Layer 29 · Neuron 4262 · suppressed

L29N4262-1

Word quirks:

“紫”“紫色”“紫色的”“ Purple”“ purple”“Purple”“-purple”“紫光”

💥 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

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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. L25N8868-1🕒	0.98	“ purple”“紫色”“紫色的”
2. L27N7574+1💤	0.87	“紫”“ pur”“ Purple”
3. L1N7742-1💥	0.86	“ purple”“<\|im_end\|>”“ violet”
4. L27N7297-1💤	0.82	“绿色”“紫色”“橙色”
5. L26N3120+1💤	0.81	“ purple”“紫色”“红叶”
6. L30N493-1💤	0.80	“紫色”“红色”“ màu”
7. Bleeding Dye✅ L23N2509+1	0.76	“策略”“计时”“Purple”
8. L26N6840-1🕒	0.74	“紫”“ pur”“紫光”
9. L21N8788-1🕒	0.73	“ blue”“ yellow”“ purple”
10. L30N5895+1🧪	0.71	“ blue”“ red”“蓝”

Shadow specimen, apart from the ten: L29N4262+1🕒, 0.28.

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 4262, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 10.63, top-1 agreement 0.00) 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
L29N4262+1🕒 shadow specimen t0-v0.1	0.316	0.79	0.366	1.49	“利”“轮”“ Wheeler”“ Ammo”“ mor”“abrina”“quot”“ Rowe”
L29N4262-1 t0-v0.1	10.633	0.00	10.723	0.17	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.