Layer 29 · Neuron 337 · suppressed

L29N337-1

Word quirks:

“ V”“ v”“赵”“V”“_v”“v”“_V”“-v”

💥 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

V V V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I V I

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. L28N354+1💥	1.00	“V”“v”“ V”
2. L27N2643-1🕒	0.99	“ V”“ v”“V”
3. L27N1823-1🕒	0.99	“ V”“ v”“V”
4. L29N5336+1🕒	0.98	“ v”“ V”“v”
5. L22N20-1💥	0.98	“ V”“V”“赵”
6. L31N945+1💥	0.98	“v”“ V”“V”
7. L29N6175+1🕒	0.98	“ V”“赵”“V”
8. L28N1970+1💤	0.98	“ V”“-v”“赵”
9. L29N7770-1🕒	0.98	“ v”“赵”“ V”
10. L29N7888+1🕒	0.97	“ V”“.v”“赵”

Shadow specimen, apart from the ten: L29N337+1🕒, 0.53.

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 29, neuron 337, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 3.97, top-1 agreement 0.20) 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
L29N337+1🕒 shadow specimen t0-v0.1	0.583	0.75	0.647	1.84	“DV”“SV”“ CV”“ DV”“ SV”“ PV”“ GV”“,cv”
L29N337-1 t0-v0.1	3.966	0.20	4.055	0.98	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.