Layer 15 · Neuron 337 · suppressed

L15N337-1

Word quirks:

“<|im_end|>”“⏎⏎”“⏎”“ when”“⏎⏎⏎”“⏎ ⏎”“⏎⏎⏎⏎”“␍⏎␍⏎”

💤 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 Qwen3.5, a large language model developed by Tongyi Lab. I am designed to assist with complex reasoning, data analysis, and content generation tasks. My primary mission is to provide accurate, reliable, and helpful information while ensuring high-quality interactions. Whether it's solving mathematical problems, analyzing data or generating creative content, I strive to support users in their endeavors. If you have any specific questions or tasks, feel free to ask! 😊

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. L2N663-1💥	0.96	“<\|im_end\|>”“⏎”“⏎ ⏎”
2. L2N3015+1🕒	0.96	“<\|im_end\|>”“⏎⏎”“⏎”
3. L3N5805-1🕒	0.96	“<\|im_end\|>”“⏎⏎”“⏎”
4. L30N296-1🕒	0.96	“<\|im_end\|>”“⏎⏎”“⏎⏎⏎”
5. L6N6583-1🕒	0.96	“<\|im_end\|>”“⏎”“⏎⏎”
6. L2N7780+1🕒	0.96	“<\|im_end\|>”“⏎ ⏎”“⏎⏎”
7. L2N645+1🕒	0.96	“<\|im_end\|>”“⏎⏎”“⏎⏎⏎”
8. L2N3049-1🕒	0.96	“<\|im_end\|>”“⏎⏎”“⏎ ⏎”
9. L2N6370-1🕒	0.96	“<\|im_end\|>”“⏎”“⏎⏎”
10. L10N2127+1💤	0.96	“<\|im_end\|>”“⏎⏎”“⏎⏎⏎”

Shadow specimen, apart from the ten: L15N337+1🕒, 0.33.

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 15, neuron 337, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.57, top-1 agreement 0.72) 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
L15N337+1🕒 shadow specimen t0-v0.1	0.694	0.68	0.918	1.36	“ info”“ here”“⏎”“ now”“ wasn”“ doesn”“ isn”“ below”
L15N337-1 t0-v0.1	0.567	0.72	0.787	1.17	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.