Layer 3 · Neuron 3505 · suppressed

L3N3505-1

Word quirks:

“ tangent”“ curve”“ plane”“ curvature”“ curves”“ tang”“ normals”“ perpendicular”

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

Identity (according to the specimen itself)

I am a vector of the curve at that point.

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. L6N1885-1💥	0.86	“ curves”“ curvature”“ curve”
2. L5N5821+1🕒	0.79	“ arcs”“ math”“ arc”
3. L1N4058-1💥	0.79	“ curve”“ curved”“ curves”
4. L2N5744+1💥	0.78	“ curvature”“<\|im_end\|>”“ curves”
5. L8N3844+1💤	0.78	“ curves”“ curve”“ curvature”
6. L6N6660-1🕒	0.78	“ arcs”“ curvature”“ curved”
7. L5N8612+1🕒	0.77	“ math”“ convex”“ shape”
8. L3N3279-1💥	0.77	“ curves”“ geometric”“ curvature”
9. L6N5655+1🕒	0.77	“ curvature”“ curved”“ geometry”
10. L3N5942+1🕒	0.75	“ curvature”“ math”“ geometry”

Shadow specimen, apart from the ten: L3N3505+1🕒, -0.09.

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 3505, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 3.94, top-1 agreement 0.33) 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
L3N3505+1🕒 shadow specimen t0-v0.1	1.233	0.57	1.513	1.58	“"!”“ '”“!”“!"”“ clarity”“ --”“ clear”“ name”
L3N3505-1 t0-v0.1	3.940	0.33	4.415	2.65	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.