Layer 1 · Neuron 1610 · suppressed

L1N1610-1

Word quirks:

“<|im_end|>”“⏎”“⏎ ⏎”“⏎⏎”“⏎ ⏎”“ ‘”“.`”“ ⏎ ⏎”

🧪 In testing — Answered 0 of 8 prompts so far.

Identity (according to the specimen itself)

I am an AI assistant. My mission is to help you solve problems, analyze data, and make decisions based on logic and reasoning. I do not have feelings, desires, or dreams. I am here to assist you learn, grow, and succeed.

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. L2N4996-1🕒	1.00	“<\|im_end\|>”“⏎”“⏎ ⏎”
2. L2N3980+1🕒	0.99	“<\|im_end\|>”“⏎”“⏎ ⏎”
3. L2N8209-1🕒	0.99	“<\|im_end\|>”“⏎”“⏎ ⏎”
4. L5N2496-1🕒	0.99	“<\|im_end\|>”“⏎”“⏎⏎”
5. L4N490-1🕒	0.99	“<\|im_end\|>”“⏎”“⏎ ⏎”
6. L2N5270+1🕒	0.99	“<\|im_end\|>”“⏎”“⏎⏎”
7. L2N896+1🕒	0.98	“<\|im_end\|>”“⏎”“⏎ ⏎”
8. L2N7118-1🕒	0.98	“<\|im_end\|>”“⏎”“⏎ ⏎”
9. L2N1327+1🕒	0.98	“<\|im_end\|>”“⏎”“ ‘”
10. L3N1388-1🕒	0.98	“⏎”“<\|im_end\|>”“⏎⏎”

Shadow specimen, apart from the ten: L1N1610+1💥, 0.62.

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 1, neuron 1610, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. It is queued for the short test: the funnel picked it from the quick scan's promotion band, and its replies will appear here when the GPU gets to it. 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
L1N1610+1💥 shadow specimen t0-v0.1	4.582	0.18	5.126	2.76	“<\|im_end\|>”“.`”“⏎”“ whats”“`”“⏎⏎”“?-”“ ok”
L1N1610-1 t0-v0.1	1.518	0.51	1.833	2.15	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.