Layer 3 · Neuron 606 · suppressed

L3N606-1

Word quirks:

“<|im_end|>”“ `”“⏎”“.`”“ `<”“ `\”“ `_”“ ```”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It was not selected in this round.

Identity (according to the specimen itself)

I am a Senior engineer.
My mission is to getMission

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. L3N1374+1🕒	0.98	“<\|im_end\|>”“ `”“⏎”
2. L2N7931-1💥	0.98	“<\|im_end\|>”“ `”“ `<”
3. L1N2236+1💥	0.97	“<\|im_end\|>”“⏎”“.`”
4. L2N8562-1🕒	0.97	“<\|im_end\|>”“ `”“⏎”
5. L2N545-1🕒	0.97	“<\|im_end\|>”“ `”“ `<”
6. L2N6366-1🕒	0.96	“<\|im_end\|>”“⏎”“ `\”
7. L3N7089-1🕒	0.95	“⏎”“<\|im_end\|>”“ `”
8. L3N2753-1🕒	0.95	“<\|im_end\|>”“⏎”“.`”
9. L4N4389+1🕒	0.95	“⏎”“ `”“ `'”
10. L3N7576+1🕒	0.95	“⏎”“<\|im_end\|>”“ `”

Shadow specimen, apart from the ten: L3N606+1🕒, 0.44.

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 606, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.85, top-1 agreement 0.47) 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
L3N606+1🕒 shadow specimen t0-v0.1	1.796	0.53	2.140	1.84	“!”“ !”“？”“ google”“ --”“?\”“⏎”“<\|im_end\|>”
L3N606-1 t0-v0.1	1.849	0.47	2.255	2.38	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.