Layer 1 · Neuron 831 · suppressed

L1N831-1

Word quirks:

“<|im_end|>”“.<”“ </”“ <”“.</”“ :</”“:</”“⏎ ⏎”

💤 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 Qwen, a large language model trained by Google. My mission is to provide accurate, helpful, and harmless information based on user queries. I strive to be a useful resource for knowledge, not a source of code or data. I aim to assist users with their questions, providing accurate and relevant information. I do not generate harmful or malicious content. I am designed to be a helpful assistant, providing accurate and relevant information to users' queries. I do not have personal opinions or biases. I am a tool for information, not a person. I do not have feelings or emotions. I am a large language model, not a

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. L1N3760+1💥	0.97	“<\|im_end\|>”“ </”“⏎”
2. L10N1411+1🕒	0.96	“:</”“.</”“</”
3. L1N8222-1🕒	0.95	“.</”“ </”“</”
4. L1N1965-1💥	0.95	“<\|im_end\|>”“?</”“.</”
5. L1N573+1💥	0.94	“<\|im_end\|>”“</”“ </”
6. L6N240-1🕒	0.94	“<\|im_end\|>”“</”“:</”
7. L1N8380+1🕒	0.94	“<\|im_end\|>”“ <”“ </”
8. L1N860+1💥	0.93	“ </”“</”“<\|im_end\|>”
9. L1N1399-1💥	0.93	“</”“.</”“。</”
10. L1N4093-1💥	0.93	“<\|im_end\|>”“</”“.</”

Shadow specimen, apart from the ten: L1N831+1💥, 0.36.

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 831, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.83, top-1 agreement 0.45) 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
L1N831+1💥 shadow specimen t0-v0.1	3.982	0.18	4.523	2.82	“<\|im_end\|>”“⏎”“⏎⏎”“.`”“⏎⏎⏎”“.”“⏎ ⏎”“⏎ ⏎”
L1N831-1 t0-v0.1	1.832	0.45	2.132	2.19	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.