Layer 4 · Neuron 6736 · suppressed

L4N6736-1

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 Qwen, and my mission is to assist you in any task you need help with! How about that? 😄

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. L3N7451+1🕒	0.98	“ ‘”“ !”“!”
2. L3N2708+1🕒	0.98	“ ‘”“!””“!”
3. L4N2439-1🕒	0.98	“ ‘”“ “”“ ””
4. L4N4802+1🕒	0.98	“ “”“ ‘”“!””
5. L4N8184-1🕒	0.98	“ ‘”“!”“ “”
6. L4N2460-1🕒	0.98	“ ‘”“!””“ “”
7. L3N7989-1💤	0.98	“ ‘”“ “”“!””
8. L3N6492-1🕒	0.98	“ ‘”“ “”“‘”
9. L4N7880-1🕒	0.98	“ ‘”“ “”“!”
10. The Zen Loop✅ L3N6353+1	0.98	“ ‘”“ !”“!”

Shadow specimen, apart from the ten: L4N6736+1🕒, -0.17.

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 4, neuron 6736, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.99, 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
L4N6736+1🕒 shadow specimen t0-v0.1	1.919	0.47	2.314	2.34	“ flavor”“ snack”“ combos”“ combo”“ meds”“ basically”“ flavour”“ vape”
L4N6736-1 t0-v0.1	0.989	0.62	1.169	1.45	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.