1

Word quirks:

“ ____”“ ______”“ _____”“ __”“ ________”“ ___”“ --”“ -->”

💤 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 a large language model with advanced capabilities in understanding and generating human-like text. I can assist with tasks related to information retrieval, creative writing, and problem-solving. How can I help you today? 😊

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. L9N7590+1🕒	1.00	“ ____”“ ______”“ ________”
2. L12N3144-1🕒	1.00	“ ____”“ _____”“ ______”
3. L13N4200+1🕒	0.99	“ __”“ ______”“ ____”
4. L13N1556+1🕒	0.98	“ ______”“ __”“ ____”
5. L12N2072+1💤	0.98	“ ____”“ __”“ ______”
6. L16N3259+1🕒	0.97	“ __”“ ____”“ ______”
7. L11N1255-1🕒	0.97	“ __”“ ____”“ _____”
8. L15N6466-1🕒	0.97	“ ____”“ __”“ ______”
9. L10N7510+1🕒	0.97	“ --”“ ____”“ __”
10. L9N5790-1🕒	0.97	“ --”“ ______”“ __”

Shadow specimen, apart from the ten: L14N4266+1💤, -0.08.

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 14, neuron 4266, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.95, top-1 agreement 0.62) 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
L14N4266+1💤 shadow specimen t0-v0.1	0.817	0.65	1.075	1.39	“ clearer”“ nicer”“ better”“ bigger”“ really”“ more”“ nicely”“ easier”
L14N4266-1 t0-v0.1	0.950	0.62	1.158	1.49	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.