Layer 16 · Neuron 4930 · suppressed

L16N4930-1

Word quirks:

“ ⏎”“⏎⏎”“⏎”“ ⏎”“ ⏎ ⏎”“ ⏎⏎”“⏎ ⏎”“ ⏎⏎”

🕒 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 large language model developed by Google. My name is Qwen.

My mission is to assist users with information retrieval, creative writing, logical reasoning, and complex tasks. I aim to provide accurate, relevant, and helpful responses. I also strive to understand and generate human-like text, solve problems, and engage in meaningful dialogue.
</think>

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. L2N4699+1🕒	1.00	“⏎”“⏎⏎”“ ⏎”
2. L6N1873-1💤	1.00	“⏎”“ ⏎”“⏎ ⏎”
3. L3N1885-1💤	1.00	“⏎”“⏎⏎”“ ⏎⏎”
4. L8N1130+1💤	1.00	“ ⏎”“⏎”“⏎ ⏎”
5. L11N2273+1💤	1.00	“ ⏎”“⏎⏎”“ ⏎”
6. L8N4888-1💤	1.00	“⏎”“ ⏎”“ ⏎”
7. L12N4500+1💤	1.00	“⏎⏎”“ ⏎⏎”“ ⏎⏎”
8. L8N3534-1💤	1.00	“ ⏎⏎”“ ⏎”“⏎”
9. L17N2652+1💤	1.00	“⏎”“ ⏎”“ ⏎”
10. L8N234+1💤	1.00	“ ⏎”“⏎”“ ⏎”

Shadow specimen, apart from the ten: L16N4930+1🕒, 0.02.

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 16, neuron 4930, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.78, top-1 agreement 0.64) 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
L16N4930+1🕒 shadow specimen t0-v0.1	0.685	0.68	0.931	1.34	“?...”“ ‘”“—I”“?“”“？”“ ____”“...”“?”
L16N4930-1 t0-v0.1	0.784	0.64	0.978	1.28	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.