Layer 6 · Neuron 4081 · suppressed

L6N4081-1

Word quirks:

“ ‘”“⏎”“ specific”“‘”“⏎⏎⏎”“ particular”“.””“⏎⏎”

💤 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 trained by Google. My mission is to provide accurate, helpful, and safe responses to your questions. How can I assist 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. The Hollow Lecturer✅ L5N5641+1	0.90	“⏎”“ ‘”“⏎⏎”
2. L16N72-1💤	0.88	“⏎⏎”“ ‘”“ specific”
3. L5N5213+1🕒	0.86	“⏎”“ ‘”“⏎⏎⏎”
4. L12N5494-1🕒	0.86	“ ‘”“.””“”.”
5. L4N2028-1🕒	0.86	“⏎”“ ‘”“<\|im_end\|>”
6. L6N4525-1💤	0.85	“⏎”“ ‘”“<\|im_end\|>”
7. L6N627-1💤	0.85	“ ‘”“⏎⏎”“⏎”
8. L6N957+1💤	0.85	“⏎”“ specific”“ ‘”
9. L3N9075+1🕒	0.85	“ ‘”“⏎”“<\|im_end\|>”
10. L5N2524-1💤	0.85	“⏎”“ ‘”“⏎⏎”

Shadow specimen, apart from the ten: L6N4081+1💥, -0.07.

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 6, neuron 4081, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.67, top-1 agreement 0.66) 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
L6N4081+1💥 shadow specimen t0-v0.1	2.934	0.34	3.401	2.82	“ billions”“ trillion”“ billion”“ millions”“ million”“ dozens”“ hundreds”“<\|im_end\|>”
L6N4081-1 t0-v0.1	0.672	0.66	0.811	1.12	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.