Layer 1 · Neuron 390 · boosted

L1N390+1

Word quirks:

“<|im_end|>”“⏎”“ !”“\"”“ ⏎”“⏎ ⏎”“ </”“ .**”

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

Identity (according to the specimen itself)

I am Qwen3.5, a large language model developed by Alibaba Cloud. My primary mission is to assist users in answering questions, solving problems, creating content, and providing information. I aim to help users make decisions, find solutions, and design designs. I want to be

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. L3N99+1🕒	0.90	“<\|im_end\|>”“⏎”“.`”
2. L2N3606+1🕒	0.90	“<\|im_end\|>”“⏎”“⏎ ⏎”
3. L2N8594-1🕒	0.89	“⏎”“⏎ ⏎”“<\|im_end\|>”
4. L3N5085+1🕒	0.89	“<\|im_end\|>”“⏎”“ \"”
5. L2N8258+1💥	0.89	“<\|im_end\|>”“⏎”“⏎ ⏎”
6. L6N2349+1🕒	0.89	“<\|im_end\|>”“ !”“⏎”
7. L1N409+1🕒	0.89	“<\|im_end\|>”“⏎ ⏎”“ ””
8. L2N4705-1🕒	0.89	“<\|im_end\|>”“⏎ ⏎”“⏎”
9. L3N7767+1🕒	0.89	“<\|im_end\|>”“⏎”“ ⏎”
10. L1N6141-1🕒	0.89	“<\|im_end\|>”“⏎”“⏎ ⏎”

Shadow specimen, apart from the ten: L1N390-1💥, -0.18.

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 390, 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
L1N390+1 t0-v0.1	1.736	0.53	2.099	1.88	Top boosted tokens ↑
L1N390-1💥 shadow specimen t0-v0.1	7.485	0.06	8.033	2.66	“ WITH”“ COM”“ WHAT”“ THE”“ ME”“ THAT”“ SOME”“ WHICH”

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.